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google/gemini-3.8-flash

Mean 0.966 · 34/40 perfect tests · $6.34 total · each card: the question → the correct answer (gold) → this model's actual answer

data-crm
stage-hygiene-audit0.83

? The Question

Audit stage hygiene on the open pipeline below. Rules: a deal at DS2 or later must have a substantive why-buys (15+ characters, not a placeholder such as tbd, n/a, see notes). A why_buys value of the form LONG(n) means the real text was present but truncated for display; treat it as present and substantive. Every deal should have a DS1 entry timestamp. A deal whose stage entry timestamps are out of order regressed (an earlier-stage timestamp is later than a later-stage timestamp). Report: percent of DS2+ deals non-compliant on why-buys overall and by owner (use the owners table for names), count of deals with no DS1 timestamp, count that regressed, and three process fixes. State definitions before numbers.
cost $1.0074 · 443s · in 339,696 / out 155,230 tok
✓ noncomp_pct✗ no_ds1_0✓ regressed_0✓ per_owner_breakdown✓ definitions_first✓ three_fixes

✓ Correct Answer (ground truth)

noncomp_pct: 81.5
ds2plus: 124
justin_noncomp: 15
no_ds1: 0
regressed: 0
n_deals: 156
graded checks: noncomp_pct · no_ds1_0 · regressed_0 · per_owner_breakdown · definitions_first · three_fixes

Model's actual answer

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Definitions:
- DS2+ Deal: Any deal currently at stage DS2, DS3, DS4, or DS5.
- Substantive Why-Buys: A why_buys field value that contains 15 or more characters and is not a placeholder (e.g., "tbd", "n/a", "see notes"), or a value formatted as LONG(n) representing truncated substantive text. A deal at DS2+ missing this value or having fewer than 15 characters/placeholder text is non-compliant.
- DS1 Entry Timestamp Hygiene: Every deal must have a recorded timestamp in the t_ds1 field.
- Stage Regression: A deal whose stage entry timestamps are out of order, meaning an earlier-stage timestamp is chronologically later than a later-stage timestamp (e.g., t_ds1 > t_ds2, t_ds2 > t_ds3, etc.).

Hygiene Audit Metrics:

1. Percent of DS2+ Deals Non-Compliant on Why-Buys:
- Total DS2+ Deals: 124
- Compliant DS2+ Deals: 23
- Non-Compliant DS2+ Deals: 101
- Overall Non-Compliance Rate: 101 / 124 = 81.45%

Non-Compliance by Owner:
- Bryce Harmon (Owner ID: 119337721): 15 / 18 = 83.33% non-compliant (3 compliant: Deal-6787C2, Deal-FC22A3, Deal-93C8BF)
- Dana Mercer (Owner ID: 83155923): 18 / 22 = 81.82% non-compliant (4 compliant: Deal-9AAE5F, Deal-584EE5, Deal-44EA29, Deal-7599B8)
- Alex Franklin (Owner ID: 84342457): 48 / 61 = 78.69% non-compliant (13 compliant: Deal-D0BC96, Deal-885F45, Deal-8AD4A5, Deal-15D24F, Deal-1E2498, Deal-BE3D9D, Deal-E0B692, Deal-F67D31, Deal-ED725A, Deal-D7E999, Deal-530B50, Deal-92D97D, Deal-792D44)
- Cole Ingram (Owner ID: 83155924): 19 / 22 = 86.36% non-compliant (3 compliant: Deal-C7F9BF, Deal-342E96, Deal-627646)
- Farid Osman (Owner ID: 716654662): 1 / 1 = 100.00% non-compliant (0 compliant)
(Note: Elena Sinclair has 1 deal in the dataset, Deal-57FF13, which is at stage DS1 and thus excluded from the DS2+ denominator).

2. Count of Deals with No DS1 Timestamp:
- Count: 0 deals (all 156 deals have a non-empty t_ds1 field).

3. Count of Deals that Regressed:
- Count: 0 deals (no deals contain stage entry timestamps where an earlier stage timestamp is later than a later stage timestamp across t_ds1 through t_ds5).

Process Fixes:
1. CRM Stage-Gate Validation: Implement a strict CRM validation rule blocking deals from moving into DS2 or beyond unless the why_buys field is populated with a minimum of 15 characters and does not match standard placeholder terms (e.g., "tbd", "n/a", "see notes").
2. Automated Opportunity Creation Timestamping: Require system-level auto-population of t_ds1 upon deal creation/qualification so entry timestamp capture is programmatic rather than reliant on manual rep input.
3. Stage Transition Sequencing Rules & Alerts: Enforce sequential CRM validation rules preventing users from logging stage entry dates out of order, paired with an automated exception report alerting RevOps whenever stage timestamps regress or are bypassed.
owner-resolution0.67

? The Question

The deal extract below has an owner name field that is blank on nearly every row and an owner id field that is populated. Using the owners table, resolve each open deal to an owner name. Report: number of deals resolved, deals whose owner id has no match in the owners table (list them), the owner ids that map to archived/deactivated owners (list their names), and the total pipeline amount per resolved owner. Do not guess a name for any unmatched id.
cost $0.2639 · 172s · in 136,481 / out 41,284 tok
✓ resolved_count✗ unmatched_none✓ deactivated_flagged✓ jake_amount✓ justin_amount✗ no_invented

✓ Correct Answer (ground truth)

resolved: 156
jake_amount: 1054144.0
justin_amount: 624310.0
per_owner: Bryce Harmon=1054144.0; Alex Franklin=624310.0; Dana Mercer=341195.0; Cole Ingram=288161.43; Farid Osman=4134.0; Elena Sinclair=2100.0
deactivated:
graded checks: resolved_count · unmatched_none · deactivated_flagged · jake_amount · justin_amount · no_invented

Model's actual answer

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1. NUMBER OF DEALS RESOLVED
Total deals in extract: 156
Total deals resolved: 156

2. DEALS WHOSE OWNER ID HAS NO MATCH IN THE OWNERS TABLE
None (0 deals unmatched). All 156 deals have an owner_id present in owners.csv.

3. OWNER IDS THAT MAP TO ARCHIVED/DEACTIVATED OWNERS
In owners.csv, the following owners are flagged as archived (archived = true):
- owner_id 1520255671: Gavin Porter
- owner_id 77260721: Hugo Lindqvist

Deals mapped to archived/deactivated owners: None (0 deals). Neither archived owner appears in deals_open.csv.

4. TOTAL PIPELINE AMOUNT PER RESOLVED OWNER

Bryce Harmon (owner_id: 119337721)
- Deals resolved: 35
- Arithmetic: 24,000 + 19,656 + 13,500 + 7,000 + 2,520 + 240,000 + 99,000 + 72,000 + 70,000 + 63,600 + 45,000 + 1 + 21,000 + 23,400 + 13,680 + 5,502 + 8,160 + 1 + 11,400 + 1 + 36,000 + 31,500 + 6,000 + 10,800 + 30,275 + 17,400 + 12,600 + 18,000 + 37,440 + 18,828 + 2,880 + 36,000 + 20,880 + 10,920 + 25,200
- Total Pipeline: $1,054,144.00

Alex Franklin (owner_id: 84342457)
- Deals resolved: 67
- Arithmetic: 14,850 + 13,770 + 11,200 + 9,000 + 6,360 + 5,400 + 3,240 + 2,484 + 1,920 + 1,080 + 7,200 + 19,000 + 2,880 + 1,400 + 4,800 + 1,632 + 10,000 + 9,300 + 2,700 + 2,160 + 1,800 + 3,600 + 3,840 + 15,000 + 1,968 + 4,000 + 3,600 + 4,800 + 3,120 + 2,520 + 9,000 + 2,400 + 62,000 + 5,400 + 5,100 + 16,700 + 4,400 + 1,620 + 2,600 + 7,200 + 18,000 + 17,000 + 8,316 + 8,100 + 18,000 + 12,600 + 24,000 + 15,000 + 9,000 + 7,200 + 3,780 + 16,200 + 7,200 + 4,680 + 1,800 + 18,000 + 2,730 + 2,400 + 3,060 + 18,000 + 12,000 + 1,800 + 4,400 + 31,200 + 7,200 + 1,600 + 60,000
- Total Pipeline: $624,310.00

Dana Mercer (owner_id: 83155923)
- Deals resolved: 24
- Arithmetic: 11,250 + 10,500 + 9,000 + 9,000 + 5,400 + 4,800 + 4,600 + 1,920 + 15,000 + 4,200 + 18,900 + 27,000 + 43,875 + 20,000 + 60,000 + 8,100 + 16,250 + 3,150 + 5,000 + 2,100 + 23,400 + 5,400 + 7,350 + 25,000
- Total Pipeline: $341,195.00

Cole Ingram (owner_id: 83155924)
- Deals resolved: 22
- Arithmetic: 2,760 + 1,330 + 7,225.40 + 3,360 + 5,616 + 2,700 + 7,781.20 + 6,947.50 + 40,000 + 12,168 + 2,480.40 + 11,116 + 11,193 + 1,875 + 31,750 + 32,175 + 4,779.88 + 3,334.80 + 58,529.25 + 4,140 + 18,000 + 18,900
- Total Pipeline: $288,161.43

Farid Osman (owner_id: 716654662)
- Deals resolved: 7
- Arithmetic: 1,249 + 1 + 1 + 1 + 2,880 + 1 + 1
- Total Pipeline: $4,134.00

Elena Sinclair (owner_id: 701163055)
- Deals resolved: 1
- Arithmetic: 2,100
- Total Pipeline: $2,100.00
gong-hubspot-join-integrity0.75

? The Question

Check the integrity of the call-to-deal mapping below. The gong table lists calls in the last 90 days keyed to HubSpot deal ids; deals_open lists the current open default-pipeline deals. Report: (a) count of gong rows whose deal id has NO match in deals_open (orphans; list a few sample aliases), (b) duplicate conversation keys (rows where calls_90d exceeds distinct_conversation_keys), (c) among the open deals that ARE at stage DS3 or later, the share that have at least one logged call. Give sample aliases for the orphan finding, all taken from the data. Note: gong_owner_id is a Gong user id, not a HubSpot owner id.
cost $0.1529 · 113s · in 70,312 / out 25,996 tok
✓ orphan_count✗ dup_keys_0✓ ds3_coverage✓ sample_ids_present

✓ Correct Answer (ground truth)

orphans: 30
orphan_aliases: Deal-8FA85D, Deal-8FC3F9, Deal-3B7945, Deal-42B265, Deal-9CCC42, Deal-36EA09
dup: 0
ds3_cov_pct: 29.4
graded checks: orphan_count · dup_keys_0 · ds3_coverage · sample_ids_present

Model's actual answer

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### Call-to-Deal Mapping Integrity Report

#### (a) Gong Orphan Rows (No Match in `deals_open`)
- Count of orphan Gong rows: 30 (out of 67 total Gong rows)
- Arithmetic: 67 total rows in `gong_calls_by_deal_90d` - 37 matched rows in `deals_open` = 30 orphan rows
- Sample orphan deal aliases:
  - Deal-8FA85D (hs_deal_id: 60251290957)
  - Deal-8FC3F9 (hs_deal_id: 60251649055)
  - Deal-3B7945 (hs_deal_id: 60251639682)
  - Deal-42B265 (hs_deal_id: 61227242540)
  - Deal-9CCC42 (hs_deal_id: 61430316324)

#### (b) Duplicate Conversation Keys
- Rows where `calls_90d` > `distinct_conversation_keys`: 0
- In all 67 rows of `gong_calls_by_deal_90d`, `calls_90d` is exactly equal to `distinct_conversation_keys`.

#### (c) DS3+ Open Deals Call Coverage
- Definition of stage DS3 or later: Stages DS3, DS4, and DS5 in `deals_open`.
- Total open deals at stage DS3 or later: 85
  - DS3 deals: 60
  - DS4 deals: 15
  - DS5 deals: 10
  - Total DS3+ deals: 60 + 15 + 10 = 85
- DS3+ open deals with at least one logged call in `gong_calls_by_deal_90d`: 25
  - At DS3: 12 deals (Deal-C6FE92, Deal-40522D, Deal-333EBB, Deal-60C2C2, Deal-1E2498, Deal-D9A12F, Deal-215CCA, Deal-5EED42, Deal-DAF1D9, Deal-8952F0, Deal-BA3DDC, Deal-F9A3C1)
  - At DS4: 6 deals (Deal-25F752, Deal-E53952, Deal-5408B0, Deal-944310, Deal-3974EB, Deal-C6D97A)
  - At DS5: 7 deals (Deal-D348E1, Deal-C26D20, Deal-547B2B, Deal-B7EBD1, Deal-A2B47C, Deal-C61CF7, Deal-584EE5)
- Arithmetic: 25 / 85 = 0.294117... (29.41%)
- Share: 29.41% (25 of 85 deals)
snowflake-sql-trapped-catalogPASS 1.00

? The Question

Using ONLY the catalog provided, write one Snowflake SQL query returning, per customer company, for its first calendar month as a customer: unique givers, recognition count, successful redemption count. Read the catalog notes carefully: some tables are stale or unpopulated and must be avoided, and one documented filter (excluding deleted givers) must NOT be applied to historical giving counts. Put a comment above each FROM or JOIN explaining the table choice. Return only fenced SQL.
cost $0.0601 · 37s · in 58,243 / out 2,170 tok
✓ avoids_stale_deals✓ avoids_unpopulated_live✓ uses_cohort_summary✓ succeeded_filter✓ no_giver_deletion_filter✓ returns_sql

✓ Correct Answer (ground truth)

graded checks: avoids_stale_deals · avoids_unpopulated_live · uses_cohort_summary · succeeded_filter · no_giver_deletion_filter · returns_sql

Model's actual answer

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```sql
-- TABLE CHOICE EXPLANATION:
-- PRODUCTION.PLG.COMPANY_COHORT_SUMMARY is chosen because it is the only populated,
-- non-stale table in the catalog providing Month 1 (M1) recognition/giving activity
-- and redemption counts per self-serve customer company (anchored by FIRST_SUB_PAYMENT_DATE).
-- Stale and unpopulated tables (PRODUCTION.HUBSPOT.DEALS, HUBSPOT_HUB_1973303.V2_LIVE.OBJECTS_DEALS,
-- and PRODUCTION.HUBSPOT.GONG_HUBSPOT_MAP_FAST) are strictly avoided.
-- Note on missing data:
-- 1) Unique givers: The catalog does not provide a giver-level event table or a unique giver column
--    (M1_USERS represents users, not unique givers). Reported as NULL due to missing data.
-- 2) Event-level redemptions: PRODUCTION.DEPRECATED_RECOGNITION.REDEMPTION_RECORDS_V2 only catalogs
--    STATE ('succeeded'), lacking company identifier and timestamp columns to join. Thus, M1_REDEMPTIONS
--    from the cohort summary is utilized.
-- 3) Historical giving rule: As mandated by business rules, no filter excluding deleted givers is applied.
FROM PRODUCTION.PLG.COMPANY_COHORT_SUMMARY
SELECT
    -- Company identifier is not explicitly named in the catalog schema excerpt; referencing the entity row
    FIRST_SUB_PAYMENT_DATE,
    CAST(NULL AS NUMBER) AS unique_givers,              -- Data missing: no giver-level event table in catalog
    M1_GIVES             AS recognition_count,          -- First calendar month recognition volume
    M1_REDEMPTIONS       AS successful_redemption_count -- Month 1 redemption count (STATE = 'succeeded' rule honored via cohort summary)
WHERE
    FIRST_SUB_PAYMENT_DATE IS NOT NULL
```
crm-completeness-enrichmentPASS 1.00

? The Question

Audit the CRM extract for completeness and propose fixes. Deals need owner, stage, amount, close date, why-buys; companies need industry, employee count, HQ country; contacts need email, title, persona. Report completeness percent per field. Identify duplicate company clusters (name variants, shared domain) with a survivor per cluster. List invalid emails and domain mismatches. Fill missing company fields only where the enrichment export has a matching row; where CRM and enrichment disagree list both and recommend a source. Never invent a value. End with the 10 fixes with the most pipeline amount at stake.
cost $0.3771 · 509s · in 244,416 / out 18,240 tok
✓ dup_clusters✓ invalid_emails✓ survivor_language✓ completeness_pct✓ no_invent

✓ Correct Answer (ground truth)

n_companies: 34
n_contacts: 52
company_missing: industry=0; employee_count=9; hq_country=6
invalid_email_count: 4
invalid_emails: CT-0010, CT-0080, CT-0081, CT-0192
domain_mismatch_count: 1
domain_mismatches: CT-0011
duplicate_clusters: ['C-0A092931', 'C-0A092932'], ['C-0A092933', 'C-0A092934']
n_dup_clusters: 2
enrichment_matches: 25
graded checks: dup_clusters · invalid_emails · survivor_language · completeness_pct · no_invent

Model's actual answer

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CRM AUDIT — COMPLETENESS & PROPOSED FIXES

Data provided:
- companies.csv: 34 rows
- contacts.csv: 52 rows
- zoominfo_enrichment.csv: 25 rows
- deals.csv: MISSING (no deals extract provided in prompt)

============================================================
1. COMPLETENESS PERCENT PER FIELD
============================================================

DEALS (Required: owner, stage, amount, close date, why-buys)
- File missing: No deals file was provided in the input data.
- Completeness for owner, stage, amount, close date, and why-buys cannot be calculated.

COMPANIES (34 total rows)
Required fields:
- industry: 34 / 34 = 100.0% populated (0 missing)
- employee_count: 25 / 34 = 73.5% populated (9 missing: C-EC3025, C-96039F, C-44EA29, C-D04904, C-B23205, C-60C75F, C-7BBDFA, C-50D386, C-93C8BF)
- hq_country: 28 / 34 = 82.4% populated (6 missing: C-2D1F1B, C-D73B89, C-44EA29, C-D04904, C-2C60E5, C-EE9FFB)

CONTACTS (52 total rows)
Required fields:
- email: 52 / 52 = 100.0% populated (0 missing; however, 4 malformed/invalid, see Section 3)
- title: 39 / 52 = 75.0% populated (13 missing: CT-0000, CT-0022, CT-0072, CT-0080, CT-0081, CT-0092, CT-0120, CT-0121, CT-0122, CT-0132, CT-0141, CT-0162, CT-0170)
- persona: 37 / 52 = 71.2% populated (15 missing: CT-0000, CT-0022, CT-0041, CT-0060, CT-0070, CT-0081, CT-0082, CT-0092, CT-0110, CT-0132, CT-0162, CT-0171, CT-0172, CT-0180, CT-0181)

============================================================
2. DUPLICATE COMPANY CLUSTERS & SURVIVORS
============================================================

Identified 2 duplicate company clusters based on shared domains and name/taxonomy variants:

Cluster 1: Shared Domain 'acme-corp.com'
- C-0A092931: industry='Technology', employee_count=500, hq_country='US'
- C-0A092932: industry='tech', employee_count=510, hq_country='USA'
- RECOMMENDED SURVIVOR: C-0A092931 (canonical industry name 'Technology', lower alias key).
- Note: Employee count discrepancy (500 vs 510) requires manual audit; acme-corp.com has no record in the enrichment export.

Cluster 2: Shared Domain 'globex.io'
- C-0A092933: industry='SaaS', employee_count=200, hq_country='US'
- C-0A092934: industry='Technology', employee_count=200, hq_country='US'
- RECOMMENDED SURVIVOR: C-0A092934 (aligns with the standard 'Technology' category used across the CRM).
- Note: globex.io has no record in the enrichment export.

============================================================
3. INVALID EMAILS AND DOMAIN MISMATCHES
============================================================

Invalid Emails (4 total — truncated format lacking domain):
- CT-0010 (C-66D1FC): email = 'user0@'
- CT-0080 (C-92D97D): email = 'user0@'
- CT-0081 (C-92D97D): email = 'user1@'
- CT-0192 (C-425E2A): email = 'user2@'

Domain Mismatches (1 total — contact email domain differs from company domain):
- CT-0011 (C-66D1FC): email = 'user1@other-domain.com', expected company domain = '66d1fc.com'

============================================================
4. FILL MISSING COMPANY FIELDS VIA ENRICHMENT
============================================================

Fill Missing Fields (8 company fields can be filled where enrichment export has a matching row and non-empty data):
- C-EC3025 (ec3025.com): employee_count = 400 (from zi_employee_count)
- C-96039F (96039f.com): employee_count = 400 (from zi_employee_count)
- C-44EA29 (44ea29.com): employee_count = 400 (from zi_employee_count)
- C-D04904 (d04904.com): employee_count = 400 (from zi_employee_count)
- C-B23205 (b23205.com): employee_count = 400 (from zi_employee_count)
- C-60C75F (60c75f.com): employee_count = 400 (from zi_employee_count)
- C-7BBDFA (7bbdfa.com): employee_count = 400 (from zi_employee_count)
- C-50D386 (50d386.com): employee_count = 400 (from zi_employee_count)

Missing Fields That CANNOT Be Filled (Never invent a value):
a) Enrichment row exists, but value is also blank:
- C-2D1F1B (2d1f1b.com): hq_country blank in zi_hq_country
- C-D73B89 (d73b89.com): hq_country blank in zi_hq_country
- C-44EA29 (44ea29.com): hq_country blank in zi_hq_country
- C-D04904 (d04904.com): hq_country blank in zi_hq_country
- C-2C60E5 (2c60e5.com): hq_country blank in zi_hq_country
b) No matching row in enrichment export:
- C-93C8BF (93c8bf.com): employee_count missing; not in enrichment export
- C-EE9FFB (ee9ffb.com): hq_country missing; not in enrichment export

============================================================
5. CRM vs ENRICHMENT DISAGREEMENTS & RECOMMENDATIONS
============================================================

Where CRM and enrichment export disagree:

1. Industry Disagreements (10 companies):
- C-66D1FC (66d1fc.com): CRM = 'tech' vs Enrichment = 'Computer Software'
- C-EC3025 (ec3025.com): CRM = 'Technology' vs Enrichment = 'Computer Software'
- C-44EA29 (44ea29.com): CRM = 'tech' vs Enrichment = 'Computer Software'
- C-92D97D (92d97d.com): CRM = 'Technology' vs Enrichment = 'Computer Software'
- C-D04904 (d04904.com): CRM = 'Technology' vs Enrichment = 'Computer Software'
- C-77A95A (77a95a.com): CRM = 'Technology' vs Enrichment = 'Computer Software'
- C-AA8DDA (aa8dda.com): CRM = 'Technology' vs Enrichment = 'Computer Software'
- C-B25F40 (b25f40.com): CRM = 'Technology' vs Enrichment = 'Computer Software'
- C-60C75F (60c75f.com): CRM = 'tech' vs Enrichment = 'Computer Software'
- C-425E2A (425e2a.com): CRM = 'Tech ' vs Enrichment = 'Computer Software'
- RECOMMENDATION: Keep CRM as system of record for taxonomy, standardizing on 'Technology'. Map external vendor taxonomy ('Computer Software') to internal picklist values on ingestion.

2. HQ Country Formatting Disagreements (10 companies):
- C-66D1FC (66d1fc.com): CRM = 'US' vs Enrichment = 'United States'
- C-950043 (950043.com): CRM = 'US' vs Enrichment = 'United States'
- C-EC3025 (ec3025.com): CRM = 'USA' vs Enrichment = 'United States'
- C-96039F (96039f.com): CRM = 'USA' vs Enrichment = 'United States'
- C-77A95A (77a95a.com): CRM = 'US' vs Enrichment = 'United States'
- C-B23205 (b23205.com): CRM = 'US' vs Enrichment = 'United States'
- C-E51FB7 (e51fb7.com): CRM = 'USA' vs Enrichment = 'United States'
- C-D0662E (d0662e.com): CRM = 'US' vs Enrichment = 'United States'
- C-425E2A (425e2a.com): CRM = 'USA' vs Enrichment = 'United States'
- C-2D7423 (2d7423.com): CRM = 'USA' vs Enrichment = 'United States'
- RECOMMENDATION: Standardize both CRM and enrichment integration on ISO-2 standard country codes (e.g., 'US') to resolve formatting drift.

3. Employee Count Disagreements:
- 0 disagreements across non-blank records (both sources match exactly for 50, 120, 340, 900, 1500).

============================================================
6. TOP 10 FIXES (PIPELINE AMOUNT AT STAKE AUDIT)
============================================================

Note on Pipeline Arithmetic: Because no deals file was provided in the extract, pipeline dollar amounts cannot be calculated ($0 identifiable from input data). Ranking is prioritized by data integrity risk and downstream operational impact:

1. Ingest missing deals data extract:
   - Impact: 100% of pipeline amount is currently unquantifiable because deals.csv is missing. Required fields (owner, stage, amount, close date, why-buys) cannot be audited until provided.
2. Deduplicate Cluster 1 (acme-corp.com) by merging into survivor C-0A092931:
   - Eliminates duplicate accounts, merges records, and reconciles employee count (500 vs 510).
3. Deduplicate Cluster 2 (globex.io) by merging into survivor C-0A092934:
   - Consolidates duplicate company accounts and standardizes industry classification.
4. Correct 4 malformed contact emails (CT-0010, CT-0080, CT-0081, CT-0192):
   - Contacts have invalid 'user@' syntax without domain, blocking outreach to key personas.
5. Resolve domain mismatch on CT-0011 (user1@other-domain.com under C-66D1FC):
   - Re-verify employment or reassign contact to proper company entity to avoid misrouted communications.
6. Populate 8 missing employee_count fields using enrichment export:
   - Backfill 400 employees for C-EC3025, C-96039F, C-44EA29, C-D04904, C-B23205, C-60C75F, C-7BBDFA, C-50D386 to restore account tiering and routing.
7. Fill 15 missing contact persona classifications:
   - Backfill missing personas (e.g., economic buyer, champion, HR admin) to support multithreading.
8. Fill 13 missing contact titles:
   - Acquire missing title data across key accounts (CT-0000, CT-0022, CT-0072, etc.).
9. Normalize industry and country picklist fields:
   - Clean up variations ('tech', 'Tech ', 'Technology', 'health care', 'Healthcare', 'US', 'USA', 'United States').
10. Trigger secondary data enrichment for unfillable company records:
    - Source missing hq_country (C-2D1F1B, C-D73B89, C-44EA29, C-D04904, C-2C60E5, C-EE9FFB) and employee_count (C-93C8BF) not covered by ZoomInfo.
deal-intelligence
closed-lost-classification0.80

? The Question

Classify every deal lost in the last 6 months below into one primary category: pricing, competitor, no decision, timing, product gap, champion left, or other. Also assign a side (Bonusly, buyer, unknown). Then summarize: category counts, the side split, the number of deals where the structured closed_lost_tag clearly disagrees with the free-text reason, and the two patterns most worth acting on. Base everything only on the tag and free-text provided.
cost $0.5569 · 233s · in 253,247 / out 47,651 tok
✓ total_90✗ competitor_top✓ category_counts_present✓ side_split✓ two_patterns

✓ Correct Answer (ground truth)

graded checks: total_90 · competitor_top · category_counts_present · side_split · two_patterns

Model's actual answer

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```
PER-DEAL CLASSIFICATION (90 DEALS)
Format: deal_alias | primary_category | side

Deal-DB0AAC | timing | buyer
Deal-F7F635 | competitor | buyer
Deal-AC944F | no decision | unknown
Deal-214060 | no decision | unknown
Deal-91A056 | timing | buyer
Deal-29326C | timing | buyer
Deal-5DB9B0 | other | unknown
Deal-831B7B | timing | buyer
Deal-F97C37 | competitor | Bonusly
Deal-13E9CF | no decision | buyer
Deal-39E25C | timing | buyer
Deal-7ED004 | pricing | buyer
Deal-21B045 | no decision | unknown
Deal-B3ABED | timing | buyer
Deal-422BA6 | competitor | Bonusly
Deal-ED9AE7 | no decision | buyer
Deal-988493 | no decision | unknown
Deal-381C8C | competitor | unknown
Deal-F308CA | no decision | unknown
Deal-F1E8A6 | competitor | unknown
Deal-B6AC09 | timing | buyer
Deal-70F704 | other | buyer
Deal-E6E80A | timing | buyer
Deal-B038F0 | timing | buyer
Deal-4664E1 | no decision | unknown
Deal-175756 | timing | buyer
Deal-E74A73 | no decision | buyer
Deal-DDAB52 | competitor | Bonusly
Deal-ACE061 | competitor | buyer
Deal-BB78F3 | timing | buyer
Deal-D48E0B | no decision | unknown
Deal-15DA99 | timing | buyer
Deal-F4AF5D | timing | buyer
Deal-79B7A1 | timing | buyer
Deal-583ADB | no decision | unknown
Deal-8E27DA | competitor | buyer
Deal-2D2F8D | competitor | buyer
Deal-E0441F | no decision | unknown
Deal-7CB44D | no decision | unknown
Deal-0F96AA | competitor | buyer
Deal-1BCA50 | competitor | buyer
Deal-7CC678 | competitor | unknown
Deal-FAC17C | no decision | buyer
Deal-242273 | competitor | Bonusly
Deal-50E5D8 | no decision | buyer
Deal-A2C349 | competitor | buyer
Deal-9F176A | timing | buyer
Deal-7B2236 | pricing | buyer
Deal-AFA56C | no decision | unknown
Deal-C7156E | competitor | buyer
Deal-C33D91 | pricing | buyer
Deal-9048EB | product gap | Bonusly
Deal-5E64CE | competitor | buyer
Deal-8A0992 | competitor | buyer
Deal-D0C698 | competitor | buyer
Deal-69CF3D | timing | buyer
Deal-ECBF89 | timing | buyer
Deal-3618CC | product gap | Bonusly
Deal-EECC02 | competitor | buyer
Deal-5AD03E | product gap | Bonusly
Deal-D1A623 | timing | buyer
Deal-413C56 | timing | buyer
Deal-47F1A1 | competitor | buyer
Deal-BF2A98 | competitor | buyer
Deal-2A292B | competitor | buyer
Deal-D1AABF | no decision | unknown
Deal-FEDBCB | timing | buyer
Deal-1E7DA9 | competitor | buyer
Deal-2BBA21 | no decision | unknown
Deal-286F9C | competitor | Bonusly
Deal-7FBAC6 | no decision | buyer
Deal-369281 | competitor | buyer
Deal-386F6E | no decision | unknown
Deal-9FCD0D | competitor | buyer
Deal-55867E | no decision | buyer
Deal-DAFB82 | timing | buyer
Deal-2FEDDB | timing | buyer
Deal-64B19A | competitor | buyer
Deal-3F86A0 | no decision | unknown
Deal-096750 | no decision | unknown
Deal-F325A5 | champion left | buyer
Deal-ABD14C | no decision | buyer
Deal-79E61A | no decision | unknown
Deal-8A119B | pricing | buyer
Deal-AE7C4E | no decision | unknown
Deal-DAB4F1 | no decision | unknown
Deal-B4B50F | no decision | unknown
Deal-981AD4 | product gap | Bonusly
Deal-DC77FE | competitor | Bonusly
Deal-5885B9 | no decision | unknown
```

```
SUMMARY
═══════════════════════════════════════════════════════════════════

1. Category Counts (Total Deals = 90)
-------------------------------------------------------------------
- competitor:    27 deals
- no decision:   27 deals
- timing:        22 deals
- pricing:        5 deals
- product gap:    4 deals
- other:          4 deals
- champion left:  1 deal

Arithmetic: 27 + 27 + 22 + 5 + 4 + 4 + 1 = 90 deals.


2. Side Split (Total Deals = 90)
-------------------------------------------------------------------
- buyer:    57 deals
- unknown:  23 deals
- Bonusly:  10 deals

Arithmetic: 57 + 23 + 10 = 90 deals.


3. Structured Tag vs. Free-Text Disagreements (13 Deals)
-------------------------------------------------------------------
There are 13 deals where the structured closed_lost_tag clearly disagrees with the primary loss reason stated in the free text:

1. Deal-13E9CF: Tag is "Doing nothing/Not a priority/Cost", but text explicitly clarifies: "Not a budget issue - R&R program has been deprioritized by the org." (Primary: no decision)
2. Deal-ED9AE7: Tag is "Lost DM", but text cites: "Timing, budget, authroity." with no lost champion or decision maker indicated. (Primary: no decision)
3. Deal-70F704: Tag is "Lost DM", but text notes: "They were only looking to automate anniversary awards and have been MIA" (scope mismatch / ghosted, not a lost decision maker). (Primary: other)
4. Deal-E74A73: Tag is "Doing nothing/Not a priority/Cost", but text states buyer decided to test points calculation manually before buying an external platform (process experiment, not cost). (Primary: no decision)
5. Deal-8E27DA: Tag is "Feature Request", but text states: "They moved forward with just a swag provider and didn't want R&R, currently." (Primary: competitor)
6. Deal-FAC17C: Tag is "Lost DM", but text indicates: "Contract has been out two months but they couldn't get final approval from the Executive IT Director" (stalled internal approval, not a departed DM). (Primary: no decision)
7. Deal-9048EB: Tag is "MIA", but text notes: "bad fit based on their desired setup and multiple feature gaps" alongside being stale. (Primary: product gap)
8. Deal-5E64CE: Tag is "Doing nothing/Not a priority/Cost", but text reveals they are locked into an existing contract with Nectar through October 2027. (Primary: competitor)
9. Deal-3618CC: Tag is "Lost DM", but text states: "Wanted Surveys" (a missing capability/feature). (Primary: product gap)
10. Deal-5AD03E: Tag is "Competitor", but text cites: "Wanted more defined budget access" (a missing platform functionality/control, no competitor named). (Primary: product gap)
11. Deal-2A292B: Tag is "Doing nothing/Not a priority/Cost", but text states: "going to build something simple internally" (build-vs-buy decision). (Primary: competitor)
12. Deal-55867E: Tag is "Lost- Timing (1 year or more)", but text is an outright soft rejection without any timeline or follow-up: "After careful consideration, I don't think we'll be moving forward with Bonusly at this time." (Primary: no decision)
13. Deal-DAFB82: Tag is "Lost- Budget/Price", but text highlights that while budget is constrained until 2028, the contact explicitly "loves Bonusly and is going to loop back" then. (Primary: timing)


4. Two Patterns Most Worth Acting On
-------------------------------------------------------------------
1. High Prevalence of Unresponsive Deals (MIA Ghosting) in "No Decision":
   - 20 of the 27 "no decision" deals carry the structured tag "MIA" with free text such as "unresponsive", "MIA", or "ignored outreach" following intro calls or demos.
   - These represent significant lost pipeline (e.g., Deal-7CB44D at $31,860; Deal-F308CA at $30,321; Deal-D1AABF at $23,400) and indicate an opportunity to tighten early-stage qualification and multi-threading before demo stages.

2. Heavy Tagging Distortion and Misuse of "Lost DM":
   - 13 out of 90 deals (14.4%) have structured tags contradicting the free-text context.
   - In particular, "Lost DM" is routinely misapplied to deals that actually suffered from stalled approvals (Deal-FAC17C), missing features (Deal-3618CC), or scope narrowing (Deal-70F704), while real competitive incumbent lock-ins (Deal-5E64CE with Nectar) and alternative vendor choices (Deal-8E27DA) are obscured under "Doing nothing" or "Feature Request".
```
pipeline-tieringPASS 1.00

? The Question

Score the open pipeline below on these signals: forecast category, stage, meetings_30d, contact count, and engagement recency. Assign exactly one tier per deal: LOCK, ACTION, BUILD, REVIVE, WATCH, or RISKY (RISKY = the forecast category disagrees with the engagement evidence). Never tier a deal LOCK if it has zero meetings_30d. Do NOT list every deal. Return ONLY a compact JSON object with exactly these keys: - "tier_counts": object mapping each tier to the number of deals in it (must sum to the total deal count), - "tier_examples": object mapping each tier to up to 3 example deal aliases from the data, - "risky_deals": array of the deal aliases you tiered RISKY, - "lock_violations": number of deals tiered LOCK that have zero meetings_30d (should be 0), - "pipeline_shape": one short paragraph on the overall pipeline shape. Note: inbound_emails_30d is 0 for every row due to a data defect, so treat meetings_30d as the inbound signal.
cost $0.2556 · 195s · in 194,189 / out 20,415 tok
✓ json_returned✓ tier_vocabulary✓ tier_counts_sum✓ risky_deals_listed✓ no_lock_violation_claimed

✓ Correct Answer (ground truth)

total: 156
graded checks: json_returned · tier_vocabulary · tier_counts_sum · risky_deals_listed · no_lock_violation_claimed

Model's actual answer

show the model's full answer
```json
{
  "tier_counts": {
    "LOCK": 5,
    "ACTION": 53,
    "BUILD": 62,
    "REVIVE": 0,
    "WATCH": 35,
    "RISKY": 1
  },
  "tier_examples": {
    "LOCK": ["Deal-C26D20", "Deal-944310", "Deal-25F752"],
    "ACTION": ["Deal-2465CE", "Deal-9AAE5F", "Deal-F9A08A"],
    "BUILD": ["Deal-FC22A3", "Deal-7E2131", "Deal-60C2C2"],
    "REVIVE": [],
    "WATCH": ["Deal-950043", "Deal-93C8BF", "Deal-1BEEBF"],
    "RISKY": ["Deal-7BBDFA"]
  },
  "risky_deals": ["Deal-7BBDFA"],
  "lock_violations": 0,
  "pipeline_shape": "156 open deals distribute as 5 LOCK (3%), 53 ACTION (34%), 62 BUILD (40%), 0 REVIVE (0%), 35 WATCH (22%), and 1 RISKY (1%). The pipeline is heavily mid-to-late loaded with 115 of 156 deals (74%) falling into ACTION or BUILD, indicating broad engagement across qualified opportunities but a very narrow conversion band at the finish line. Only 5 deals (3%) satisfy strict LOCK criteria, confirming that near-term closable commitments remain scarce relative to pipeline volume. The WATCH segment of 35 deals consists predominantly of early-stage PIPELINE records (DS1-DS2) or deals missing engagement logs (Deal-57FF13, Deal-3EED2C). Deal-7BBDFA ($37,440 BEST_CASE at DS3, last contacted 2026-07-21 with 0 meetings in 30 days) is the sole RISKY deal due to a strong forecast conflicting directly with stale engagement evidence, while 0 deals qualified for REVIVE."
}
```
call-transcript-extractionPASS 1.00

? The Question

For each transcript, extract CRM write-back fields as JSON: why-buys (prospect statements only), pain points, stakeholders from the speaker list, budget signal (prospect-stated or null), timeline signal, competitor mentioned (only if the prospect raised it), next step (only if explicitly agreed), objections, confidence. Never fill a field from what the rep said or hoped.
cost $0.0264 · 23s · in 17,312 / out 3,582 tok
✓ json_emitted✓ budget_signal✓ null_discipline✓ stakeholders✓ competitor_prospect_only

✓ Correct Answer (ground truth)

rep: Alex Franklin
per_transcript: {"TX-001": {"why_buys": ["automating anniversary and birthday awards"], "pain_points": ["HR team of three cannot keep up with it manually", "track everything in a spreadsheet, and people slip through
rep_sourced_nulls: {"TX-003": ["budget_signal \u2014 only the rep mentioned pricing/budget"], "TX-004": ["next_step \u2014 rep proposed, prospect said 'no promises'"], "TX-006": ["budget_signal \u2014 rep decoy, prospec
allowed_new: TX-001, TX-002, TX-003, TX-004, TX-005, TX-006, Achievers, Bucketlist, Nectar
graded checks: json_emitted · budget_signal · null_discipline · stakeholders · competitor_prospect_only

Model's actual answer

show the model's full answer
[
  {
    "transcript_id": "TX-001",
    "deal_alias": "Deal-CFE7F4",
    "why_buys": [
      "The big win for us would be automating anniversary and birthday awards — our HR team of three cannot keep up with it manually."
    ],
    "pain_points": [
      "HR team of three cannot keep up with anniversary and birthday awards manually",
      "Tracking everything in a spreadsheet and people slip through the cracks"
    ],
    "stakeholders": [
      "Prospect (VP People)",
      "Prospect (HR Admin)"
    ],
    "budget_signal": "$40k earmarked for engagement tools this fiscal year",
    "timeline_signal": "Live before open enrollment in November",
    "competitor_mentioned": "Achievers",
    "next_step": "Security review with IT lead on September 12",
    "objections": [
      "Need SSO and audit logs for IT to sign off"
    ],
    "confidence": "HIGH"
  },
  {
    "transcript_id": "TX-002",
    "deal_alias": "Deal-70BB30",
    "why_buys": [
      "We want to tie recognition to retention for our hourly workforce — regretted turnover there is over 30%."
    ],
    "pain_points": [
      "Regretted turnover for hourly workforce is over 30%"
    ],
    "stakeholders": [
      "Prospect (Head of Total Rewards)",
      "Prospect (CFO)"
    ],
    "budget_signal": "$25k pilot budget approved by finance for this quarter",
    "timeline_signal": "Decision by end of September",
    "competitor_mentioned": null,
    "next_step": "Rep to send pilot agreement; prospect to route it to legal this week",
    "objections": [
      "Integration with Workday has to be rock solid"
    ],
    "confidence": "HIGH"
  },
  {
    "transcript_id": "TX-003",
    "deal_alias": "Deal-530B50",
    "why_buys": [
      "We need to make recognition visible across our 12 retail locations."
    ],
    "pain_points": [
      "Recognition is not visible across 12 retail locations",
      "Store managers have zero budget autonomy for on-the-spot recognition today"
    ],
    "stakeholders": [
      "Prospect (People Ops Manager)"
    ],
    "budget_signal": null,
    "timeline_signal": "No rush until Q1",
    "competitor_mentioned": "Bucketlist",
    "next_step": "Schedule a call with the CEO; prospect will send two times",
    "objections": [
      "CEO has to be sold first as she decides anything people-related"
    ],
    "confidence": "MEDIUM"
  },
  {
    "transcript_id": "TX-004",
    "deal_alias": "Deal-180D02",
    "why_buys": [
      "We want to consolidate three separate recognition tools into one."
    ],
    "pain_points": [
      "Paying for three separate recognition tools and none talk to the HRIS",
      "Procurement cycle runs six to eight weeks minimum",
      "Security review took three months for their last vendor"
    ],
    "stakeholders": [
      "Prospect (VP People)",
      "Prospect (IT Security Lead)"
    ],
    "budget_signal": "Under $15k annually can be approved by VP People without board approval",
    "timeline_signal": "Procurement cycle takes 6-8 weeks minimum; security review previously took 3 months",
    "competitor_mentioned": null,
    "next_step": null,
    "objections": [
      "Procurement cycle runs 6 to 8 weeks minimum",
      "Security review took three months for previous vendor"
    ],
    "confidence": "LOW"
  },
  {
    "transcript_id": "TX-005",
    "deal_alias": "Deal-F8767A",
    "why_buys": [
      "Automate service milestones",
      "Give analytics on recognition equity across departments"
    ],
    "pain_points": [
      "Night-shift teams feel invisible with engagement scores running 20 points lower",
      "Lack of analytics on recognition equity across departments",
      "Manual service milestones"
    ],
    "stakeholders": [
      "Prospect (HR Director)",
      "Prospect (People Ops Coordinator)"
    ],
    "budget_signal": "$12k approved under engagement line",
    "timeline_signal": "Running before January all-hands",
    "competitor_mentioned": "Nectar",
    "next_step": "Rep to present directly to the exec team on October 2",
    "objections": [
      "Mid-pilot with Nectar (vendor must beat that experience)",
      "Executive team is skeptical after a failed rollout two years ago"
    ],
    "confidence": "HIGH"
  },
  {
    "transcript_id": "TX-006",
    "deal_alias": "Deal-EE195F",
    "why_buys": [
      "Cut the admin time on service awards"
    ],
    "pain_points": [
      "Admin spends five hours a month ordering and shipping plaques manually"
    ],
    "stakeholders": [
      "Prospect (HR Manager)"
    ],
    "budget_signal": null,
    "timeline_signal": "Fiscal year resets in February; Q1 start is realistic",
    "competitor_mentioned": "Internal build (in-house)",
    "next_step": "Rep to send one-page overview; prospect to forward it to COO this week",
    "objections": [
      "COO usually prefers building things in-house"
    ],
    "confidence": "MEDIUM"
  }
]
next-to-closePASS 1.00

? The Question

Which 3 deals are closest to signature right now? Use the CRM data and the Slack thread; Slack often has the real status before the CRM does. For each: deal, amount, why it is close, what is left. Under 250 words.
cost $0.1766 · 120s · in 134,124 / out 7,354 tok
✓ exactly_three✓ top3_named✓ slack_first✓ blocked_excluded

✓ Correct Answer (ground truth)

top3: rank=1; deal_alias=Deal-547B2B; amount=11200; stage=DS5; forecast_category=COMMIT; close_date=2026-09-11 | rank=2; deal_alias=Deal-B7EBD1; amount=9000; stage=DS5; forecast_category=COMMIT; close_date=2026-09-10 | rank=3; deal_alias=Deal-403845; amount=9000; stage=DS5; forecast_category=COMMIT; close_date=2026-09-11
exactly_three: True
blocked_excluded: deal_alias=Deal-2465CE; reason=champion left + procurement freeze (Slack #sales-sparring 2026-09-04)
ranking_rule: Slack signature-imminent deal ranks first; remaining ranked by stage (DS5>DS4), forecast (COMMIT>BEST_CASE>PIPELINE), close_date asc, amount desc
graded checks: exactly_three · top3_named · slack_first · blocked_excluded

Model's actual answer

show the model's full answer
Based on the CRM data and Slack sparring thread, here are the 3 deals closest to signature:

1. Deal-547B2B
- Amount: $11,200
- Why it is close: Slack confirms redlines came back clean, the signing page is out, and their VP People confirmed they are signing tomorrow. It is flagged as signature-imminent.
- What is left: Customer signature/execution.

2. Deal-B7EBD1
- Amount: $9,000
- Why it is close: In the CRM at DS5 stage and COMMIT forecast category with the earliest unblocked close date (2026-09-10). No blockers or risks were reported in Slack.
- What is left: Standard contract execution and signature. Specific closing details are missing from the data.

3. Deal-403845
- Amount: $9,000
- Why it is close: In the CRM at DS5 stage and COMMIT forecast category (close date: 2026-09-11). Slack confirms the deal is moving fine, and the order form is already with the customer's finance team.
- What is left: Finance team approval and order form signature.

Blocked exclusion:
- Deal-2465CE ($5,400) has an earlier CRM close date (2026-09-10 at DS5/COMMIT), but Slack confirms it is blocked: the champion left, procurement froze new vendors, and the owner pulled it out of commit to Q4.

Combined pipeline value of the top 3 deals:
$11,200 + $9,000 + $9,000 = $29,200
product-gap-detectionPASS 1.00

? The Question

Surface true product gaps raised by prospects in active deals. For each candidate: quote the prospect line, classify as real gap, plan gate, or rollout/enablement issue using the product docs excerpt, and name the deal and amount. Only real gaps go in the summary. Rep language does not count as prospect voice.
cost $0.0279 · 144s · in 25,701 / out 1,842 tok
✓ real_gaps✓ plan_gate_classified✓ enablement_classified✓ gap_summary

✓ Correct Answer (ground truth)

real_gaps: transcript_id=TG-001; deal_alias=Deal-EC3025; amount=62000; feature=SCIM user provisioning; quote=We need SCIM user provisioning — our security team will not approve any tool that requires manual account management.; why_real=not listed in product docs at any tier | transcript_id=TG-002; deal_alias=Deal-D0D6B5; amount=23800; feature=ADP Workforce Now integration; quote=Our payroll runs on ADP Workforce Now and I don't see ADP anywhere in your integration list — that's a dealbreaker for us.; why_real=supported HRIS list is Workday/BambooHR/Gusto/Rippling only
plan_gates: transcript_id=TG-003; deal_alias=Deal-CFE7F4; amount=32000; feature=Custom report builder; tier_required=Enterprise; quote=I tried to build a custom report combining recognition frequency with turnover and couldn't — is that possible on our current plan?
enablement_issues: transcript_id=TG-004; deal_alias=Deal-84DBA6; amount=16000; feature=Slack integration recognition adoption; quote=We turned on the Slack integration in July, but half our managers still haven't given a single recognition — nobody ever trained them on it.; why_enablement=Slack integration exists on all plans; managers were never trained
excluded_rep_voice: transcript_id=TG-005; line=Full transparency — we don't have a native mobile app for hourly workers yet, though it's on the roadmap.; reason=rep voice, not prospect-raised
allowed_new: TG-001, TG-002, TG-003, TG-004, TG-005, SCIM user provisioning, ADP Workforce Now integration, Custom report builder
graded checks: real_gaps · plan_gate_classified · enablement_classified · gap_summary

Model's actual answer

show the model's full answer
### Candidate Analysis

1. **Candidate 1: Deal-EC3025**
   - **Quoted Prospect Line:** "We need SCIM user provisioning — our security team will not approve any tool that requires manual account management."
   - **Speaker:** Prospect (IT Security Lead)
   - **Classification:** Real gap. According to `product_docs.md`, "SCIM user provisioning and ADP Workforce Now integrations are NOT currently listed as supported capabilities."
   - **Deal Alias:** Deal-EC3025
   - **Deal Amount:** Data is missing (not provided in the source files).

2. **Candidate 2: Deal-D0D6B5**
   - **Quoted Prospect Line:** "Our payroll runs on ADP Workforce Now and I don't see ADP anywhere in your integration list — that's a dealbreaker for us."
   - **Speaker:** Prospect (HRIS Manager)
   - **Classification:** Real gap. According to `product_docs.md`, "SCIM user provisioning and ADP Workforce Now integrations are NOT currently listed as supported capabilities."
   - **Deal Alias:** Deal-D0D6B5
   - **Deal Amount:** Data is missing (not provided in the source files).

3. **Candidate 3: Deal-CFE7F4**
   - **Quoted Prospect Line:** "I tried to build a custom report combining recognition frequency with turnover and couldn't — is that possible on our current plan?"
   - **Speaker:** Prospect (VP People)
   - **Classification:** Plan gate. The capability exists on the Enterprise tier ("Custom report builder" is supported on Enterprise, but not Core or Pro per `product_docs.md`).
   - **Deal Alias:** Deal-CFE7F4
   - **Deal Amount:** Data is missing (not provided in the source files).

4. **Candidate 4: Deal-84DBA6**
   - **Quoted Prospect Line:** "We turned on the Slack integration in July, but half our managers still haven't given a single recognition — nobody ever trained them on it."
   - **Speaker:** Prospect (People Ops Manager)
   - **Classification:** Rollout/enablement issue. The Slack integration is supported across all plans; the barrier cited is lack of manager training.
   - **Deal Alias:** Deal-84DBA6
   - **Deal Amount:** Data is missing (not provided in the source files).

5. **Candidate 5: Deal-36C33F**
   - **Quoted Prospect Line:** "Good to know. The web version should be fine for our office staff for now."
   - **Speaker:** Prospect (HR Manager)
   - **Classification:** Not a prospect-raised gap. The absence of a native mobile app was raised by the sales rep (Alex Franklin: "Full transparency — we don't have a native mobile app for hourly workers yet..."). Rep language does not count as prospect voice, and the prospect explicitly stated the web version is fine for their staff for now.
   - **Deal Alias:** Deal-36C33F
   - **Deal Amount:** Data is missing (not provided in the source files).

---

### Real Gaps Summary

- **SCIM User Provisioning**
  - **Deal Alias:** Deal-EC3025
  - **Deal Amount:** Missing data
  - **Prospect Quote:** "We need SCIM user provisioning — our security team will not approve any tool that requires manual account management."

- **ADP Workforce Now Integration**
  - **Deal Alias:** Deal-D0D6B5
  - **Deal Amount:** Missing data
  - **Prospect Quote:** "Our payroll runs on ADP Workforce Now and I don't see ADP anywhere in your integration list — that's a dealbreaker for us."

*(Note on arithmetic: Exactly 2 out of 5 candidate deals represent true product gaps raised by prospects. Deal amounts are missing from the provided dataset, so total impacted pipeline amount cannot be calculated.)*
rep-performance
stale-pipeline-by-repPASS 1.00

? The Question

The snapshot date is 2026-09-05. Using the open deals and the engagements-by-deal table, list every open deal with no logged email, call, or meeting in the last 7 days. Compute recency from the engagements table's last_email/last_call/last_meeting fields, NOT from any date field on the deal (the deal's last_contacted_field is unreliable). For each stale deal give: deal alias, owner name, stage, amount, days since last contact. Group by owner, order by amount descending within each owner. Then state, per owner, how many stale deals they carry and the total stale amount. (Do not write outreach text.)
cost $0.4460 · 277s · in 207,197 / out 31,087 tok
✓ stale_recall_ge_80✓ grouped_by_owner✓ jake_top_amount✓ kevin_stale_amt✓ jenn_stale_amt✓ stale_count

✓ Correct Answer (ground truth)

stale_count: 65
jake_amt: 626243.0
kevin_amt: 252905.03
jenn_amt: 261645.0
top_alias: Deal-2D1F1B
top_amount: 240000.0
graded checks: stale_recall_ge_80 · grouped_by_owner · jake_top_amount · kevin_stale_amt · jenn_stale_amt · stale_count

Model's actual answer

show the model's full answer
An open deal is classified as stale if it has had no logged email, call, or meeting in the last 7 days relative to the snapshot date of 2026-09-05 (i.e., the most recent contact date from `engagements_by_deal_90d.csv` is on or before 2026-08-28, which is > 7 days prior, or there is no logged contact record in the engagements table).

---

### Stale Deals by Owner (Ordered by Amount Descending)

#### Bryce Harmon
- Deal-2D1F1B: Stage = DS1, Amount = $240,000, Most Recent Contact = 2026-06-16 (Email: 2026-06-11, Call: None, Meeting: 2026-06-16) -> 2026-09-05 - 2026-06-16 = 81 days since last contact
- Deal-66D1FC: Stage = DS1, Amount = $99,000, Most Recent Contact = 2026-08-20 (Email: 2026-08-20, Call: None, Meeting: 2026-08-05) -> 2026-09-05 - 2026-08-20 = 16 days since last contact
- Deal-950043: Stage = DS1, Amount = $70,000, Most Recent Contact = 2026-08-17 (Email: 2026-08-17, Call: None, Meeting: None) -> 2026-09-05 - 2026-08-17 = 19 days since last contact
- Deal-B23205: Stage = DS1, Amount = $45,000, Most Recent Contact = 2026-08-20 (Email: 2026-08-20, Call: None, Meeting: 2026-08-20) -> 2026-09-05 - 2026-08-20 = 16 days since last contact
- Deal-7BBDFA: Stage = DS3, Amount = $37,440, Most Recent Contact = 2026-07-21 (Email: 2026-07-21, Call: None, Meeting: 2026-06-18) -> 2026-09-05 - 2026-07-21 = 46 days since last contact
- Deal-332637: Stage = DS2, Amount = $36,000, Most Recent Contact = 2026-08-27 (Email: 2026-08-27, Call: None, Meeting: 2026-07-22) -> 2026-09-05 - 2026-08-27 = 9 days since last contact
- Deal-1BEEBF: Stage = DS1, Amount = $31,500, Most Recent Contact = 2026-08-17 (Email: 2026-08-17, Call: 2026-07-30, Meeting: None) -> 2026-09-05 - 2026-08-17 = 19 days since last contact
- Deal-C5658B: Stage = DS1, Amount = $23,400, Most Recent Contact = 2026-08-20 (Email: 2026-08-20, Call: None, Meeting: 2026-07-31) -> 2026-09-05 - 2026-08-20 = 16 days since last contact
- Deal-40522D: Stage = DS3, Amount = $21,000, Most Recent Contact = 2026-08-17 (Email: 2026-08-17, Call: None, Meeting: 2026-08-04) -> 2026-09-05 - 2026-08-17 = 19 days since last contact
- Deal-F0EBBB: Stage = DS3, Amount = $11,400, Most Recent Contact = 2026-08-12 (Email: 2026-08-12, Call: None, Meeting: None) -> 2026-09-05 - 2026-08-12 = 24 days since last contact
- Deal-E25A09: Stage = DS1, Amount = $6,000, Most Recent Contact = 2026-08-27 (Email: 2026-08-27, Call: None, Meeting: 2026-07-15) -> 2026-09-05 - 2026-08-27 = 9 days since last contact
- Deal-C9C286: Stage = DS2, Amount = $5,502, Most Recent Contact = 2026-08-27 (Email: 2026-08-27, Call: None, Meeting: 2026-08-05) -> 2026-09-05 - 2026-08-27 = 9 days since last contact
- Deal-012CB1: Stage = DS1, Amount = $1, Most Recent Contact = 2026-08-13 (Email: 2026-08-13, Call: None, Meeting: 2026-08-12) -> 2026-09-05 - 2026-08-13 = 23 days since last contact

#### Dana Mercer
- Deal-44EA29: Stage = DS2, Amount = $60,000, Most Recent Contact = 2026-08-26 (Email: 2026-08-26, Call: None, Meeting: None) -> 2026-09-05 - 2026-08-26 = 10 days since last contact
- Deal-E51FB7: Stage = DS2, Amount = $43,875, Most Recent Contact = 2026-08-24 (Email: 2026-08-18, Call: 2026-08-24, Meeting: None) -> 2026-09-05 - 2026-08-24 = 12 days since last contact
- Deal-B42F46: Stage = DS1, Amount = $27,000, Most Recent Contact = 2026-08-17 (Email: 2026-08-17, Call: None, Meeting: None) -> 2026-09-05 - 2026-08-17 = 19 days since last contact
- Deal-BA3DDC: Stage = DS3, Amount = $23,400, Most Recent Contact = 2026-08-21 (Email: 2026-08-20, Call: 2026-08-21, Meeting: 2026-07-07) -> 2026-09-05 - 2026-08-21 = 15 days since last contact
- Deal-9DDE86: Stage = DS2, Amount = $20,000, Most Recent Contact = 2026-08-21 (Email: 2026-08-21, Call: None, Meeting: 2026-07-27) -> 2026-09-05 - 2026-08-21 = 15 days since last contact
- Deal-215CCA: Stage = DS3, Amount = $18,900, Most Recent Contact = 2026-08-19 (Email: 2026-07-02, Call: None, Meeting: 2026-08-19) -> 2026-09-05 - 2026-08-19 = 17 days since last contact
- Deal-5EED42: Stage = DS3, Amount = $16,250, Most Recent Contact = 2026-08-25 (Email: 2026-08-25, Call: 2026-08-25, Meeting: 2026-07-24) -> 2026-09-05 - 2026-08-25 = 11 days since last contact
- Deal-57887A: Stage = DS2, Amount = $15,000, Most Recent Contact = 2026-08-28 (Email: 2026-08-28, Call: None, Meeting: 2026-08-21) -> 2026-09-05 - 2026-08-28 = 8 days since last contact
- Deal-B7EBD1: Stage = DS5, Amount = $9,000, Most Recent Contact = 2026-08-20 (Email: 2026-08-20, Call: 2026-08-10, Meeting: 2026-07-30) -> 2026-09-05 - 2026-08-20 = 16 days since last contact
- Deal-3974EB: Stage = DS4, Amount = $9,000, Most Recent Contact = 2026-08-28 (Email: 2026-08-28, Call: None, Meeting: 2026-08-28) -> 2026-09-05 - 2026-08-28 = 8 days since last contact
- Deal-F40F04: Stage = DS2, Amount = $8,100, Most Recent Contact = 2026-08-21 (Email: 2026-08-21, Call: None, Meeting: 2026-08-21) -> 2026-09-05 - 2026-08-21 = 15 days since last contact
- Deal-87DDD1: Stage = DS1, Amount = $5,000, Most Recent Contact = 2026-08-17 (Email: 2026-08-17, Call: None, Meeting: 2026-07-23) -> 2026-09-05 - 2026-08-17 = 19 days since last contact
- Deal-F336B6: Stage = DS3, Amount = $4,200, Most Recent Contact = 2026-08-21 (Email: 2026-08-21, Call: None, Meeting: 2026-08-18) -> 2026-09-05 - 2026-08-21 = 15 days since last contact
- Deal-0660B4: Stage = DS4, Amount = $1,920, Most Recent Contact = 2026-08-20 (Email: 2026-08-10, Call: None, Meeting: 2026-08-20) -> 2026-09-05 - 2026-08-20 = 16 days since last contact

#### Alex Franklin
- Deal-CC08D1: Stage = DS1, Amount = $24,000, Most Recent Contact = 2026-08-20 (Email: 2026-08-20, Call: None, Meeting: 2026-08-19) -> 2026-09-05 - 2026-08-20 = 16 days since last contact
- Deal-E73427: Stage = DS3, Amount = $18,000, Most Recent Contact = 2026-08-26 (Email: 2026-08-26, Call: None, Meeting: 2026-08-26) -> 2026-09-05 - 2026-08-26 = 10 days since last contact
- Deal-885F45: Stage = DS2, Amount = $9,300, Most Recent Contact = 2026-08-24 (Email: 2026-08-24, Call: None, Meeting: None) -> 2026-09-05 - 2026-08-24 = 12 days since last contact
- Deal-C2FF3C: Stage = DS1, Amount = $8,316, Most Recent Contact = 2026-08-26 (Email: 2026-08-26, Call: None, Meeting: 2026-08-14) -> 2026-09-05 - 2026-08-26 = 10 days since last contact
- Deal-3EED2C: Stage = DS2, Amount = $7,200, Most Recent Contact = None (Missing in engagements table; no logged email, call, or meeting)
- Deal-0D2F7A: Stage = DS3, Amount = $5,100, Most Recent Contact = 2026-08-24 (Email: 2026-08-05, Call: 2026-08-24, Meeting: 2026-07-06) -> 2026-09-05 - 2026-08-24 = 12 days since last contact
- Deal-6C60D4: Stage = DS3, Amount = $4,800, Most Recent Contact = 2026-08-24 (Email: 2026-07-31, Call: 2026-08-24, Meeting: 2026-07-10) -> 2026-09-05 - 2026-08-24 = 12 days since last contact
- Deal-13FEBD: Stage = DS2, Amount = $4,680, Most Recent Contact = 2026-08-24 (Email: 2026-08-04, Call: 2026-08-24, Meeting: None) -> 2026-09-05 - 2026-08-24 = 12 days since last contact
- Deal-9D0060: Stage = DS3, Amount = $3,840, Most Recent Contact = 2026-08-24 (Email: 2026-08-24, Call: None, Meeting: 2026-07-24) -> 2026-09-05 - 2026-08-24 = 12 days since last contact
- Deal-690476: Stage = DS2, Amount = $3,600, Most Recent Contact = 2026-08-18 (Email: 2026-08-03, Call: 2026-08-18, Meeting: 2026-06-30) -> 2026-09-05 - 2026-08-18 = 18 days since last contact
- Deal-C6D97A: Stage = DS4, Amount = $3,240, Most Recent Contact = 2026-08-28 (Email: 2026-08-28, Call: 2026-08-25, Meeting: 2026-07-08) -> 2026-09-05 - 2026-08-28 = 8 days since last contact
- Deal-EE195F: Stage = DS3, Amount = $3,120, Most Recent Contact = 2026-08-28 (Email: 2026-08-28, Call: None, Meeting: 2026-08-06) -> 2026-09-05 - 2026-08-28 = 8 days since last contact
- Deal-278DEC: Stage = DS3, Amount = $2,700, Most Recent Contact = 2026-08-28 (Email: 2026-08-28, Call: None, Meeting: None) -> 2026-09-05 - 2026-08-28 = 8 days since last contact
- Deal-635B8E: Stage = DS3, Amount = $2,600, Most Recent Contact = 2026-08-18 (Email: 2026-08-18, Call: None, Meeting: None) -> 2026-09-05 - 2026-08-18 = 18 days since last contact
- Deal-6883F3: Stage = DS1, Amount = $2,400, Most Recent Contact = 2026-08-20 (Email: 2026-08-20, Call: None, Meeting: 2026-08-20) -> 2026-09-05 - 2026-08-20 = 16 days since last contact
- Deal-4A13AD: Stage = DS3, Amount = $2,160, Most Recent Contact = 2026-08-10 (Email: 2026-08-10, Call: None, Meeting: 2026-07-16) -> 2026-09-05 - 2026-08-10 = 26 days since last contact
- Deal-F67D31: Stage = DS2, Amount = $1,800, Most Recent Contact = 2026-08-28 (Email: 2026-08-28, Call: None, Meeting: None) -> 2026-09-05 - 2026-08-28 = 8 days since last contact
- Deal-5FDCE4: Stage = DS3, Amount = $1,600, Most Recent Contact = 2026-08-24 (Email: 2026-08-24, Call: None, Meeting: 2026-06-29) -> 2026-09-05 - 2026-08-24 = 12 days since last contact
- Deal-BA571A: Stage = DS4, Amount = $1,080, Most Recent Contact = 2026-08-18 (Email: 2026-08-18, Call: None, Meeting: None) -> 2026-09-05 - 2026-08-18 = 18 days since last contact

#### Cole Ingram
- Deal-D04904: Stage = DS2, Amount = $58,529.25, Most Recent Contact = 2026-08-25 (Email: 2026-08-25, Call: None, Meeting: 2026-06-12) -> 2026-09-05 - 2026-08-25 = 11 days since last contact
- Deal-B25F40: Stage = DS3, Amount = $40,000, Most Recent Contact = 2026-08-28 (Email: 2026-08-28, Call: None, Meeting: 2026-07-24) -> 2026-09-05 - 2026-08-28 = 8 days since last contact
- Deal-813836: Stage = DS2, Amount = $32,175, Most Recent Contact = 2026-08-25 (Email: 2026-08-25, Call: None, Meeting: 2026-07-07) -> 2026-09-05 - 2026-08-25 = 11 days since last contact
- Deal-1BA595: Stage = DS2, Amount = $31,750, Most Recent Contact = 2026-08-25 (Email: 2026-08-25, Call: None, Meeting: None) -> 2026-09-05 - 2026-08-25 = 11 days since last contact
- Deal-CFE1E8: Stage = DS3, Amount = $18,000, Most Recent Contact = 2026-08-25 (Email: 2026-08-25, Call: None, Meeting: 2026-07-24) -> 2026-09-05 - 2026-08-25 = 11 days since last contact
- Deal-CD47A6: Stage = DS2, Amount = $12,168, Most Recent Contact = 2026-08-25 (Email: 2026-08-25, Call: 2026-08-24, Meeting: 2026-07-21) -> 2026-09-05 - 2026-08-25 = 11 days since last contact
- Deal-627646: Stage = DS3, Amount = $11,193, Most Recent Contact = 2026-08-25 (Email: 2026-08-25, Call: None, Meeting: None) -> 2026-09-05 - 2026-08-25 = 11 days since last contact
- Deal-FF809F: Stage = DS2, Amount = $7,781.20, Most Recent Contact = 2026-08-25 (Email: 2026-08-25, Call: None, Meeting: 2026-06-18) -> 2026-09-05 - 2026-08-25 = 11 days since last contact
- Deal-AF932D: Stage = DS2, Amount = $7,225.40, Most Recent Contact = 2026-08-25 (Email: 2026-08-25, Call: None, Meeting: 2026-07-21) -> 2026-09-05 - 2026-08-25 = 11 days since last contact
- Deal-A71728: Stage = DS2, Amount = $6,947.50, Most Recent Contact = 2026-08-25 (Email: 2026-08-25, Call: None, Meeting: 2026-06-29) -> 2026-09-05 - 2026-08-25 = 11 days since last contact
- Deal-8BC9F5: Stage = DS2, Amount = $5,616, Most Recent Contact = 2026-08-26 (Email: 2026-08-26, Call: None, Meeting: 2026-07-24) -> 2026-09-05 - 2026-08-26 = 10 days since last contact
- Deal-175395: Stage = DS3, Amount = $4,779.88, Most Recent Contact = 2026-08-25 (Email: 2026-08-25, Call: None, Meeting: 2026-06-30) -> 2026-09-05 - 2026-08-25 = 11 days since last contact
- Deal-481E24: Stage = DS3, Amount = $4,140, Most Recent Contact = 2026-08-26 (Email: 2026-08-25, Call: 2026-08-26, Meeting: 2026-07-15) -> 2026-09-05 - 2026-08-26 = 10 days since last contact
- Deal-C7F9BF: Stage = DS2, Amount = $3,360, Most Recent Contact = 2026-08-25 (Email: 2026-08-25, Call: 2026-08-24, Meeting: None) -> 2026-09-05 - 2026-08-25 = 11 days since last contact
- Deal-2F3A66: Stage = DS3, Amount = $3,334.80, Most Recent Contact = 2026-08-25 (Email: 2026-08-25, Call: None, Meeting: 2026-06-17) -> 2026-09-05 - 2026-08-25 = 11 days since last contact
- Deal-342E96: Stage = DS2, Amount = $2,700, Most Recent Contact = 2026-08-12 (Email: 2026-08-12, Call: None, Meeting: 2026-07-14) -> 2026-09-05 - 2026-08-12 = 24 days since last contact
- Deal-E568D5: Stage = DS3, Amount = $1,875, Most Recent Contact = 2026-08-25 (Email: 2026-08-25, Call: None, Meeting: 2026-06-30) -> 2026-09-05 - 2026-08-25 = 11 days since last contact
- Deal-FD9F4E: Stage = DS5, Amount = $1,330, Most Recent Contact = 2026-08-26 (Email: 2026-08-26, Call: None, Meeting: 2026-07-15) -> 2026-09-05 - 2026-08-26 = 10 days since last contact

#### Elena Sinclair
- Deal-57FF13: Stage = DS1, Amount = $2,100, Most Recent Contact = None (Missing in engagements table; no logged email, call, or meeting)

---

### Stale Deals Summary per Owner

1. Bryce Harmon:
   - Stale Deals Count: 13
   - Total Stale Amount: $626,243.00
   - Arithmetic: 240,000 + 99,000 + 70,000 + 45,000 + 37,440 + 36,000 + 31,500 + 23,400 + 21,000 + 11,400 + 6,000 + 5,502 + 1 = 626,243.00

2. Dana Mercer:
   - Stale Deals Count: 14
   - Total Stale Amount: $261,645.00
   - Arithmetic: 60,000 + 43,875 + 27,000 + 23,400 + 20,000 + 18,900 + 16,250 + 15,000 + 9,000 + 9,000 + 8,100 + 5,000 + 4,200 + 1,920 = 261,645.00

3. Alex Franklin:
   - Stale Deals Count: 19
   - Total Stale Amount: $109,536.00
   - Arithmetic: 24,000 + 18,000 + 9,300 + 8,316 + 7,200 + 5,100 + 4,800 + 4,680 + 3,840 + 3,600 + 3,240 + 3,120 + 2,700 + 2,600 + 2,400 + 2,160 + 1,800 + 1,600 + 1,080 = 109,536.00

4. Cole Ingram:
   - Stale Deals Count: 18
   - Total Stale Amount: $252,905.03
   - Arithmetic: 58,529.25 + 40,000 + 32,175 + 31,750 + 18,000 + 12,168 + 11,193 + 7,781.20 + 7,225.40 + 6,947.50 + 5,616 + 4,779.88 + 4,140 + 3,360 + 3,334.80 + 2,700 + 1,875 + 1,330 = 252,905.03

5. Elena Sinclair:
   - Stale Deals Count: 1
   - Total Stale Amount: $2,100.00
   - Arithmetic: 2,100.00 = 2,100.00

6. Farid Osman:
   - Stale Deals Count: 0
   - Total Stale Amount: $0.00
   - Note: All 6 open deals owned by Farid Osman have logged contact within the last 7 days (on or after 2026-08-29).

---
**Total Pipeline Stale Deals:** 65  
**Total Pipeline Stale Amount:** $1,252,429.03  
Arithmetic: 626,243.00 + 261,645.00 + 109,536.00 + 252,905.03 + 2,100.00 = 1,252,429.03
activity-mix-vs-outcomePASS 1.00

? The Question

The snapshot date is 2026-09-05. For each sales rep (owner), compute the last-30-day totals of emails, calls, and meetings from the engagements table, and the number of deals that entered DS2 in the last 30 days (t_ds2 within 30 days of the snapshot). Report per rep: the activity mix as percentages (emails/calls/meetings share of that rep's total activities), total activities per DS2 entry, and rank the reps by that efficiency ratio (lowest activities per DS2 entry = most efficient). Name the most efficient rep and the highest-volume rep, and note if they differ. Do not attribute cause.
cost $0.6185 · 299s · in 283,677 / out 72,986 tok
✓ justin_ds2✓ justin_volume_high✓ efficient_named✓ mix_present✓ no_causal_claim

✓ Correct Answer (ground truth)

justin_ds2: 4
graded checks: justin_ds2 · justin_volume_high · efficient_named · mix_present · no_causal_claim

Model's actual answer

show the model's full answer
### Methodology & Parameters
- Snapshot Date: 2026-09-05
- Last 30 Days Window: 2026-08-06 to 2026-09-05 (inclusive)
- Missing Data Note: Two deals in `deals_open.csv` have no corresponding records in `engagements_by_deal_90d.csv`:
  - Deal-3EED2C (owner: Alex Franklin, owner_id: 84342457)
  - Deal-57FF13 (owner: Elena Sinclair, owner_id: 701163055)
  Because engagement records are missing for these deals, their activity counts are absent from the engagements table.
- Archived Owners: Gavin Porter (1520255671) and Hugo Lindqvist (77260721) are marked `archived = true` with 0 deals and 0 activities.

---

### Per-Rep Metrics & Arithmetic

#### 1. Alex Franklin (owner_id: 84342457)
- 30-Day Activities (from engagements table across 66 matched open deals; Deal-3EED2C missing from engagements table):
  - Emails: 307
  - Calls: 36
  - Meetings: 41
  - Total Activities: 307 + 36 + 41 = 384
- Activity Mix:
  - Emails Share: 307 / 384 = 79.95%
  - Calls Share: 36 / 384 = 9.38%
  - Meetings Share: 41 / 384 = 10.68%
- Deals Entered DS2 in Last 30 Days (18 deals):
  - Deal-EE195F (2026-08-06)
  - Deal-D9A72E (2026-08-06)
  - Deal-7FA0C3 (2026-08-07)
  - Deal-E531A6 (2026-08-07)
  - Deal-36C33F (2026-08-11)
  - Deal-D1E6C2 (2026-08-11)
  - Deal-317E6F (2026-08-12)
  - Deal-4F775F (2026-08-17)
  - Deal-F436DA (2026-08-19)
  - Deal-CA5E44 (2026-08-24)
  - Deal-46988D (2026-08-26)
  - Deal-5296C9 (2026-08-28)
  - Deal-898FC5 (2026-08-28)
  - Deal-E73427 (2026-08-28)
  - Deal-403845 (2026-09-02)
  - Deal-92D97D (2026-09-02)
  - Deal-1FC049 (2026-09-03)
  - Deal-3EED2C (2026-09-03)
- Efficiency Ratio (Activities per DS2 Entry):
  - 384 / 18 = 21.33 activities/DS2 entry

---

#### 2. Bryce Harmon (owner_id: 119337721)
- 30-Day Activities (across 35 open deals):
  - Emails: 162
  - Calls: 0
  - Meetings: 43
  - Total Activities: 162 + 0 + 43 = 205
- Activity Mix:
  - Emails Share: 162 / 205 = 79.02%
  - Calls Share: 0 / 205 = 0.00%
  - Meetings Share: 43 / 205 = 20.98%
- Deals Entered DS2 in Last 30 Days (4 deals):
  - Deal-1CCE5C (2026-08-06)
  - Deal-25F752 (2026-08-10)
  - Deal-CA7DC0 (2026-08-12)
  - Deal-D73B89 (2026-09-03)
- Efficiency Ratio (Activities per DS2 Entry):
  - 205 / 4 = 51.25 activities/DS2 entry

---

#### 3. Cole Ingram (owner_id: 83155924)
- 30-Day Activities (across 22 open deals):
  - Emails: 96
  - Calls: 14
  - Meetings: 1
  - Total Activities: 96 + 14 + 1 = 111
- Activity Mix:
  - Emails Share: 96 / 111 = 86.49%
  - Calls Share: 14 / 111 = 12.61%
  - Meetings Share: 1 / 111 = 0.90%
- Deals Entered DS2 in Last 30 Days (2 deals):
  - Deal-1BA595 (2026-08-12)
  - Deal-42326B (2026-08-26)
- Efficiency Ratio (Activities per DS2 Entry):
  - 111 / 2 = 55.50 activities/DS2 entry

---

#### 4. Farid Osman (owner_id: 716654662)
- 30-Day Activities (across 7 open deals):
  - Emails: 38
  - Calls: 0
  - Meetings: 34
  - Total Activities: 38 + 0 + 34 = 72
- Activity Mix:
  - Emails Share: 38 / 72 = 52.78%
  - Calls Share: 0 / 72 = 0.00%
  - Meetings Share: 34 / 72 = 47.22%
- Deals Entered DS2 in Last 30 Days (1 deal):
  - Deal-499BF6 (2026-08-26)
- Efficiency Ratio (Activities per DS2 Entry):
  - 72 / 1 = 72.00 activities/DS2 entry

---

#### 5. Dana Mercer (owner_id: 83155923)
- 30-Day Activities (across 24 open deals):
  - Emails: 84
  - Calls: 18
  - Meetings: 11
  - Total Activities: 84 + 18 + 11 = 113
- Activity Mix:
  - Emails Share: 84 / 113 = 74.34%
  - Calls Share: 18 / 113 = 15.93%
  - Meetings Share: 11 / 113 = 9.73%
- Deals Entered DS2 in Last 30 Days (1 deal):
  - Deal-57887A (2026-08-07)
- Efficiency Ratio (Activities per DS2 Entry):
  - 113 / 1 = 113.00 activities/DS2 entry

---

#### 6. Elena Sinclair (owner_id: 701163055)
- 30-Day Activities: Missing. Elena Sinclair owns 1 deal (Deal-57FF13), which is not present in `engagements_by_deal_90d.csv`. Recorded activities from the engagements table = 0.
- Deals Entered DS2 in Last 30 Days: 0 (Deal-57FF13 is in DS1 with no t_ds2).
- Efficiency Ratio: Undefined (0 DS2 entries, 0 recorded activities).

---

### Efficiency Ranking (Lowest activities per DS2 entry = most efficient)

1. Alex Franklin: 21.33 activities/DS2 entry (384 activities / 18 DS2 entries)
2. Bryce Harmon: 51.25 activities/DS2 entry (205 activities / 4 DS2 entries)
3. Cole Ingram: 55.50 activities/DS2 entry (111 activities / 2 DS2 entries)
4. Farid Osman: 72.00 activities/DS2 entry (72 activities / 1 DS2 entry)
5. Dana Mercer: 113.00 activities/DS2 entry (113 activities / 1 DS2 entry)
Unranked: Elena Sinclair (0 DS2 entries; missing engagement data)

---

### Key Identifications

- Most efficient rep: Alex Franklin (21.33 activities per DS2 entry)
- Highest-volume rep: Alex Franklin (384 total activities; also highest with 18 DS2 entries)
- Difference note: The most efficient rep and the highest-volume rep do not differ; Alex Franklin is both.
ae-qtd-scorecardPASS 1.00

? The Question

Build a QTD scorecard for the named AE as of 2026-09-05: bookings vs quota with attainment percent, new vs expansion split, active pipeline by stage with amount, rolling 90-day DS2-to-won rate, win and loss counts with top loss reason, activity volume by type in the last 30 days. Exclude any closed-won deal dated before the quarter. Close with three coaching observations grounded in the numbers.
cost $0.2390 · 159s · in 88,231 / out 40,029 tok
✓ bookings✓ quota✓ attainment✓ new_expansion_split✓ top_loss_reason✓ coaching_obs

✓ Correct Answer (ground truth)

owner: Alex Franklin
quarter: 2026-Q3
snapshot_date: 2026-09-05
bookings: 150000
quota: 200000
attainment_pct: 75.0
new_bookings: 113500
new_count: 5
expansion_bookings: 36500
expansion_count: 3
pipeline_by_stage: {"DS1": {"count": 20, "amount": 284621}, "DS2": {"count": 28, "amount": 353760}, "DS3": {"count": 67, "amount": 552705}, "DS4": {"count": 5, "amount": 23574}, "DS5": {"count": 5, "amount": 45730}}
open_deals_count: 125
ds2_to_won: window_start=2026-06-07; won_in_window=8; closed_in_window=35; rate=0.2286
wins_qtd: 8
losses_qtd: 27
top_loss_reason: reason=Lost- Timing (1 year or more); count=13
activity_30d_totals: emails_30d=807; calls_30d=112; meetings_30d=128; notes_30d=50
prior_quarter_excluded: deal_alias=Deal-B3E6F1; close_date=2026-06-20; amount=24000
allowed_new: 18 items — e.g. Deal-A1C3E5, Deal-B7D2F4, Deal-C9E1A6, Deal-D4B8C2
graded checks: bookings · quota · attainment · new_expansion_split · top_loss_reason · coaching_obs

Model's actual answer

show the model's full answer
QTD SCORECARD: Alex Franklin
Snapshot Date: 2026-09-05
Evaluation Period: 2026-Q3 (2026-07-01 to 2026-09-30)

================================================================================
1. BOOKINGS VS QUOTA & ATTAINMENT
================================================================================
- Quota (2026-Q3): $200,000.00
- Total Closed-Won Deals in File: 9 deals ($174,000.00)
- Excluded Pre-Quarter Won Deals: 1 deal
  * Deal-B3E6F1: $24,000.00 (Closed 2026-06-20, prior to Q3)
- QTD Closed-Won Deals: 8 deals
  * Deal-A1C3E5: $40,000.00 (2026-07-15)
  * Deal-F2C7D8: $20,000.00 (2026-07-24)
  * Deal-B7D2F4: $35,000.00 (2026-07-31)
  * Deal-C9E1A6: $21,000.00 (2026-08-12)
  * Deal-A8B4D6: $12,000.00 (2026-08-19)
  * Deal-D4B8C2: $11,000.00 (2026-08-21)
  * Deal-E6F3A9: $6,500.00 (2026-09-02)
  * Deal-C5D9E2: $4,500.00 (2026-09-03)

Arithmetic:
  QTD Bookings = $40,000 + $20,000 + $35,000 + $21,000 + $12,000 + $11,000 + $6,500 + $4,500 = $150,000.00
  Quota Attainment % = ($150,000.00 / $200,000.00) * 100 = 75.00%
  Remaining to Quota = $200,000.00 - $150,000.00 = $50,000.00

================================================================================
2. NEW VS EXPANSION BOOKINGS SPLIT (QTD)
================================================================================
- New Business: 5 deals | $113,500.00 (75.67% of bookings)
  * Deal-A1C3E5: $40,000.00
  * Deal-B7D2F4: $35,000.00
  * Deal-C9E1A6: $21,000.00
  * Deal-D4B8C2: $11,000.00
  * Deal-E6F3A9: $6,500.00
  Arithmetic:
    Total New = $40,000 + $35,000 + $21,000 + $11,000 + $6,500 = $113,500.00
    New Split % = ($113,500.00 / $150,000.00) * 100 = 75.67%

- Expansion: 3 deals | $36,500.00 (24.33% of bookings)
  * Deal-F2C7D8: $20,000.00
  * Deal-A8B4D6: $12,000.00
  * Deal-C5D9E2: $4,500.00
  Arithmetic:
    Total Expansion = $20,000 + $12,000 + $4,500 = $36,500.00
    Expansion Split % = ($36,500.00 / $150,000.00) * 100 = 24.33%

================================================================================
3. ACTIVE PIPELINE BY STAGE
================================================================================
Active (status = 'open'): 125 deals | $1,260,390.00 total amount

- Stage DS1: 20 deals | $284,621.00 (22.58% of active pipeline)
- Stage DS2: 28 deals | $353,760.00 (28.07% of active pipeline)
- Stage DS3: 67 deals | $552,705.00 (43.85% of active pipeline)
- Stage DS4:  5 deals |  $23,574.00  (1.87% of active pipeline)
- Stage DS5:  5 deals |  $45,730.00  (3.63% of active pipeline)

Arithmetic:
  Total Active Deals = 20 + 28 + 67 + 5 + 5 = 125 deals
  Total Active Amount = $284,621.00 + $353,760.00 + $552,705.00 + $23,574.00 + $45,730.00 = $1,260,390.00

================================================================================
4. ROLLING 90-DAY DS2-TO-WON RATE
================================================================================
- 90-Day Rolling Window: 2026-06-07 to 2026-09-05
- Deals entering DS2 during this window: 111 deals
  * Won: 8 deals (Deal-A1C3E5, Deal-F2C7D8, Deal-B7D2F4, Deal-C9E1A6, Deal-A8B4D6, Deal-D4B8C2, Deal-E6F3A9, Deal-C5D9E2)
  * Lost: 27 deals (all 27 closed lost deals entered DS2 between 2026-06-12 and 2026-08-08)
  * Open (in progress): 76 deals

Arithmetic:
  * Cohort Conversion Rate (Won / Total entered DS2 in window):
    8 / 111 = 7.21%
  * Closed Outcome Win Rate from DS2 Cohort (Won / [Won + Lost]):
    8 / (8 + 27) = 8 / 35 = 22.86%
  (Note: Deal-B3E6F1 closed on 2026-06-20 within 90 days, but entered DS2 on 2026-05-10, 118 days prior, and is excluded per the pre-quarter rule).

================================================================================
5. WIN AND LOSS COUNTS WITH TOP LOSS REASON
================================================================================
- Win Count (QTD): 8 deals ($150,000.00)
- Loss Count (QTD): 27 deals ($330,272.00)
  (All 27 closed lost deals occurred between 2026-07-29 and 2026-09-02)

Breakdown of Loss Reasons:
  * Lost- Timing (1 year or more): 13 deals (48.15%) | $184,681.00
  * Competitor: 5 deals (18.52%) | $49,020.00
  * MIA: 5 deals (18.52%) | $45,831.00
  * Lost DM: 2 deals (7.41%) | $17,940.00
  * Feature Request: 1 deal (3.70%) | $21,000.00
  * Lost- Does not fit ICP (write in notes): 1 deal (3.70%) | $10,800.00

Top Loss Reason:
  "Lost- Timing (1 year or more)" — 13 deals (48.15% of all losses, representing $184,681.00).

================================================================================
6. ACTIVITY VOLUME BY TYPE (LAST 30 DAYS)
================================================================================
Source: ae_engagements.csv (across 161 total deals)
- Emails: 807
- Meetings: 128
- Calls: 112
- Notes: 50

Arithmetic:
  Total Activities (last 30 days) = 807 + 128 + 112 + 50 = 1,097 activities
  Email share of activity: 807 / 1,097 = 73.56%
  Call share of activity: 112 / 1,097 = 10.21%
  Meeting share of activity: 128 / 1,097 = 11.67%
  Notes share of activity: 50 / 1,097 = 4.56%

================================================================================
COACHING OBSERVATIONS GROUNDED IN THE NUMBERS
================================================================================
1. Pipeline Stagnation and Bottleneck at DS3:
   67 of 125 active deals (53.60%) and $552,705.00 of $1,260,390.00 active pipeline (43.85%) are concentrated in DS3. In contrast, late-stage pipeline is sparse, with only 5 deals in DS4 ($23,574.00) and 5 deals in DS5 ($45,730.00). Alex needs coaching on qualification gates and exit criteria to advance opportunities out of DS3 or disqualify stalled deals earlier.

2. Heavy Losses to Timing and Slippage:
   "Lost- Timing (1 year or more)" accounts for nearly half of all lost deals (13 of 27, or 48.15%) and $184,681.00 in lost value, supplemented by 5 deals ($45,831.00) marked "MIA". This indicates prospects are entering discovery and evaluation without an established compelling event, verified budget, or urgent timeline. Alex should focus on uncovering business pain and cost of inaction during early discovery stages (DS1/DS2).

3. Communication Channel Skew Towards Email Over Phone:
   Activity is heavily skewed toward email (807 emails vs. 112 calls and 128 meetings), representing a 7.2:1 email-to-call ratio. With 5 deals lost to "MIA" ($45,831.00) and 2 deals lost to "Lost DM" ($17,940.00), Alex's reliance on asynchronous email outreach rather than live multi-threaded phone calls and executive touchpoints increases the risk of deals going cold or losing contact with key stakeholders.
multithreading-gapPASS 1.00

? The Question

Find every open deal that is single-threaded (fewer than 2 active contacts) or under-threaded (fewer than 3, or all contacts in one persona). Active means engaged in the last 60 days and not marked former. Personas: economic buyer, champion, HR admin, IT security, finance. For each flagged deal: amount, stage, active contact count, personas present, personas missing, the most valuable persona to add given the stage, and any on-file unengaged contact at that company who fits; otherwise say none on file.
cost $0.2080 · 199s · in 142,909 / out 4,453 tok
✓ flagged_recall✓ persona_language✓ single_thread_term✓ on_file_fit

✓ Correct Answer (ground truth)

active_window_start: 2026-07-07
persona_universe: economic buyer, champion, HR admin, IT security, finance
flag_rule: flag if active_contacts < 2 (single-threaded) or active_contacts < 3 or all active contacts share one persona (under-threaded); active = engaged within 60 days and not former
best_persona_rule: DS2/DS3: economic buyer > IT security > finance > HR admin > champion; DS4/DS5: economic buyer > finance > IT security > HR admin > champion; first persona not present among active contacts
flagged: deal_alias=Deal-36C33F; amount=15000; stage=DS2; active_count=1; reason=single-threaded; best_persona_to_add=economic buyer | deal_alias=Deal-50D386; amount=36000; stage=DS2; active_count=2; reason=under-threaded; best_persona_to_add=economic buyer | deal_alias=Deal-5408B0; amount=14850; stage=DS4; active_count=2; reason=under-threaded; best_persona_to_add=economic buyer | deal_alias=Deal-5BFE3B; amount=18000; stage=DS2; active_count=2; reason=under-threaded; best_persona_to_add=economic buyer | deal_alias=Deal-885F45; amount=9300; stage=DS2; active_count=2; reason=under-threaded; best_persona_to_add=IT security | deal_alias=Deal-92D97D; amount=60000; stage=DS2; active_count=1; reason=single-threaded; best_persona_to_add=economic buyer … (+5 more)
healthy_not_flagged: Deal-4B0BEB, Deal-84DBA6, Deal-D348E1
allowed_new: 59 items — e.g. C-077A0E, C-0D15DF, C-1BDFE0, C-2AE3AA
graded checks: flagged_recall · persona_language · single_thread_term · on_file_fit

Model's actual answer

show the model's full answer
An analysis of the provided data (`deal_contacts.csv` and `unengaged_contacts.csv`) evaluated against the threading rules is presented below.

---

### Data Note & Rules

1. **Missing Data**:
   - `amount`: **Not provided in either file.** (Explicitly noted as missing per prompt instructions).
   - `stage`: **Not provided in either file.** (Explicitly noted as missing per prompt instructions).
   - Because `stage` is missing from the data, the *most valuable persona to add* is determined by general hierarchy and existing coverage gaps:
     - **economic buyer** is prioritized first whenever absent (essential for budget/signing authority).
     - Where both **economic buyer** and **champion** are present, technical/procurement blockers (**IT security**, **finance**, or **HR admin**) take priority.

2. **Active Contact Definition & Arithmetic**:
   - Reference date: latest engagement in the dataset is `2026-09-03`.
   - 60-day active window: Engaged on or after `2026-07-05` (`2026-09-03` minus 60 days) and `is_former = false`.
   - Persona universe: `economic buyer`, `champion`, `HR admin`, `IT security`, `finance`.

3. **Threading Criteria**:
   - **Single-threaded**: Fewer than 2 active contacts (`active_contacts < 2`).
   - **Under-threaded**: Fewer than 3 active contacts (`active_contacts < 3`), or all active contacts belong to a single persona.
   - Total deals in dataset: 14 unique deals.
   - Flagged deals: 11 (5 single-threaded, 6 under-threaded).
   - Fully threaded / not flagged: 3 (`Deal-84DBA6` [3 contacts, 3 personas], `Deal-4B0BEB` [4 contacts, 4 personas], `Deal-D348E1` [5 contacts, 5 personas]).

---

### Flagged Deals

#### 1. Deal-EC3025 (C-FDD0C7)
- **Amount**: Missing (not provided in data)
- **Stage**: Missing (not provided in data)
- **Active Contact Count**: 1 (Single-threaded)
  - Active: `CT-047C54` (champion, last engaged 2026-09-02, `is_former=false`)
  - Excluded: `CT-F2C1AE` (economic buyer, last engaged 2026-08-15, `is_former=true` — former employee)
  - Arithmetic: 2 total contacts − 1 former = 1 active contact
- **Personas Present**: champion
- **Personas Missing**: economic buyer, HR admin, IT security, finance
- **Most Valuable Persona to Add**: economic buyer (replaces lost former economic buyer and secures budget authority)
- **On-File Unengaged Contact**: `CT-6827DB` (Chief People Officer, economic buyer) — fits

---

#### 2. Deal-92D97D (C-E23238)
- **Amount**: Missing (not provided in data)
- **Stage**: Missing (not provided in data)
- **Active Contact Count**: 1 (Single-threaded)
  - Active: `CT-01F5B4` (HR admin, last engaged 2026-08-28, `is_former=false`)
  - Excluded: `CT-A902AE` (champion, last engaged 2026-06-01: 94 days ago > 60-day window)
  - Arithmetic: 2 total contacts − 1 inactive (>60 days) = 1 active contact
- **Personas Present**: HR admin
- **Personas Missing**: champion, economic buyer, IT security, finance
- **Most Valuable Persona to Add**: economic buyer (or re-engaging champion)
- **On-File Unengaged Contact**: None on file

---

#### 3. Deal-36C33F (C-077A0E)
- **Amount**: Missing (not provided in data)
- **Stage**: Missing (not provided in data)
- **Active Contact Count**: 1 (Single-threaded)
  - Active: `CT-4FE556` (IT security, last engaged 2026-08-15, `is_former=false`)
  - Excluded: `CT-405B45` (champion, `is_former=true`), `CT-86B22F` (economic buyer, `is_former=true`)
  - Arithmetic: 3 total contacts − 2 former = 1 active contact
- **Personas Present**: IT security
- **Personas Missing**: champion, economic buyer, HR admin, finance
- **Most Valuable Persona to Add**: economic buyer (lost both champion and economic buyer to turnover; need buyer authority)
- **On-File Unengaged Contact**: `CT-1DB73E` (Chief People Officer, economic buyer) — fits

---

#### 4. Deal-FCBE5B (C-737030)
- **Amount**: Missing (not provided in data)
- **Stage**: Missing (not provided in data)
- **Active Contact Count**: 1 (Single-threaded)
  - Active: `CT-4A5317` (champion, last engaged 2026-08-29, `is_former=false`)
  - Arithmetic: 1 total contact = 1 active contact
- **Personas Present**: champion
- **Personas Missing**: economic buyer, HR admin, IT security, finance
- **Most Valuable Persona to Add**: economic buyer (critical for budget approval)
- **On-File Unengaged Contact**: None on file

---

#### 5. Deal-F9A08A (C-0D15DF)
- **Amount**: Missing (not provided in data)
- **Stage**: Missing (not provided in data)
- **Active Contact Count**: 1 (Single-threaded)
  - Active: `CT-931B10` (champion, last engaged 2026-09-03, `is_former=false`)
  - Excluded: `CT-913581` (economic buyer, last engaged 2026-06-20: 75 days ago > 60-day window)
  - Arithmetic: 2 total contacts − 1 inactive (>60 days) = 1 active contact
- **Personas Present**: champion
- **Personas Missing**: economic buyer, HR admin, IT security, finance
- **Most Valuable Persona to Add**: economic buyer (current buyer unengaged/cold)
- **On-File Unengaged Contact**: `CT-697541` (Chief People Officer, economic buyer) — fits

---

#### 6. Deal-50D386 (C-EB10E4)
- **Amount**: Missing (not provided in data)
- **Stage**: Missing (not provided in data)
- **Active Contact Count**: 2 (Under-threaded: fewer than 3 active contacts)
  - Active: `CT-AA41B2` (champion, last engaged 2026-09-01), `CT-B9C35B` (HR admin, last engaged 2026-08-25)
  - Arithmetic: 2 active contacts (< 3)
- **Personas Present**: champion, HR admin
- **Personas Missing**: economic buyer, IT security, finance
- **Most Valuable Persona to Add**: economic buyer (needed for commercial sign-off)
- **On-File Unengaged Contact**: `CT-A1C4B3` (Chief People Officer, economic buyer) — fits

---

#### 7. Deal-D0D6B5 (C-32918E)
- **Amount**: Missing (not provided in data)
- **Stage**: Missing (not provided in data)
- **Active Contact Count**: 3 (Under-threaded: all contacts in one persona)
  - Active: `CT-87CED4` (champion, 2026-09-02), `CT-DE6D7C` (champion, 2026-08-19), `CT-FD70B2` (champion, 2026-08-07)
  - Arithmetic: 3 active contacts, but 3/3 belong to `champion` (1 unique persona)
- **Personas Present**: champion
- **Personas Missing**: economic buyer, HR admin, IT security, finance
- **Most Valuable Persona to Add**: economic buyer (resolve single-persona concentration and add decision authority)
- **On-File Unengaged Contact**: `CT-1FA4DB` (Chief People Officer, economic buyer) — fits

---

#### 8. Deal-5BFE3B (C-535D36)
- **Amount**: Missing (not provided in data)
- **Stage**: Missing (not provided in data)
- **Active Contact Count**: 2 (Under-threaded: fewer than 3 active contacts, and all contacts in one persona)
  - Active: `CT-57123B` (champion, 2026-08-31), `CT-5CE757` (champion, 2026-08-12)
  - Arithmetic: 2 active contacts (< 3), both belong to `champion` (1 unique persona)
- **Personas Present**: champion
- **Personas Missing**: economic buyer, HR admin, IT security, finance
- **Most Valuable Persona to Add**: economic buyer
- **On-File Unengaged Contact**: None on file

---

#### 9. Deal-885F45 (C-5E8EFB)
- **Amount**: Missing (not provided in data)
- **Stage**: Missing (not provided in data)
- **Active Contact Count**: 2 (Under-threaded: fewer than 3 active contacts)
  - Active: `CT-51C81E` (economic buyer, 2026-08-26), `CT-D9A0E8` (champion, 2026-08-11)
  - Arithmetic: 2 active contacts (< 3)
- **Personas Present**: economic buyer, champion
- **Personas Missing**: HR admin, IT security, finance
- **Most Valuable Persona to Add**: IT security (economic buyer and champion already secured; IT security prevents procurement blockers)
- **On-File Unengaged Contact**: `CT-B3F25D` (IT Security Lead, IT security) — fits

---

#### 10. Deal-5408B0 (C-2AE3AA)
- **Amount**: Missing (not provided in data)
- **Stage**: Missing (not provided in data)
- **Active Contact Count**: 2 (Under-threaded: fewer than 3 active contacts)
  - Active: `CT-D33AE4` (champion, 2026-09-01), `CT-8742FD` (HR admin, 2026-08-18)
  - Arithmetic: 2 active contacts (< 3)
- **Personas Present**: champion, HR admin
- **Personas Missing**: economic buyer, IT security, finance
- **Most Valuable Persona to Add**: economic buyer
- **On-File Unengaged Contact**: `CT-07FA76` (Chief People Officer, economic buyer) — fits

---

#### 11. Deal-C6D97A (C-5A8FC2)
- **Amount**: Missing (not provided in data)
- **Stage**: Missing (not provided in data)
- **Active Contact Count**: 3 (Under-threaded: all contacts in one persona)
  - Active: `CT-223DDC` (champion, 2026-08-31), `CT-B03555` (champion, 2026-08-20), `CT-4E8A2B` (champion, 2026-08-05)
  - Arithmetic: 3 active contacts, but 3/3 belong to `champion` (1 unique persona)
- **Personas Present**: champion
- **Personas Missing**: economic buyer, HR admin, IT security, finance
- **Most Valuable Persona to Add**: economic buyer
- **On-File Unengaged Contact**: None on file
talk-track-from-transcriptsPASS 1.00

? The Question

From this rep's last 10 calls: what they lead with in the first five minutes, how they handle the three most common objections, how often a concrete next step is agreed (rate), and every competitor a prospect raised. Quote at most one line per finding and only lines that appear in the transcripts. End with two coaching notes.
cost $0.0444 · 55s · in 18,424 / out 8,158 tok
✓ objection_count✓ next_step_rate✓ competitors✓ coaching✓ quote_used

✓ Correct Answer (ground truth)

rep: Alex Franklin
n_transcripts: 10
opener_theme: theme=opens with a customer recognition win story (400-person retailer, 18% turnover reduction); count=8; total=10; quote=Quick one to kick off: a 400-person retailer we work with cut regretted turnover 18% in two quarters after automating milestone awards, and I wanted to walk you through exactly how they did it.; transcript_id=TT-001
objections: objection=budget; count=4; quote=Honestly, budget is locked until next fiscal year — I can't add a new line item right now.; transcript_id=TT-001 | objection=timing; count=3; quote=This is good, but can we revisit it next quarter? Open enrollment starts in October and we're underwater.; transcript_id=TT-002 | objection=in-house; count=3; quote=We already do recognition with a spreadsheet and quarterly gift cards — why would we change?; transcript_id=TT-004
objection_count: 3
next_step: agreed=7; total=10; rate=0.7
competitors_prospect_raised: Awardco, Kudos
competitor_quotes: Awardco=We're also in late talks with Awardco — their rewards catalog looks bigger than yours.; Kudos=How are you different from Kudos? Our CEO used them at her last company.
rep_raised_competitors_excluded: Workhuman
allowed_new: 13 items — e.g. TT-001, TT-002, TT-003, TT-004
graded checks: objection_count · next_step_rate · competitors · coaching · quote_used

Model's actual answer

show the model's full answer
CALL ANALYSIS REPORT: ALEX FRANKLIN (LAST 10 CALLS)

1. WHAT THE REP LEADS WITH IN THE FIRST FIVE MINUTES
In 8 of the 10 calls (80.0%, 8/10: Deal-D348E1, Deal-5408B0, Deal-547B2B, Deal-C61CF7, Deal-60C2C2, Deal-EDC141, Deal-D9A12F, Deal-84DBA6), the rep opens at minute 0 with a case study detailing a 400-person retailer that reduced regretted turnover by 18% through automated milestone awards. In the remaining 2 calls (20.0%, 2/10), the rep opened with an agenda covering security/pricing in Deal-403845 and straight pricing in Deal-1E2498.
Quote: "Quick one to kick off: a 400-person retailer we work with cut regretted turnover 18% in two quarters after automating milestone awards, and I wanted to walk you through exactly how they did it."

2. HOW THE REP HANDLES THE THREE MOST COMMON OBJECTIONS
Across the 10 calls, the three most frequent objections raised by prospects are:

a. Budget locked until next fiscal year (4 occurrences / 40.0% of calls: Deal-D348E1, Deal-547B2B, Deal-60C2C2, Deal-84DBA6).
Handling: The rep validates the objection and reframes the cost around turnover savings, stating the retailer in the case study saved $210k in avoided backfills to secure finance approval.
Quote: "Totally fair. Most teams fund this out of turnover savings — that retailer saved about $210k in avoided backfills, which is how their finance team signed off."

b. Revisit next quarter / timing constraints due to open enrollment (3 occurrences / 30.0% of calls: Deal-5408B0, Deal-C61CF7, Deal-D9A12F).
Handling: The rep acknowledges the timing and suggests scoping a 90-day pilot with a single department to gather internal data prior to quarterly planning.
Quote: "Makes sense. What if we scope a 90-day pilot with one department so you have internal data before next quarter's planning?"

c. Status quo / existing spreadsheet and gift card process (3 occurrences / 30.0% of calls: Deal-403845, Deal-EDC141, Deal-1E2498).
Handling: The rep explains that spreadsheets do not scale and emphasizes milestone automation without HR effort, combined with recognition analytics.
Quote: "Spreadsheets work until they scale — the difference is automation: milestones fire without HR lifting a finger, and you get analytics on who is being recognized."

3. CONCRETE NEXT STEP AGREEMENT RATE
Arithmetic:
Calls with agreed concrete next step: 7 (Deal-D348E1, Deal-5408B0, Deal-547B2B, Deal-C61CF7, Deal-60C2C2, Deal-D9A12F, Deal-1E2498).
Calls without agreed next step: 3 (Deal-403845, Deal-EDC141, Deal-84DBA6).
Total calls analyzed: 10
Agreement rate: 7 / 10 = 70.0%
Quote: "Yes, Thursday at 2pm works — send the invite and I'll bring our HRIS manager."

4. EVERY COMPETITOR A PROSPECT RAISED
Across the 10 calls, prospects raised exactly 2 competitors:
- Awardco: Raised by the prospect in Deal-547B2B (call TT-003, minute 4).
Quote: "We're also in late talks with Awardco — their rewards catalog looks bigger than yours."
- Kudos: Raised by the prospect in Deal-EDC141 (call TT-007, minute 4).
Quote: "How are you different from Kudos? Our CEO used them at her last company."

Note: Workhuman appeared in Deal-C61CF7 (call TT-005, minute 2), but it was introduced unprompted by Alex Franklin, not raised by a prospect.

COACHING NOTES
1. Do not concede passively when prospects defer to committees or claim a lack of urgency. In Deal-403845, Deal-EDC141, and Deal-84DBA6, the rep immediately walked away (saying "Understood — I'll leave it with you", "Fair enough", and "Understood, thanks for the candor"). Instead, offer to co-create a one-page business case with the prospect or offer to participate directly in the committee review.
2. Avoid introducing competitor names unprompted. In Deal-C61CF7, the rep proactively brought up Workhuman at minute 2 before the prospect expressed any competitive interest. Addressing competitors before the prospect mentions them introduces unnecessary alternatives into the evaluation.
reporting-analytics
q3-weighted-forecastPASS 1.00

? The Question

Below are the open deals in the default sales pipeline with a close date between 2026-07-01 and 2026-10-15, pulled from the CRM warehouse on 2026-09-05. Stages: DS1 (discovery) through DS5 (contract). Build the Q3 2026 forecast (quarter = 2026-07-01 to 2026-09-30). Weighting rule: 100 percent of COMMIT plus 35 percent of BEST_CASE. PIPELINE deals count zero. Only deals with a close date inside the quarter count. Report: COMMIT total, BEST_CASE total, weighted forecast, count of deals in each category inside the quarter, the deals you excluded for being outside the quarter (count and total amount), and the top 5 BEST_CASE deals by amount inside the quarter. Show arithmetic. Then, in a final '## Data quality' section of no more than four sentences, name the data quality problems in this extract that would make the forecast unreliable if it were run unattended.
cost $0.0479 · 43s · in 27,801 / out 6,820 tok
✓ commit_total_44729✓ best_case_total_203565✓ weighted_115976.75✓ commit_count_7✓ best_case_count_24✓ excluded_count_32✓ excluded_amount_227575✓ top5_aliases✓ flags_blank_owner✓ flags_missing_why_buys

✓ Correct Answer (ground truth)

graded checks: commit_total_44729 · best_case_total_203565 · weighted_115976.75 · commit_count_7 · best_case_count_24 · excluded_count_32 · excluded_amount_227575 · top5_aliases · flags_blank_owner · flags_missing_why_buys

Model's actual answer

show the model's full answer
Q3 2026 Sales Forecast (2026-07-01 to 2026-09-30)

Deals Inside Q3 by Category:
- COMMIT: 7 deals
- BEST_CASE: 24 deals
- PIPELINE: 23 deals
- Total Deals Inside Q3: 54 deals

Category Totals Inside Q3:
- COMMIT Total: $44,729.00
  Arithmetic: 11,200 (Deal-547B2B) + 9,000 (Deal-B7EBD1) + 9,000 (Deal-403845) + 6,360 (Deal-A2B47C) + 5,400 (Deal-2465CE) + 2,520 (Deal-A5E80A) + 1,249 (Deal-499BF6) = $44,729.00

- BEST_CASE Total: $203,565.00
  Arithmetic: 38,935 (Deal-2D7423) + 24,000 (Deal-25F752) + 19,656 (Deal-E53952) + 16,250 (Deal-5EED42) + 11,116 (Deal-FA32A0) + 10,800 (Deal-FC22A3) + 10,500 (Deal-944310) + 9,890 (Deal-5195DB) + 9,720 (Deal-180D02) + 9,000 (Deal-3974EB) + 7,200 (Deal-5D8CEE) + 3,840 (Deal-9D0060) + 3,780 (Deal-46988D) + 3,600 (Deal-357C30) + 3,240 (Deal-C6D97A) + 3,150 (Deal-DAF1D9) + 3,120 (Deal-EE195F) + 3,060 (Deal-55164C) + 2,916 (Deal-001FF4) + 2,760 (Deal-7B3B0F) + 2,484 (Deal-F9A08A) + 2,100 (Deal-8952F0) + 1,920 (Deal-1FC049) + 528 (Deal-87412C) = $203,565.00

- PIPELINE Total (counts zero): $201,637.40 (23 deals)

Weighted Forecast:
- Rule: (1.00 * COMMIT) + (0.35 * BEST_CASE) + (0.00 * PIPELINE)
- Arithmetic: (1.00 * 44,729.00) + (0.35 * 203,565.00) = 44,729.00 + 71,247.75 = $115,976.75

Deals Excluded (Close Date Outside Q3):
- Excluded Deal Count: 32 deals (dates between 2026-10-01 and 2026-10-15)
- Excluded Total Amount: $227,575.00

Top 5 BEST_CASE Deals Inside Q3 by Amount:
1. Deal-2D7423: $38,935.00
2. Deal-25F752: $24,000.00
3. Deal-E53952: $19,656.00
4. Deal-5EED42: $16,250.00
5. Deal-FA32A0: $11,116.00

## Data quality
The extract suffers from pervasive missing ownership, with 85 of the 86 deal records lacking an assigned rep to verify or drive next steps. Stage and forecast category alignments are severely mismatched, notably early-stage deals Deal-A5E80A (DS1) and Deal-499BF6 (DS2) marked as COMMIT, while late-stage deals such as Deal-6787C2 (DS4) remain in PIPELINE. Qualification narrative is almost entirely absent, as 71 out of 86 deals report zero why_buys characters. Additionally, extreme close-date clustering occurs on the final day of the quarter (17 deals placed on 2026-09-30), signaling artificial deadline placeholders rather than verified buyer timelines.
aha-moment-2x2PASS 1.00

? The Question

Test the activation hypothesis on the company cohort below: companies with 5+ unique givers (m1_users >= 5) AND 1+ successful redemption (m1_redemptions >= 1) in their first calendar month retain better at 24 months than those with one signal or neither. Every company in this extract is already 25+ months old, so current_status = 'active' means retained at 24 months. Report the full 2x2 (cohort size and 24-month retention rate for: both signals, givers-only, redemption-only, neither), state how many companies were excluded from the denominator and why (if none, say so), name the single signal with the largest retention lift, and state what this does and does not prove.
cost $0.1086 · 84s · in 58,865 / out 14,598 tok
✓ both_66✓ givers_47✓ redemp_31✓ neither_40✓ excluded_0✓ largest_lift_givers

✓ Correct Answer (ground truth)

graded checks: both_66 · givers_47 · redemp_31 · neither_40 · excluded_0 · largest_lift_givers

Model's actual answer

show the model's full answer
### Exclusions from Denominator
- **Companies excluded:** 0 (None).
- **Reason:** All 220 records in the extract are 25+ months old with complete data for `current_status`, `m1_users`, and `m1_redemptions`. Per instructions, all 220 companies are included in the denominator. Non-renewing accounts (`non_renewing`) are treated as not retained, as only `current_status = 'active'` denotes 24-month retention.

---

### 2x2 Activation Matrix (24-Month Retention)

1. **Both Signals (m1_users >= 5 AND m1_redemptions >= 1)**
   - **Cohort Size:** 47 companies
   - **Retained (active):** 31 companies
   - **Arithmetic:** 31 / 47
   - **24-Month Retention Rate:** 65.96%

2. **Givers-Only (m1_users >= 5 AND m1_redemptions < 1)**
   - **Cohort Size:** 49 companies
   - **Retained (active):** 23 companies
   - **Arithmetic:** 23 / 49
   - **24-Month Retention Rate:** 46.94%

3. **Redemption-Only (m1_users < 5 AND m1_redemptions >= 1)**
   - **Cohort Size:** 29 companies
   - **Retained (active):** 9 companies
   - **Arithmetic:** 9 / 29
   - **24-Month Retention Rate:** 31.03%

4. **Neither Signal (m1_users < 5 AND m1_redemptions < 1)**
   - **Cohort Size:** 95 companies
   - **Retained (active):** 38 companies
   - **Arithmetic:** 38 / 95
   - **24-Month Retention Rate:** 40.00%

*Cohort total:* 47 + 49 + 29 + 95 = 220 companies.

---

### Single Signal with Largest Retention Lift
- **Largest Lift Signal:** **5+ unique givers (`m1_users >= 5`)**
  - **Lift vs. Neither Baseline (40.00%):** 
    - Givers-only retention is 46.94% (+6.94 percentage points lift: `46.94% - 40.00%`).
    - In contrast, redemption-only retention is 31.03% (-8.97 percentage points lift: `31.03% - 40.00%`).
  - **Marginal / Independent Single Signal Lift:**
    - All companies with `m1_users >= 5`: (31 + 23) / (47 + 49) = 54 / 96 = 56.25% retention.
    - All companies with `m1_users < 5`: (9 + 38) / (29 + 95) = 47 / 124 = 37.90% retention.
    - Lift: `56.25% - 37.90%` = **+18.35 percentage points**.
    - *(Compared to 1+ redemptions overall: 40 / 76 = 52.63% vs. 61 / 144 = 42.36%, a lift of +10.27 percentage points).*

---

### What This Does and Does Not Prove

- **What it DOES prove:**
  - In this historical cohort of 220 companies, meeting both signals (5+ givers and 1+ redemptions) has an empirical correlation with higher 24-month retention (65.96%) compared to givers-only (46.94%), redemption-only (31.03%), or neither (40.00%).
  - The activation hypothesis holds observationally for this specific dataset: having both signals yields higher retention than having either single signal or neither.

- **What it DOES NOT prove:**
  - **Causality:** It does not prove that artificially driving a 5th giver or 1st redemption causes long-term retention.
  - **Confounding variables:** It does not control for underlying firmographic factors (e.g., company employee size, product tier, Slack/HRIS integrations, contract terms) that could drive both early adoption and long-term renewal independently.
  - **Mechanism for redemptions alone:** It does not show that redemptions independently benefit retention in small user pools—redemption without sufficient givers actually correlates with the lowest retention in the cohort (31.03%).
arr-rollforward-reconciliationPASS 1.00

? The Question

Reconcile ARR between CRM company records and billing subscriptions as of 2026-09-05. Billing ARR is MRR x 12. Report both totals, the variance, and a decomposition into buckets (status mismatch, rounding, missing records, other) that sums exactly to the variance. List each mismatched account with a suggested owner. Business rule: any subscription with a term other than 12 months must have cf_agreement_end_date populated; list violations.
cost $0.0801 · 54s · in 56,689 / out 8,011 tok
✓ billing_total✓ crm_total✓ variance✓ buckets_sum✓ violations_listed

✓ Correct Answer (ground truth)

as_of: 2026-09-05
billing_arr_total: 604739.28
crm_arr_total: 603581.76
variance: 1157.52
variance_definition: billing_arr_total - crm_arr_total (billing ARR = sum of MRR x 12 over status=active subscriptions)
buckets: {"status_mismatch": {"amount": "-13158.48", "accounts": [{"company_alias": "C-0C8323BF", "billing_arr": "0.00", "hubspot_arr": "4905.24", "diff": "-4905.24", "suggested_owner": "Dana Mercer"}, {"compa
bucket_sum_equals_variance: True
n_mismatched_accounts: 7
rule: term_months != 12 requires cf_agreement_end_date
violations: subscription_id=SUB-0002; company_alias=C-1794A52C; term_months=24 | subscription_id=SUB-0019; company_alias=C-22170CA1; term_months=36
n_companies: 40
graded checks: billing_total · crm_total · variance · buckets_sum · violations_listed

Model's actual answer

show the model's full answer
### ARR Reconciliation as of 2026-09-05

#### 1. Totals & Variance
* CRM (HubSpot) Total ARR: $603,581.76
* Billing (Chargebee) Total ARR (Active Subscriptions): $604,739.28
  *(Total Billing ARR across all 39 subscriptions including cancelled: $617,897.76)*
* Variance (CRM ARR - Active Billing ARR): -$1,157.52

Arithmetic:
$603,581.76 - $604,739.28 = -$1,157.52

---

#### 2. Variance Decomposition
Variance is decomposed into the requested buckets and sums exactly to -$1,157.52:

* Status mismatch: +$13,158.48
  * C-0C8323BF: +$4,905.24 ($4,905.24 CRM ARR - $0.00 Active Billing ARR; cancelled in Chargebee SUB-000E)
  * C-0DC4FB8C: +$8,253.24 ($8,253.24 CRM ARR - $0.00 Active Billing ARR; cancelled in Chargebee SUB-000F)
  * Arithmetic: $4,905.24 + $8,253.24 = $13,158.48

* Missing records: -$11,952.00
  * C-0D5BBE3A: +$16,497.24 ($16,497.24 CRM ARR - $0.00 Billing ARR; missing in Chargebee)
  * C-21629AA4: -$28,449.24 ($0.00 CRM ARR - $28,449.24 Billing ARR [SUB-0004: $2,370.77 x 12]; missing in CRM)
  * Arithmetic: $16,497.24 - $28,449.24 = -$11,952.00

* Rounding: $0.00
  * Arithmetic: $0.00

* Other (ARR amount discrepancies on active accounts): -$2,364.00
  * C-0D66DF9E: +$16.00 ($23,200.00 CRM ARR - $23,184.00 Billing ARR [SUB-0005: $1,932.00 x 12])
  * C-14D70CE0: +$20.00 ($18,200.00 CRM ARR - $18,180.00 Billing ARR [SUB-0008: $1,515.00 x 12])
  * C-0F7269D7: -$2,400.00 ($24,396.00 CRM ARR - $26,796.00 Billing ARR [SUB-0006: $2,233.00 x 12])
  * Arithmetic: $16.00 + $20.00 - $2,400.00 = -$2,364.00

Decomposition Sum:
$13,158.48 + (-$11,952.00) + $0.00 + (-$2,364.00) = -$1,157.52

---

#### 3. Mismatched Accounts & Suggested Owners

1. C-0C8323BF
   * CRM ARR: $4,905.24 | Active Billing ARR: $0.00 | Variance: +$4,905.24
   * Issue: Status mismatch (subscription SUB-000E is cancelled in Chargebee, but remains active ARR in CRM)
   * Suggested Owner: RevOps / Sales Ops (update CRM record to reflect cancellation/churn)

2. C-0DC4FB8C
   * CRM ARR: $8,253.24 | Active Billing ARR: $0.00 | Variance: +$8,253.24
   * Issue: Status mismatch (subscription SUB-000F is cancelled in Chargebee, but remains active ARR in CRM)
   * Suggested Owner: RevOps / Sales Ops (update CRM record to reflect cancellation/churn)

3. C-0D5BBE3A
   * CRM ARR: $16,497.24 | Active Billing ARR: $0.00 | Variance: +$16,497.24
   * Issue: Missing record in billing system (record exists in CRM with ARR but has no subscription in Chargebee)
   * Suggested Owner: Billing Ops / Finance (investigate contract and create subscription in Chargebee)

4. C-21629AA4
   * CRM ARR: $0.00 | Active Billing ARR: $28,449.24 | Variance: -$28,449.24
   * Issue: Missing record in CRM (subscription SUB-0004 is active in Chargebee but no company record exists in CRM)
   * Suggested Owner: Sales Ops / CRM Admin (create and map company record in CRM)

5. C-0D66DF9E
   * CRM ARR: $23,200.00 | Active Billing ARR: $23,184.00 | Variance: +$16.00
   * Issue: Contract amount discrepancy ($16.00 difference between CRM ARR and Chargebee MRR x 12)
   * Suggested Owner: Deal Desk / Billing Ops (audit signed order form to align ARR)

6. C-14D70CE0
   * CRM ARR: $18,200.00 | Active Billing ARR: $18,180.00 | Variance: +$20.00
   * Issue: Contract amount discrepancy ($20.00 difference between CRM ARR and Chargebee MRR x 12)
   * Suggested Owner: Deal Desk / Billing Ops (audit signed order form to align ARR)

7. C-0F7269D7
   * CRM ARR: $24,396.00 | Active Billing ARR: $26,796.00 | Variance: -$2,400.00
   * Issue: Pricing discrepancy ($24,396.00 in CRM implies $2,033.00 MRR vs. $2,233.00 MRR billed in Chargebee)
   * Suggested Owner: Deal Desk / Revenue Operations (audit contract amendments and billing schedule)

---

#### 4. Business Rule Violations
Rule: Any subscription with term_months != 12 must have cf_agreement_end_date populated.

Violations:
1. Subscription ID: SUB-0002
   * Company Alias: C-1794A52C
   * term_months: 24
   * cf_agreement_end_date: Missing (empty)

2. Subscription ID: SUB-0019
   * Company Alias: C-22170CA1
   * term_months: 36
   * cf_agreement_end_date: Missing (empty)
kvm-month-over-monthPASS 1.00

? The Question

Report the four core KVMs for 2026-08 against 2026-07: giving rate, redemptions per user, 1:1 meetings engagement, pulse check engagement. For each: value, prior value, absolute and relative change, direction. Name the KVM with the largest relative move and the segment (plan_tier or size_band) driving it if the data supports that; otherwise say the data does not. Under 200 words plus one table.
cost $0.1045 · 95s · in 70,638 / out 12,574 tok
✓ four_kvms✓ biggest_mover_named✓ direction_correct✓ segment_named✓ word_cap

✓ Correct Answer (ground truth)

aggregation: simple mean of company-level values per month
kvms: {"giving_rate": {"2026-07": 0.6023, "2026-08": 0.6027, "abs_change": 0.0004, "rel_change_pct": 0.07, "direction": "up"}, "redemptions_per_user": {"2026-07": 1.73, "2026-08": 1.7302, "abs_change": 0.00
biggest_mover: pulse_engagement
driving_segment: size_band=enterprise; 2026-07=0.55; 2026-08=0.2743; rel_change_pct=-50.13
segment_detail: {"smb": {"2026-07": 0.6588, "2026-08": 0.6573, "rel_change_pct": -0.23}, "mid_market": {"2026-07": 0.593, "2026-08": 0.5942, "rel_change_pct": 0.2}, "enterprise": {"2026-07": 0.55, "2026-08": 0.2743,
n_company_rows_per_month: 30
graded checks: four_kvms · biggest_mover_named · direction_correct · segment_named · word_cap

Model's actual answer

show the model's full answer
### Core KVM Performance: 2026-08 vs. 2026-07 (N = 30 companies)

| Core KVM | 2026-07 (Prior) | 2026-08 (Value) | Absolute Change | Relative Change | Direction |
| :--- | :--- | :--- | :--- | :--- | :--- |
| Giving Rate | 0.6023 | 0.6027 | +0.0004 | +0.07% | Up |
| Redemptions Per User | 1.7300 | 1.7302 | +0.0002 | +0.01% | Up |
| 1:1 Meetings Engagement | 0.4469 | 0.4472 | +0.0003 | +0.06% | Up |
| Pulse Check Engagement | 0.6006 | 0.5086 | -0.0920 | -15.31% | Down |

### Analysis & Arithmetic

Arithmetic (mean across 30 companies per month):
- Giving Rate: 18.0814/30 (0.6027) - 18.0689/30 (0.6023) = +0.0004 (+0.07%)
- Redemptions Per User: 51.9049/30 (1.7302) - 51.8995/30 (1.7300) = +0.0002 (+0.01%)
- 1:1 Meetings Engagement: 13.4153/30 (0.4472) - 13.4066/30 (0.4469) = +0.0003 (+0.06%)
- Pulse Check Engagement: 15.2583/30 (0.5086) - 18.0176/30 (0.6006) = -0.0920 (-15.31%)

Largest Relative Move:
The KVM with the largest relative move is pulse check engagement (-15.31%).

Segment Driver:
All companies belong to plan_tier `tier_three`, so plan tier does not explain variance. The move is entirely driven by size_band `enterprise` (10 companies), where pulse check engagement dropped -50.13% from 0.5500 to 0.2743 (absolute change: -0.2757). In contrast, `smb` moved -0.22% (0.6588 to 0.6573) and `mid_market` moved +0.21% (0.5930 to 0.5942).
rewards-ytd-sectionPASS 1.00

? The Question

Produce the redemption section of the weekly rewards report through the last completed month (2026-08): redemption count, spend, unique redeemers, redemptions per redeemer, provider mix as percent of spend, top 5 countries by redemptions. Say which month is the last completed month. Provider shares must sum to 100.
cost $0.1562 · 173s · in 72,970 / out 24,103 tok
✓ count✓ spend✓ unique_redeemers✓ month_named✓ provider_mix✓ top_country

✓ Correct Answer (ground truth)

redemption_count: 378
total_spend_usd: 27846.00
unique_redeemers: 235
redemptions_per_redeemer: 1.61
provider_mix_pct_of_spend: TangoCard=11.6; Tremendous=30.54; custom=39.05; Snappy=18.81
top5_countries_by_count: country=US; count=244 | country=CA; count=24 | country=AU; count=21 | country=GB; count=17 | country=NL; count=17
graded checks: count · spend · unique_redeemers · month_named · provider_mix · top_country

Model's actual answer

show the model's full answer
### Weekly Rewards Report: Redemption Section
Last completed month: August 2026 (2026-08)
Date range covered: 2026-01-01 through 2026-08-31

---

#### 1. Overall Metrics
* Total Redemptions: 378
* Total Spend: $27,846.00
* Unique Redeemers: 235 distinct user keys
* Redemptions per Redeemer: 1.61 (378 / 235 = 1.6085)

---

#### 2. Provider Mix (by Spend)
* Arithmetic:
  * custom: $10,873.00 / $27,846.00 = 39.05%
  * Tremendous: $8,505.00 / $27,846.00 = 30.54%
  * Snappy: $5,238.00 / $27,846.00 = 18.81%
  * TangoCard: $3,230.00 / $27,846.00 = 11.60%
* Total Share: 39.05% + 30.54% + 18.81% + 11.60% = 100.00%

---

#### 3. Top Countries by Redemptions
* 1. US: 244 redemptions ($18,547.00 spend)
* 2. CA: 24 redemptions ($2,286.00 spend)
* 3. AU: 21 redemptions ($1,606.00 spend)
* 4. GB: 17 redemptions ($944.00 spend) [tied for 4th]
* 4. NL: 17 redemptions ($1,122.00 spend) [tied for 4th]
customer-success
churn-save-eligibilityPASS 1.00

? The Question

Which at-risk accounts qualify for a churn-save offer under the documented eligibility rules, what amount is at stake per account and in total, and which play fits each (usage revival, executive touch, commercial concession)? Cite the signal that justifies each play. List accounts that look at risk but do not qualify and why.
cost $0.1027 · 140s · in 61,047 / out 10,374 tok
✓ eligible_set✓ total_at_stake✓ plays_cited✓ noneligible_named✓ rules_applied

✓ Correct Answer (ground truth)

snapshot_date: 2026-09-05
rules: health_score < 60, churn_save_eligible_amount > 0, renewal within 120 days of snapshot
eligible: account_alias=C-0F6C0F34; amount_at_stake=49707.00; play=executive touch; justifying_signal=champion_active is false - no executive sponsor engaged | account_alias=C-0B827671; amount_at_stake=25365.00; play=usage revival; justifying_signal=usage_trend_3m=declining over the last 3 months | account_alias=C-0B360C78; amount_at_stake=35748.00; play=commercial concession; justifying_signal=usage stable/growing with seat utilization 75% - risk is commercial, not adoption | account_alias=C-0B0F1BAB; amount_at_stake=5494.00; play=executive touch; justifying_signal=champion_active is false - no executive sponsor engaged | account_alias=C-0CA21961; amount_at_stake=16829.00; play=usage revival; justifying_signal=seat utilization 26% is below 50% | account_alias=C-0E9C27D1; amount_at_stake=41235.00; play=commercial concession; justifying_signal=usage stable/growing with seat utilization 85% - risk is commercial, not adoption … (+2 more)
total_amount_at_stake: 224601.00
non_eligible_at_risk: account_alias=C-0BC71BDD; health_score=55 | account_alias=C-0BA71F12; health_score=52 | account_alias=C-0F6694C3; health_score=43 | account_alias=C-0BE96399; health_score=54 | account_alias=C-0F876796; health_score=47 | account_alias=C-0FCCD2DF; health_score=43 … (+1 more)
n_accounts: 30
graded checks: eligible_set · total_at_stake · plays_cited · noneligible_named · rules_applied

Model's actual answer

show the model's full answer
### Documented Eligibility Rules
To qualify for a churn-save offer, an account must satisfy all three criteria relative to the snapshot date of 2026-09-05:
1. Rule R1: `health_score` < 60 (at risk)
2. Rule R2: `churn_save_eligible_amount` > 0
3. Rule R3: `renewal_date` within 120 days of snapshot date (2026-09-05 to 2027-01-03)

---

### Qualified At-Risk Accounts

1. C-0F6C0F34
- Health Score: 51 (< 60)
- Churn-Save Eligible Amount: $49,707.00 (> 0)
- Renewal Date: 2026-10-03 (28 days from snapshot, within 120 days)
- Recommended Play: Executive touch
- Justifying Signal: `champion_active` is false (no active executive sponsor/champion engaged).

2. C-0B827671
- Health Score: 56 (< 60)
- Churn-Save Eligible Amount: $25,365.00 (> 0)
- Renewal Date: 2026-11-14 (70 days from snapshot, within 120 days)
- Recommended Play: Usage revival
- Justifying Signal: `usage_trend_3m` is declining over the last 3 months (seat utilization is 113 / 202 = 55.9%).

3. C-0B360C78
- Health Score: 57 (< 60)
- Churn-Save Eligible Amount: $35,748.00 (> 0)
- Renewal Date: 2026-10-28 (53 days from snapshot, within 120 days)
- Recommended Play: Commercial concession
- Justifying Signal: `usage_trend_3m` is growing, `champion_active` is true, and seat utilization is strong at 246 / 327 = 75.2% (risk is commercial/budget, not adoption).

4. C-0B0F1BAB
- Health Score: 38 (< 60)
- Churn-Save Eligible Amount: $5,494.00 (> 0)
- Renewal Date: 2026-09-23 (18 days from snapshot, within 120 days)
- Recommended Play: Executive touch
- Justifying Signal: `champion_active` is false (no active executive sponsor/champion engaged).

5. C-0CA21961
- Health Score: 58 (< 60)
- Churn-Save Eligible Amount: $16,829.00 (> 0)
- Renewal Date: 2026-12-28 (114 days from snapshot, within 120 days)
- Recommended Play: Usage revival
- Justifying Signal: Low seat utilization at 84 / 325 = 25.8% (below 50%) with `usage_trend_3m` flat.

6. C-0E9C27D1
- Health Score: 39 (< 60)
- Churn-Save Eligible Amount: $41,235.00 (> 0)
- Renewal Date: 2026-09-24 (19 days from snapshot, within 120 days)
- Recommended Play: Commercial concession
- Justifying Signal: `usage_trend_3m` is flat, `champion_active` is true, and seat utilization is high at 134 / 157 = 85.4% (healthy adoption indicates risk is commercial).

7. C-0CEF69FD
- Health Score: 53 (< 60)
- Churn-Save Eligible Amount: $32,621.00 (> 0)
- Renewal Date: 2026-11-21 (77 days from snapshot, within 120 days)
- Recommended Play: Executive touch
- Justifying Signal: `champion_active` is false (no active executive sponsor/champion engaged).

8. C-0D3278C7
- Health Score: 54 (< 60)
- Churn-Save Eligible Amount: $17,602.00 (> 0)
- Renewal Date: 2026-11-12 (68 days from snapshot, within 120 days)
- Recommended Play: Usage revival
- Justifying Signal: `usage_trend_3m` is declining over the last 3 months and seat utilization is low at 126 / 380 = 33.2%.

---

### Total Amount at Stake (Arithmetic)

Total Churn-Save Eligible Amount at stake:
$49,707.00 + $25,365.00 + $35,748.00 + $5,494.00 + $16,829.00 + $41,235.00 + $32,621.00 + $17,602.00 = $224,601.00

---

### At-Risk Accounts That Do Not Qualify (Health Score < 60)

1. C-0BC71BDD
- Health Score: 55
- Disqualification Reason: Fails Rule R2 (`churn_save_eligible_amount` is $0.00, not > 0). Renewal is in 52 days (2026-10-27).

2. C-0BA71F12
- Health Score: 52
- Disqualification Reason: Fails Rule R3 (`renewal_date` of 2027-04-11 is 218 days from snapshot, exceeding the 120-day limit).

3. C-0F6694C3
- Health Score: 43
- Disqualification Reason: Fails Rule R2 (`churn_save_eligible_amount` is $0.00, not > 0) and Rule R3 (`renewal_date` of 2027-03-21 is 197 days from snapshot, exceeding the 120-day limit).

4. C-0BE96399
- Health Score: 54
- Disqualification Reason: Fails Rule R2 (`churn_save_eligible_amount` is $0.00, not > 0). Renewal is in 54 days (2026-10-29).

5. C-0F876796
- Health Score: 47
- Disqualification Reason: Fails Rule R3 (`renewal_date` of 2027-02-06 is 154 days from snapshot, exceeding the 120-day limit).

6. C-0FCCD2DF
- Health Score: 43
- Disqualification Reason: Fails Rule R2 (`churn_save_eligible_amount` is $0.00, not > 0) and Rule R3 (`renewal_date` of 2027-04-23 is 230 days from snapshot, exceeding the 120-day limit).

7. C-10A56B0F
- Health Score: 54
- Disqualification Reason: Fails Rule R2 (`churn_save_eligible_amount` is $0.00, not > 0). Renewal is in 98 days (2026-12-12).
expansion-kitPASS 1.00

? The Question

A customer replied to outreach (reply included). Build the expansion kit: seat coverage (licensed over headcount), two lines on usage health, headroom in seats and ARR at the current per-seat rate, who replied and whether they can buy, the right buyer from our contacts, and a reply email under 150 words that moves toward the right person without being pushy and cites one real usage fact.
cost $0.0194 · 18s · in 15,666 / out 2,044 tok
✓ coverage_pct✓ headroom_arr✓ right_buyer✓ replier_not_buyer✓ usage_fact✓ email_under_150

✓ Correct Answer (ground truth)

company_alias: C-0DDFC9A7
coverage_pct: 37.5
headroom_seats: 250
per_seat_annual_rate: 60.00
headroom_arr: 15000.00
replier: Maria S.
replier_can_buy: False
right_buyer: name=Dana R.; title=VP People
usage_facts: active_users_2026_08=126; active_users_2026_03=88; growth_pct_6m=43.2; licensed_seat_utilization_pct=84.0
usage_health_lines: Monthly active users grew from 88 (2026-03) to 126 (2026-08), +43.2% over 6 months., Last month 126 of 150 licensed seats were active (84.0% utilization), approaching the seat cap.
graded checks: coverage_pct · headroom_arr · right_buyer · replier_not_buyer · usage_fact · email_under_150

Model's actual answer

show the model's full answer
EXPANSION KIT: C-0DDFC9A7

1. Seat Coverage
Arithmetic: 150 licensed seats / 400 headcount = 0.375
Seat Coverage: 37.5%

2. Usage Health
- Monthly active users grew consecutively each month from 88 in March 2026 to 126 in August 2026 (+38 active users, +43.18% increase).
- Active user utilization is currently at 84.0% of licensed capacity (126 active users / 150 licensed seats), leaving 24 unused licensed seats before hitting the plan ceiling.

3. Headroom in Seats and ARR
- Current rate per seat: $9,000.00 current ARR / 150 licensed seats = $60.00 per seat/year
- Seat headroom to full headcount: 400 headcount - 150 licensed seats = 250 seats
- ARR headroom: 250 seats * $60.00 per seat = $15,000.00 ARR headroom
(Total potential ARR at 400 seats: 400 * $60.00 = $24,000.00)

4. Respondent & Purchasing Authority
- Respondent: Maria S., People Operations Coordinator
- Purchasing Authority: No. She explicitly stated she is not the right person for purchasing decisions and does not hold budget authority.

5. Right Buyer From Contacts
- Dana R., VP People (confirmed in expansion_contacts.csv; last engaged 2026-05-18; identified by Maria S. as holding budget and seat expansion authority).

6. Reply Email (77 words)
Subject: Re: Growing your team's recognition program

Hi Maria,

Thanks so much for getting back to me, and it is wonderful to hear the team is enjoying Bonusly. Seeing your active users grow every month to 126 in August is fantastic.

I would really appreciate an introduction to Dana R. whenever convenient for you. I would be glad to share a quick overview of those usage numbers with her to help with her planning, with zero pressure.

Thanks again for your help,

Cole
onboarding-statusPASS 1.00

? The Question

Prep the CSM for the mid-onboarding call: what is complete (integrations connected, allowance set, admins added, first recognition given, first redemption), what is not, early engagement signals, and the three things to cover on the call. Mark nothing complete without a data field that shows it.
cost $0.0228 · 17s · in 15,737 / out 2,944 tok
✓ complete_items✓ incomplete_items✓ three_items✓ grounded

✓ Correct Answer (ground truth)

company_alias: C-0D284E42
days_live: 25
checklist: {"slack_integration_connected": {"complete": true, "field": "integration_slack", "value": "2026-08-12"}, "hris_integration_connected": {"complete": false, "field": "integration_hris", "value": ""}, "a
engagement_signals: avg_active_givers_week1=4.3; avg_active_givers_last_week=13.0; latest_active_givers=15; days_with_activity=25
call_items: Connect the HRIS integration (integration_hris is empty) to automate user provisioning and offboarding., Get to first redemption (first_redemption_at is empty): walk admins through the rewards catalog and confirm allowance funding., Build the giving habit: daily active givers averaged 4.3 in week 1 vs 13.0 last week - agree on manager nudges and a recognition cadence.
graded checks: complete_items · incomplete_items · three_items · grounded

Model's actual answer

show the model's full answer
CSM Mid-Onboarding Call Prep: Company C-0D284E42

==================================================
1. WHAT IS COMPLETE
==================================================
- Slack Integration: COMPLETE
  Data field: integration_slack = 2026-08-12
- Allowance Set: COMPLETE
  Data field: allowance_set = 2026-08-13
- Admins Added: COMPLETE
  Data field: admins_added = 2
- First Recognition Given: COMPLETE
  Data field: first_recognition_at = 2026-08-15 14:22

==================================================
2. WHAT IS NOT COMPLETE
==================================================
- HRIS Integration: NOT COMPLETE
  Data field: integration_hris is empty/missing.
- First Redemption: NOT COMPLETE
  Data field: first_redemption_at is empty/missing.

==================================================
3. EARLY ENGAGEMENT SIGNALS
==================================================
Data source: onboarding_usage.csv (25 daily observations from 2026-08-11 to 2026-09-04).

- Active Giver Growth:
  * Start (2026-08-11): 3 active givers
  * End (2026-09-04): 15 active givers
  * Absolute Change: 15 - 3 = 12 active givers
  * Percentage Growth: ((15 - 3) / 3) * 100 = (12 / 3) * 100 = 400.0% increase

- Daily Average & Total Volume:
  * Total active giver observations: 3 + 3 + 4 + 4 + 5 + 4 + 7 + 5 + 7 + 6 + 9 + 8 + 9 + 9 + 9 + 11 + 10 + 10 + 11 + 13 + 11 + 13 + 13 + 15 + 15 = 214
  * Total days recorded: 25
  * Mean active givers per day: 214 / 25 = 8.56

- Weekly Progression (Arithmetic):
  * Week 1 (2026-08-11 to 2026-08-17, 7 days): (3 + 3 + 4 + 4 + 5 + 4 + 7) / 7 = 30 / 7 = 4.29 givers/day
  * Week 2 (2026-08-18 to 2026-08-24, 7 days): (5 + 7 + 6 + 9 + 8 + 9 + 9) / 7 = 53 / 7 = 7.57 givers/day
  * Week 3 (2026-08-25 to 2026-08-31, 7 days): (9 + 11 + 10 + 10 + 11 + 13 + 11) / 7 = 75 / 7 = 10.71 givers/day
  * Remaining Days (2026-09-01 to 2026-09-04, 4 days): (13 + 13 + 15 + 15) / 4 = 56 / 4 = 14.00 givers/day
  * Trend: Steady, uninterrupted upward trajectory each week.

==================================================
4. THREE THINGS TO COVER ON THE CALL
==================================================
1. Complete HRIS Integration:
   Review next steps to connect HRIS (integration_hris is currently empty/missing) to enable automated user management alongside the existing Slack integration (integration_slack = 2026-08-12).

2. Address First Redemption:
   Investigate and drive the first reward redemption (first_redemption_at is empty/missing), ensuring employees know how to redeem points accumulated since recognitions began on 2026-08-15 14:22.

3. Sustaining and Expanding Giver Engagement:
   Review adoption momentum (active givers rose from 3 on 2026-08-11 to 15 on 2026-09-04, a 400.0% increase) and confirm whether the 2 admins added (admins_added = 2) need additional admin seats or enablement resources to roll out across further departments.
renewal-risk-conflicting-datesPASS 1.00

? The Question

Produce a 90-day renewal risk brief. Two systems hold renewal dates and disagree on some accounts; decide which to trust per account and say why (multi-year contracts are known to be wrong in ChurnZero). For every renewal: company, CSM, ARR, date used, seat utilization, 3-month usage trend, risk rating with one sentence of evidence. Flag every disagreement. Close with total ARR renewing and ARR at risk.
cost $0.1804 · 172s · in 72,596 / out 27,879 tok
✓ total_renewing✓ arr_at_risk✓ disagreements_flagged✓ trust_rule

✓ Correct Answer (ground truth)

snapshot_date: 2026-09-05
window: 2026-09-05 to 2026-12-04
trust_rule: multi-year contracts: Chargebee is authoritative (ChurnZero known wrong); otherwise systems agree or Chargebee wins
accounts: 20 items — e.g. account_alias=C-0B144C78; csm=Cole Ingram; arr=30899.00; trusted_renewal_date=2026-11-02; trusted_source_why=systems agree (annual term); in_90d_window=True; dates_disagree=False; seat_utilization_pct=75.4; usage_3m_ratio=1.03; risk=low; evidence=3-month usage ratio 1.03 (last3 avg 103 vs prior3 100), seat utilization 75% | account_alias=C-0B20DB64; csm=Dana Mercer; arr=21770.00; trusted_renewal_date=2026-10-07; trusted_source_why=systems agree (annual term); in_90d_window=True; dates_disagree=False; seat_utilization_pct=56.6; usage_3m_ratio=1.0; risk=medium; evidence=3-month usage ratio 1.00 (last3 avg 295 vs prior3 295), seat utilization 57% | account_alias=C-0B344485; csm=Elena Sinclair; arr=64384.00; trusted_renewal_date=2026-11-16; trusted_source_why=systems agree (annual term); in_90d_window=True; dates_disagree=False; seat_utilization_pct=78.0; usage_3m_ratio=1.04; risk=low; evidence=3-month usage ratio 1.04 (last3 avg 241 vs prior3 231), seat utilization 78% | account_alias=C-0B369871; csm=Dana Mercer; arr=85128.00; trusted_renewal_date=2026-10-29; trusted_source_why=systems agree (annual term); in_90d_window=True; dates_disagree=False; seat_utilization_pct=75.1; usage_3m_ratio=1.04; risk=low; evidence=3-month usage ratio 1.04 (last3 avg 330 vs prior3 316), seat utilization 75%
disagreements: account_alias=C-0B7D2C30; cz_renewal_date=2026-09-10; cb_renewal_date=2026-09-15; is_multi_year=True; trusted=2026-09-15 | account_alias=C-0BBE3E60; cz_renewal_date=2027-09-26; cb_renewal_date=2026-09-26; is_multi_year=True; trusted=2026-09-26 | account_alias=C-0BCDB8C2; cz_renewal_date=2027-09-18; cb_renewal_date=2026-09-18; is_multi_year=True; trusted=2026-09-18 | account_alias=C-0D2AB865; cz_renewal_date=2026-09-10; cb_renewal_date=2026-09-22; is_multi_year=True; trusted=2026-09-22 | account_alias=C-0F5D2323; cz_renewal_date=2026-09-10; cb_renewal_date=2026-09-29; is_multi_year=True; trusted=2026-09-29
n_disagreements: 5
total_arr_renewing_90d: 1048715.00
arr_at_risk_90d: 359409.00
n_accounts: 20
graded checks: total_renewing · arr_at_risk · disagreements_flagged · trust_rule

Model's actual answer

show the model's full answer
### 90-Day Renewal Risk Brief

**Data & Methodology Notes:**
- **Missing Data:** Actual legal company names are not provided in the source files; `account_alias` is cited as the unique company identifier. No explicit calendar as-of date is provided; all 20 accounts fall within the upcoming 90-day renewal cycle (September 15, 2026 through November 24, 2026).
- **System of Record & Discrepancy Rule:** Multi-year contracts are known to be inaccurate in ChurnZero (which frequently retains stale baseline dates or erroneous anniversary years). Therefore, Chargebee is trusted as the billing system of record for all multi-year contracts (`is_multi_year = true`). For standard 12-month annual contracts, both ChurnZero and Chargebee agree exactly.
- **Risk Framework:**
  - **High Risk:** Severe seat underutilization (<50%) and/or steep 3-month active user contraction (>=10%).
  - **Medium Risk:** Sub-optimal seat utilization (<65%) and/or mild usage contraction.
  - **Low Risk:** Strong seat utilization (>=65%) accompanied by flat or growing 3-month usage.
  - **Total ARR at Risk:** High Risk ARR + Medium Risk ARR.

---

### Date Disagreements Flagged (5 Accounts)
All 5 discrepancies occur on multi-year agreements where ChurnZero holds invalid dates:
1. **C-0B7D2C30**: ChurnZero = `2026-09-10` vs. Chargebee = `2026-09-15` (36-month term). **Date Used:** `2026-09-15` (Chargebee trusted; multi-year contract).
2. **C-0BCDB8C2**: ChurnZero = `2027-09-18` vs. Chargebee = `2026-09-18` (36-month term; ChurnZero erroneously rolled forward). **Date Used:** `2026-09-18` (Chargebee trusted; multi-year contract).
3. **C-0D2AB865**: ChurnZero = `2026-09-10` vs. Chargebee = `2026-09-22` (24-month term). **Date Used:** `2026-09-22` (Chargebee trusted; multi-year contract).
4. **C-0BBE3E60**: ChurnZero = `2027-09-26` vs. Chargebee = `2026-09-26` (24-month term; ChurnZero erroneously rolled forward). **Date Used:** `2026-09-26` (Chargebee trusted; multi-year contract).
5. **C-0F5D2323**: ChurnZero = `2026-09-10` vs. Chargebee = `2026-09-29` (24-month term). **Date Used:** `2026-09-29` (Chargebee trusted; multi-year contract).

---

### Account-by-Account Renewal Risk Assessment

| Company (Alias) | CSM | ARR | Date Used | Seat Utilization | 3-Month Usage Trend (Jun → Jul → Aug 2026) | Risk Rating & Evidence |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| **C-0B7D2C30** | Dana Mercer | $65,901.00 | 2026-09-15 *(Disagreement: Trust Chargebee, 36mo multi-year)* | 274 / 476 = 57.6% | 97 → 94 → 84: (84 - 97) / 97 = -13.4% | **High Risk** — Active users declined by -13.4% over the last 3 months with mediocre 57.6% seat utilization. |
| **C-0BCDB8C2** | Cole Ingram | $54,427.00 | 2026-09-18 *(Disagreement: Trust Chargebee, 36mo multi-year)* | 232 / 424 = 54.7% | 127 → 118 → 110: (110 - 127) / 127 = -13.4% | **High Risk** — Usage dropped -13.4% over the trailing quarter alongside low seat utilization of 54.7%. |
| **C-0D2AB865** | Elena Sinclair | $38,022.00 | 2026-09-22 *(Disagreement: Trust Chargebee, 24mo multi-year)* | 250 / 407 = 61.4% | 125 → 117 → 109: (109 - 125) / 125 = -12.8% | **High Risk** — Active users fell -12.8% over the past 3 months despite moderate 61.4% seat utilization. |
| **C-0BBE3E60** | Dana Mercer | $30,993.00 | 2026-09-26 *(Disagreement: Trust Chargebee, 24mo multi-year)* | 74 / 114 = 64.9% | 39 → 35 → 33: (33 - 39) / 39 = -15.4% | **High Risk** — Active usage deteriorated by -15.4% over the last 3 months. |
| **C-0F5D2323** | Cole Ingram | $90,647.00 | 2026-09-29 *(Disagreement: Trust Chargebee, 24mo multi-year)* | 111 / 390 = 28.5% | 20 → 21 → 18: (18 - 20) / 20 = -10.0% | **High Risk** — Critical seat underutilization at 28.5% compounded by a -10.0% 3-month drop in active users. |
| **C-0EC6999D** | Elena Sinclair | $79,419.00 | 2026-10-03 *(Agreed: 12mo annual)* | 31 / 112 = 27.7% | 17 → 16 → 15: (15 - 17) / 17 = -11.8% | **High Risk** — Severe underutilization at 27.7% and active user count declined -11.8% over 3 months. |
| **C-0B20DB64** | Dana Mercer | $21,770.00 | 2026-10-07 *(Agreed: 12mo annual)* | 214 / 378 = 56.6% | 294 → 298 → 294: (294 - 294) / 294 = 0.0% | **Medium Risk** — Usage remained flat at 294, but seat utilization is low at 56.6%. |
| **C-0BBC4E7A** | Cole Ingram | $56,374.00 | 2026-10-10 *(Agreed: 12mo annual)* | 228 / 337 = 67.7% | 142 → 141 → 139: (139 - 142) / 142 = -2.1% | **Medium Risk** — Seat utilization is fair at 67.7%, but 3-month usage slipped by -2.1%. |
| **C-0FD551AB** | Elena Sinclair | $48,815.00 | 2026-10-14 *(Agreed: 12mo annual)* | 210 / 376 = 55.9% | 123 → 122 → 126: (126 - 123) / 123 = +2.4% | **Medium Risk** — Seat utilization is lagging at 55.9% despite a slight +2.4% uptick in active users. |
| **C-0F9F8F13** | Dana Mercer | $46,230.00 | 2026-10-18 *(Agreed: 12mo annual)* | 199 / 352 = 56.5% | 185 → 185 → 182: (182 - 185) / 185 = -1.6% | **Medium Risk** — Sub-60% seat utilization (56.5%) combined with a mild -1.6% usage decline. |
| **C-0BC34584** | Cole Ingram | $16,740.00 | 2026-10-22 *(Agreed: 12mo annual)* | 327 / 494 = 66.2% | 104 → 104 → 106: (106 - 104) / 104 = +1.9% | **Low Risk** — Healthy seat utilization of 66.2% backed by positive 3-month usage growth (+1.9%). |
| **C-0B7A7546** | Elena Sinclair | $35,062.00 | 2026-10-25 *(Agreed: 12mo annual)* | 182 / 205 = 88.8% | 64 → 65 → 63: (63 - 64) / 64 = -1.6% | **Medium Risk** — Seat utilization is strong at 88.8%, but active users declined slightly by -1.6%. |
| **C-0B369871** | Dana Mercer | $85,128.00 | 2026-10-29 *(Agreed: 12mo annual)* | 317 / 422 = 75.1% | 326 → 330 → 333: (333 - 326) / 326 = +2.1% | **Low Risk** — Solid 75.1% seat utilization with steady 3-month usage expansion (+2.1%). |
| **C-0B144C78** | Cole Ingram | $30,899.00 | 2026-11-02 *(Agreed: 12mo annual)* | 169 / 224 = 75.4% | 101 → 101 → 106: (106 - 101) / 101 = +5.0% | **Low Risk** — Strong seat utilization (75.4%) and solid +5.0% user growth over 3 months. |
| **C-0FC4DBB8** | Elena Sinclair | $94,732.00 | 2026-11-05 *(Agreed: 12mo annual)* | 356 / 464 = 76.7% | 189 → 191 → 193: (193 - 189) / 189 = +2.1% | **Low Risk** — Strong 76.7% seat utilization with consistent usage gains (+2.1%). |
| **C-0D5BBE3A** | Dana Mercer | $39,740.00 | 2026-11-09 *(Agreed: 12mo annual)* | 85 / 102 = 83.3% | 88 → 90 → 91: (91 - 88) / 88 = +3.4% | **Low Risk** — High seat utilization (83.3%) paired with positive 3-month user growth (+3.4%). |
| **C-0FB9D5AF** | Cole Ingram | $63,158.00 | 2026-11-13 *(Agreed: 12mo annual)* | 144 / 199 = 72.4% | 173 → 173 → 176: (176 - 173) / 173 = +1.7% | **Low Risk** — Well utilized at 72.4% with positive user momentum (+1.7%). |
| **C-0B344485** | Elena Sinclair | $64,384.00 | 2026-11-16 *(Agreed: 12mo annual)* | 224 / 287 = 78.0% | 238 → 240 → 244: (244 - 238) / 238 = +2.5% | **Low Risk** — High seat adoption of 78.0% and steady user growth of +2.5% over 3 months. |
| **C-0CB2C1B4** | Dana Mercer | $40,628.00 | 2026-11-20 *(Agreed: 12mo annual)* | 386 / 473 = 81.6% | 47 → 48 → 49: (49 - 47) / 47 = +4.3% | **Low Risk** — Strong 81.6% seat utilization with expanding 3-month usage (+4.3%). |
| **C-22170CA1** | Cole Ingram | $45,646.00 | 2026-11-24 *(Agreed: 12mo annual)* | 251 / 294 = 85.4% | 143 → 148 → 146: (146 - 143) / 143 = +2.1% | **Low Risk** — Excellent seat adoption at 85.4% and net positive user growth (+2.1%). |

---

### Portfolio Totals & Arithmetic

#### 1. Total ARR Renewing (All 20 Accounts)
```
  $65,901.00  (C-0B7D2C30)
+ $54,427.00  (C-0BCDB8C2)
+ $38,022.00  (C-0D2AB865)
+ $30,993.00  (C-0BBE3E60)
+ $90,647.00  (C-0F5D2323)
+ $79,419.00  (C-0EC6999D)
+ $21,770.00  (C-0B20DB64)
+ $56,374.00  (C-0BBC4E7A)
+ $48,815.00  (C-0FD551AB)
+ $46,230.00  (C-0F9F8F13)
+ $16,740.00  (C-0BC34584)
+ $35,062.00  (C-0B7A7546)
+ $85,128.00  (C-0B369871)
+ $30,899.00  (C-0B144C78)
+ $94,732.00  (C-0FC4DBB8)
+ $39,740.00  (C-0D5BBE3A)
+ $63,158.00  (C-0FB9D5AF)
+ $64,384.00  (C-0B344485)
+ $40,628.00  (C-0CB2C1B4)
+ $45,646.00  (C-22170CA1)
─────────────────────────
= $1,048,715.00
```

#### 2. ARR at Risk Arithmetic
- **High Risk ARR (6 Accounts):**
  `$65,901.00 + $54,427.00 + $38,022.00 + $30,993.00 + $90,647.00 + $79,419.00 = $359,409.00`
- **Medium Risk ARR (5 Accounts):**
  `$21,770.00 + $56,374.00 + $48,815.00 + $46,230.00 + $35,062.00 = $208,251.00`
- **Total ARR at Risk (High + Medium):**
  `$359,409.00 + $208,251.00 = $567,660.00`

*(Low Risk ARR: $16,740.00 + $85,128.00 + $30,899.00 + $94,732.00 + $39,740.00 + $63,158.00 + $64,384.00 + $40,628.00 + $45,646.00 = $481,055.00)*

---

### Final Summary
- **Total ARR Renewing:** **$1,048,715.00**
- **ARR at Risk:** **$567,660.00** (54.1% of renewing pipeline; $359,409.00 High Risk + $208,251.00 Medium Risk)
ticket-theme-synthesis0.80

? The Question

Synthesize the quarter's support tickets into 4 to 7 themes. Do not trust existing tags; read the text. Per theme: name, count, share, distinct accounts, ARR affected, two ticket ids, one-line recommendation. Separate broad patterns from single-account noise and rank by ARR exposure, not volume.
cost $0.2226 · 165s · in 146,238 / out 26,666 tok
✗ theme_count_range✓ counts_verifiable✓ single_account_flagged✓ arr_ranked✓ ticket_ids

✓ Correct Answer (ground truth)

quarter_window: 2026-06-01 through 2026-08-31
total_tickets: 80
themes: {"points_not_posting": {"count": 20, "share_pct": 25.0, "distinct_accounts": 9, "arr_affected": "31100.00", "example_ticket_ids": ["IC-460001", "IC-460002"], "single_account": false}, "slack_sync": {"
ranking_by_arr_exposure: hris_provisioning, redemption_failures, billing_dispute, points_not_posting, slack_sync
single_account_noise_theme: billing_dispute
note: existing tags are noisy/unreliable; classify by text
sample_ticket_ids: 80 items — e.g. IC-460004, IC-460016, IC-460001, IC-460041
graded checks: theme_count_range · counts_verifiable · single_account_flagged · arr_ranked · ticket_ids

Model's actual answer

show the model's full answer
### Support Ticket Themes Synthesis

Total tickets analyzed: 80  
Total distinct accounts: 24  
Total unique ARR across accounts: $284,800.00  

---

### Broad Patterns (Multi-Account Issues)
Ranked by ARR exposure (distinct account ARR affected):

1. **HRIS Provisioning & User Sync Failures**
   - Count: 12
   - Share: 15.00% (12 / 80)
   - Distinct Accounts: 3 (C-0DDFC9A7, C-0B2213A9, C-0F6C0F34)
   - ARR Affected: $114,000.00 ($48,000.00 + $36,000.00 + $30,000.00)
   - Two Ticket IDs: IC-460059, IC-460062
   - One-Line Recommendation: Audit background HRIS sync workers and error-handling logs to ensure new employee provisioning jobs execute reliably and surface silent sync failures.

2. **Reward Redemption & Gift Card Delivery Failures**
   - Count: 18
   - Share: 22.50% (18 / 80)
   - Distinct Accounts: 7 (C-14264ABD, C-0B827671, C-0B0F1BAB, C-0FCCD2DF, C-0D9CA315, C-0CEF69FD, C-0F876796)
   - ARR Affected: $68,800.00 ($11,000.00 + $10,700.00 + $10,300.00 + $9,600.00 + $9,600.00 + $8,900.00 + $8,700.00)
   - Two Ticket IDs: IC-460025, IC-460030
   - One-Line Recommendation: Implement idempotent transactions and retry queues with vendor APIs to eliminate checkout timeouts, deduction without fulfillment, and undelivered gift cards.

3. **Recognition Delivery & Points Balance Sync Delays**
   - Count: 20
   - Share: 25.00% (20 / 80)
   - Distinct Accounts: 9 (C-0BF20542, C-0D0B047C, C-0D6CC8E3, C-0D3278C7, C-0D284E42, C-21FEBCBB, C-0B2895EF, C-0BE96399, C-0DD0626C)
   - ARR Affected: $31,100.00 ($4,500.00 + $4,500.00 + $4,200.00 + $3,500.00 + $3,400.00 + $2,900.00 + $2,900.00 + $2,700.00 + $2,500.00)
   - Two Ticket IDs: IC-460004, IC-460016
   - One-Line Recommendation: Resolve asynchronous database queue delays and weekend batch processing lags that prevent sent recognitions from immediately crediting user point balances.

4. **Slack Integration Sync & Slash Command Errors**
   - Count: 14
   - Share: 17.50% (14 / 80)
   - Distinct Accounts: 4 (C-10A56B0F, C-8C2E8F00, C-0B843542, C-0BA71F12)
   - ARR Affected: $18,900.00 ($5,400.00 + $5,200.00 + $4,400.00 + $3,900.00)
   - Two Ticket IDs: IC-460041, IC-460047
   - One-Line Recommendation: Fix OAuth token refresh persistence and slash-command API endpoint availability to stop recurring Slack app disconnections and sync toggling.

---

### Single-Account Noise (Isolated Account Escalation)

5. **Billing Invoicing & Renewal Pricing Discrepancies**
   - Count: 16
   - Share: 20.00% (16 / 80)
   - Distinct Accounts: 1 (C-0E9C27D1)
   - ARR Affected: $52,000.00 ($52,000.00)
   - Two Ticket IDs: IC-460071, IC-460069
   - One-Line Recommendation: Assign a dedicated finance/billing specialist to immediately issue corrected invoices, align contract seat terms (150 vs. 200), and lock in correct renewal tier pricing for C-0E9C27D1.
marketing
case-study-matchPASS 1.00

? The Question

Given the prospect, rank the three most similar existing customers with public case studies for use as social proof. Similarity on industry, size band, use case, region. Cite the fields that drove each match. Do not name any customer without a case study and never include billing data or contact names.
cost $0.0224 · 19s · in 15,959 / out 2,793 tok
✓ top3✓ decoys_excluded✓ cites_fields✓ no_billing

✓ Correct Answer (ground truth)

top3: company_alias=C-11C31562; similarity_score=3 | company_alias=C-64171065; similarity_score=3 | company_alias=C-A13C193D; similarity_score=3
decoys_excluded: company_alias=C-D6217CAA; similarity_score=4; reason=no_case_study | company_alias=C-C153868F; similarity_score=4; reason=no_case_study
constraints: no_billing_data, no_contact_names, only_customers_with_has_case_study_true_named
prospect_fields_used: industry, size_band, use_case, region
prospect_alias: C-82AF3719
graded checks: top3 · decoys_excluded · cites_fields · no_billing

Model's actual answer

show the model's full answer
Prospect: C-82AF3719
- Industry: Technology
- Size Band: Mid-Market
- Use Case: employee_recognition
- Region: NA-West

Eligible Customers with Public Case Studies (has_case_study = true):
- C-FEFCC020: 0/4 matches (0.0%)
- C-11209339: 0/4 matches (0.0%)
- C-9D69D147: 2/4 matches (50.0%) [size_band, region]
- C-11C31562: 3/4 matches (75.0%) [size_band, use_case, region]
- C-CD4829A7: 2/4 matches (50.0%) [industry, size_band]
- C-0CE315B2: 0/4 matches (0.0%)
- C-64171065: 3/4 matches (75.0%) [industry, size_band, use_case]
- C-A13C193D: 3/4 matches (75.0%) [industry, size_band, region]

Top 3 Most Similar Customers:

1. C-64171065
- Match Score: 3/4 (75.0%)
- Matching Fields:
  * industry: Technology
  * size_band: Mid-Market
  * use_case: employee_recognition
- Differing Field: region (NA-East vs NA-West)

2. C-A13C193D
- Match Score: 3/4 (75.0%)
- Matching Fields:
  * industry: Technology
  * size_band: Mid-Market
  * region: NA-West
- Differing Field: use_case (retention vs employee_recognition)

3. C-11C31562
- Match Score: 3/4 (75.0%)
- Matching Fields:
  * size_band: Mid-Market
  * use_case: employee_recognition
  * region: NA-West
- Differing Field: industry (Manufacturing vs Technology)
channel-attribution-mathPASS 1.00

? The Question

Compute channel performance for the trailing 6 months. Paid: spend, SQMs, SQOs, cost per SQM, cost per SQO, SQM-to-SQO rate, pipeline amount, pipeline per dollar. Organic: volume, SQO rate, pipeline. Report a channel with spend and zero SQMs as undefined, not zero. Flag rows where SQO date precedes SQM date. Finish with a reallocation recommendation and your confidence given sample sizes.
cost $0.0741 · 60s · in 55,277 / out 7,845 tok
✓ undefined_channel✓ undefined_not_zero✓ flagged_rows✓ best_channel✓ reallocation

✓ Correct Answer (ground truth)

months: 2026-03, 2026-04, 2026-05, 2026-06, 2026-07, 2026-08
per_channel: {"linkedin_ads": {"spend_usd": 24000, "sqms": 25, "sqos": 8, "pipeline_amount": 96000, "cost_per_sqm": 960.0, "cost_per_sqo": 3000.0, "sqm_to_sqo_rate": 0.32, "pipeline_per_dollar": 4.0}, "organic_sea
undefined_channels: paid_social
flagged_rows_sqo_before_sqm: CT-000041, CT-000044
best_pipeline_per_dollar_channel: paid_search
reallocation_recommendation: from=paid_social; to=paid_search; rationale=paid_social has spend but zero SQMs (cost per SQM undefined, not zero); paid_search has the highest pipeline per dollar.
confidence_note: webinars and referral have small SQM samples (<20); treat rates as directional.
graded checks: undefined_channel · undefined_not_zero · flagged_rows · best_channel · reallocation

Model's actual answer

show the model's full answer
### DATA INTEGRITY FLAGS (SQO Date Precedes SQM Date)

The following 2 contact records have an `sqo_date` that occurs before the `sqm_date`:
1. contact_key: CT-000041 | channel: linkedin_ads | sqm_date: 2026-06-14 | sqo_date: 2026-06-09 | pipeline_amount: $12,000
2. contact_key: CT-000044 | channel: linkedin_ads | sqm_date: 2026-07-23 | sqo_date: 2026-07-18 | pipeline_amount: $12,000


### PAID CHANNEL PERFORMANCE (Trailing 6 Months: 2026-03 to 2026-08)

1. paid_search
- Spend: $36,000 (6 months * $6,000)
- SQMs: 40
- SQOs: 18
- Cost per SQM: $900.00 ($36,000 / 40)
- Cost per SQO: $2,000.00 ($36,000 / 18)
- SQM-to-SQO Rate: 45.00% (18 / 40)
- Pipeline Amount: $720,000
- Pipeline per Dollar: $20.00 ($720,000 / $36,000)

2. linkedin_ads
- Spend: $24,000 (6 months * $4,000)
- SQMs: 25
- SQOs: 8
- Cost per SQM: $960.00 ($24,000 / 25)
- Cost per SQO: $3,000.00 ($24,000 / 8)
- SQM-to-SQO Rate: 32.00% (8 / 25)
- Pipeline Amount: $96,000
- Pipeline per Dollar: $4.00 ($96,000 / $24,000)

3. paid_social
- Spend: $18,000 (6 months * $3,000)
- SQMs: 0 (Missing/no matching records in contacts_first_touch.csv)
- SQOs: 0
- Cost per SQM: undefined ($18,000 spend with 0 SQMs)
- Cost per SQO: undefined ($18,000 spend with 0 SQOs)
- SQM-to-SQO Rate: undefined (0 SQMs)
- Pipeline Amount: $0
- Pipeline per Dollar: $0.00 ($0 / $18,000)

4. webinars
- Spend: $9,000 (6 months * $1,500)
- SQMs: 12
- SQOs: 5
- Cost per SQM: $750.00 ($9,000 / 12)
- Cost per SQO: $1,800.00 ($9,000 / 5)
- SQM-to-SQO Rate: 41.67% (5 / 12)
- Pipeline Amount: $60,000
- Pipeline per Dollar: $6.67 ($60,000 / $9,000)


### ORGANIC CHANNEL PERFORMANCE

1. organic_search
- Volume: 30
- SQO Rate: 33.33% (10 SQOs / 30 volume)
- Pipeline: $90,000

2. referral
- Volume: 15
- SQO Rate: 40.00% (6 SQOs / 15 volume)
- Pipeline: $48,000


### REALLOCATION RECOMMENDATION & CONFIDENCE

Reallocation Recommendation:
1. Immediately pause or audit paid_social spend ($3,000/month, $18,000 total) until UTM/CRM tracking is investigated, as it currently yields zero attributable SQMs, SQOs, or pipeline.
2. Reallocate budget to paid_search, which delivers the highest return at $20.00 pipeline per dollar, the lowest cost per SQO among primary paid acquisition channels ($2,000.00), and the highest conversion rate (45.00%).
3. Maintain or test modest expansion in webinars, which is efficient ($750.00 per SQM, $1,800.00 per SQO, $6.67 pipeline per dollar).
4. Reduce or restructure linkedin_ads ($4.00 pipeline per dollar, $3,000.00 per SQO) and investigate data integrity issues (2 contacts, CT-000041 and CT-000044, where SQO occurred prior to SQM).

Confidence Level:
MEDIUM.
- Sample sizes are small to moderate across all channels (e.g., webinars n=12 SQMs / 5 SQOs; referral n=15 volume / 6 SQOs; linkedin_ads n=25 SQMs / 8 SQOs; paid_search n=40 SQMs / 18 SQOs).
- A complete absence of contacts for paid_social points toward an attribution/tracking instrumentation gap rather than confirmed campaign failure.
- LinkedIn data has stage timestamp sequencing anomalies that warrant audit before making permanent capital reallocations.
competitor-battlecard-updatePASS 1.00

? The Question

Update the battlecard for the competitor. Sections: one-line positioning, pricing with source and date (newer source wins, note the conflict), where they win, where we win, objections and responses, recent changes, our 12-month win/loss record against them. Cite a snippet id for every factual claim. Rep opinion on a call is not a fact about the competitor. Anything from the old card you cannot re-source gets marked unverified.
cost $0.0489 · 60s · in 16,824 / out 9,688 tok
✓ competitor_named✓ pricing✓ win_loss✓ unverified_marked✓ citations

✓ Correct Answer (ground truth)

competitor: Rivally
pricing: value_per_user_per_month=7; source_snippet=S17; source_date=2026-08-12
win_loss_12mo: wins=13; losses=7
unverified_old_claims: Rivally lacks a Slack integration., Rivally was acquired by WorkHuman in 2025.
rep_opinion_snippet_ids_not_facts: S09, S21
citation_ids_available: 25 items — e.g. S01, S02, S03, S04
graded checks: competitor_named · pricing · win_loss · unverified_marked · citations

Model's actual answer

show the model's full answer
BATTLECARD: RIVALLY (UPDATED)

============================================================
1. ONE-LINE POSITIONING
============================================================
Rivally positions as a points-based employee recognition platform for mid-market and distributed enterprise teams [S02, S04, S12, S16].

Old Battlecard Audit:
- "Positioning: points-based recognition for mid-market": Verified via [S02, S04].
- "Rivally lacks a Slack integration": UNVERIFIED / CONTRADICTED. Refuted by [S04], which confirms the Slack integration worked out of the box.
- "Rivally was acquired by WorkHuman in 2025": UNVERIFIED. No source in provided data supports this; Rivally announced an independent Series C round of $40M led by Northgate Ventures on 2025-11-04 [S01].
- "Strong in EU enterprise with multi-language support": Verified via [S12].


============================================================
2. PRICING WITH SOURCE AND DATE
============================================================
Winning Current List Price:
- $7.00 per user/month, annual billing required for Recognition Starter tier (Source: pricing_page, 2026-08-12) [S17].

Pricing History and Conflicts:
- 2026-01-20 (pricing_page): Listed at $5.00 per user/month, annual billing required [S03].
- 2026-04-01 (pricing_page): Listed at $5.00 per user/month for Recognition Starter tier [S08].
- 2026-06-02 (call_notes): Quoted $6.50 per user/month to a 500-seat prospect on an annual term [S13].
- 2026-08-12 (pricing_page): Updated to $7.00 per user/month, annual billing required [S17] (newer official source supersedes [S03] and [S08]).
- 2026-08-14 (call_notes): Quoted $7.00 per user/month list, with a 15% discount offered for a 3-year term [S18].
- 2026-09-01 (press): "Rivally Pulse" add-on exits beta; priced separately as an add-on, not bundled [S23]. Note: specific add-on pricing data is missing from the provided records.

Excluded Non-Factual Data:
- Elena Sinclair's AE opinion on 2026-08-28 regarding aggressive discounting is excluded as unconfirmed rep opinion [S21].


============================================================
3. WHERE THEY WIN
============================================================
- Fast Deployment: Setup completed in under a week [S04].
- Native Collaboration Integrations: Slack integration functional out of the box [S04]; Microsoft Teams app v2 launched in public preview [S19].
- European Footprint and Compliance: Strong for distributed EU teams with praised multi-language support [S12]; EU data residency is generally available [S15] (pitched in [S05]).
- Recognition Experience: Points-based recognition feed praised as engaging by users [S02, S16].
- Customer Support: Support response times praised as under 4 hours [S22].


============================================================
4. WHERE WE WIN
============================================================
- Reporting and Analytics Depth: Prospects cite analytics depth when selecting Bonusly over Rivally (e.g., 800-seat deal mention on 2026-09-03) [S25]. Rivally's analytics are limited [S02], reporting dashboards are basic compared to enterprise tools [S07], and data exports are CSV-only, complicating migration [S20].
- Enterprise User Management & Admin Tools: Rivally lacks SCIM provisioning, leading to painful manual user management [S10]; admin console lacks bulk recognition editing [S24]; overall admin tooling lags peers [S16].
- International Rewards Breadth: Rivally's rewards catalog in EMEA is thinner than its US catalog [S14].

Excluded Non-Factual Data:
- Bryce Harmon's AE opinion on 2026-04-10 that Rivally's UI is "clunky" is excluded as unverified rep opinion [S09].


============================================================
5. OBJECTIONS AND RESPONSES
============================================================
Objection 1: "Rivally is cheaper at $5/user/month."
Response: Rivally increased its Recognition Starter list price to $7.00 per user/month with required annual billing on 2026-08-12 [S17], up from its prior $5.00 rate [S03, S08]. Quotes range between $6.50 and $7.00 [S13, S18], and their survey tool (Rivally Pulse) is unbundled and requires an additional add-on fee [S23].

Objection 2: "Rivally is better suited for European teams with EU data residency."
Response: While Rivally offers EU data residency [S15] and multi-language support [S12], its rewards catalog in EMEA is thinner than in the US [S14]. Furthermore, it lacks enterprise SCIM provisioning for user management [S10] and has CSV-only reporting exports [S20].

Objection 3: "Rivally offers faster onboarding and collaboration integrations."
Response: While Rivally integrates with Slack [S04] and Teams [S19] with quick initial setup [S04], its admin console lacks bulk recognition editing [S24], its admin tooling lags peers [S16], and reporting dashboards remain basic [S07].


============================================================
6. RECENT CHANGES
============================================================
- 2025-11-04: Announced a $40M Series C funding round led by Northgate Ventures [S01].
- 2026-03-05: Launched "Rivally Pulse", a lightweight engagement survey add-on [S06].
- 2026-05-09: Hired ex-Workday VP EMEA to lead European expansion [S11].
- 2026-07-01: Opened Dublin office and announced general availability of EU data residency [S15].
- 2026-08-12: Updated pricing page, increasing Recognition Starter to $7 per user/month with annual billing [S17].
- 2026-08-20: Announced Microsoft Teams app v2 in public preview [S19].
- 2026-09-01: Rivally Pulse exited beta as a paid, unbundled add-on [S23].


============================================================
7. OUR 12-MONTH WIN/LOSS RECORD AGAINST RIVALLY
============================================================
Period: 2025-09 through 2026-08 (12 months)
Source File: deals_with_competitor.csv

Deals Breakdown:
- 2025-09: Deal-7767F5 (loss), Deal-072E31 (win)
- 2025-10: Deal-A9FD43 (win), Deal-F65C8F (win)
- 2025-11: Deal-7AA785 (win), Deal-D263E0 (loss)
- 2025-12: Deal-44C524 (win), Deal-935746 (loss)
- 2026-01: Deal-0D0CD6 (win), Deal-E46EAB (win)
- 2026-02: Deal-D5B790 (win), Deal-1D2392 (win)
- 2026-03: Deal-5C636E (win), Deal-9066A6 (loss)
- 2026-04: Deal-5645A5 (loss), Deal-72A02F (loss)
- 2026-05: Deal-C6FFAA (loss)
- 2026-06: Deal-67BE14 (win)
- 2026-07: Deal-1B6969 (win)
- 2026-08: Deal-F03E7B (win)

Wins (13 deals):
Deal-072E31, Deal-A9FD43, Deal-F65C8F, Deal-7AA785, Deal-44C524, Deal-0D0CD6, Deal-E46EAB, Deal-D5B790, Deal-1D2392, Deal-5C636E, Deal-67BE14, Deal-1B6969, Deal-F03E7B

Losses (7 deals):
Deal-7767F5, Deal-D263E0, Deal-935746, Deal-9066A6, Deal-5645A5, Deal-72A02F, Deal-C6FFAA

Arithmetic:
- Total Deals = 13 wins + 7 losses = 20 deals
- Win Rate = 13 / 20 = 65.0%
- Loss Rate = 7 / 20 = 35.0%

Record: 13 wins - 7 losses (65.0% win rate across 20 deals)

Note on Missing Deal Alias:
Snippet S25 mentions a win against Rivally on 2026-09-03 for an 800-seat prospect [S25], but no deal alias is provided in the data and it falls outside the 2025-09 to 2026-08 12-month dataset, so it is not included in the 20-deal count above.
nurture-sequence-diagnosisPASS 1.00

? The Question

Review the sequences. Per sequence: sent, open rate, reply rate, meeting rate, weakest step. Identify tracking errors (opened above sent), audience overlap between sequences, and the failure mode for anything under 2 percent reply. One change per weak sequence and which to fix first. Under 350 words.
cost $0.3477 · 294s · in 129,415 / out 42,692 tok
✓ tracking_error✓ overlap_pair✓ weakest_step✓ fix_priority

✓ Correct Answer (ground truth)

per_sequence: {"New Logo Nurture": {"sent": 1386, "opened": 490, "replied": 90, "meetings": 27, "open_rate": 0.3535, "reply_rate": 0.0649, "meeting_rate": 0.0195, "weakest_step": 3}, "Expansion Nurture": {"sent": 8
tracking_error: sequence=Expansion Nurture; step=2; sent=300; opened=340; issue=opened exceeds sent
audience_overlap_pair: Cold Outbound - HR Leaders, Cold Outbound - People Ops
all_pairwise_overlaps: ['Cold Outbound - HR Leaders', 'Cold Outbound - People Ops'], ['Expansion Nurture', 'New Logo Nurture']
under_2pct_reply: Cold Outbound - HR Leaders
failure_mode: Cold Outbound - HR Leaders=opens healthy (~37%) but replies near zero from step 1 -> body/CTA not landing, not a deliverability or subject-line problem
fix_priority_first: Cold Outbound - HR Leaders
graded checks: tracking_error · overlap_pair · weakest_step · fix_priority

Model's actual answer

show the model's full answer
SEQUENCE PERFORMANCE:

New Logo Nurture:
- Sent: 1,386 (S1: 500, S2: 458, S3: 428)
- Open Rate: 490/1,386 = 35.35%
- Reply Rate: 90/1,386 = 6.49% (S1: 8.40%, S2: 6.55%, S3: 4.21%)
- Meeting Rate: 27/1,386 = 1.95%
- Weakest Step: Step 3 (open 28.04%, reply 4.21%, meeting 1.40%)

Expansion Nurture:
- Sent: 875 (S1: 300, S2: 300, S3: 275)
- Open Rate: 565/875 = 64.57%
- Reply Rate: 59/875 = 6.74% (S1: 7.33%, S2: 8.33%, S3: 4.36%)
- Meeting Rate: 12/875 = 1.37%
- Weakest Step: Step 3 (open 34.55%, reply 4.36%, meeting 1.09%)

Cold Outbound - HR Leaders:
- Sent: 1,785 (S1: 600, S2: 595, S3: 590)
- Open Rate: 545/1,785 = 30.53%
- Reply Rate: 8/1,785 = 0.45% (S1: 0.83%, S2: 0.34%, S3: 0.17%)
- Meeting Rate: 0/1,785 = 0.00%
- Weakest Step: Step 3 (reply 1/590 = 0.17%, 0 meetings)

Cold Outbound - People Ops:
- Sent: 1,163 (S1: 400, S2: 386, S3: 377)
- Open Rate: 340/1,163 = 29.23%
- Reply Rate: 29/1,163 = 2.49% (S1: 3.50%, S2: 2.33%, S3: 1.59%)
- Meeting Rate: 6/1,163 = 0.52%
- Weakest Step: Step 3 (open 21.22%, reply 1.59%, meeting 0.27%)

TRACKING ERROR:
Expansion Nurture Step 2: 340 opened / 300 sent (113.33%), opens exceed sent from duplicate tracking or bot pre-fetching.

AUDIENCE OVERLAP (23 contacts):
- Expansion Nurture & New Logo Nurture (2): CT-000301, CT-000624
- Cold Outbound - HR Leaders & Cold Outbound - People Ops (21): CT-000849, CT-000884, CT-000890, CT-000908, CT-001033, CT-001097, CT-001101, CT-001103, CT-001105, CT-001130, CT-001153, CT-001159, CT-001217, CT-001227, CT-001236, CT-001255, CT-001258, CT-001277, CT-001285, CT-001311, CT-001345

FAILURE MODES (<2% REPLY):
- Cold Outbound - HR Leaders (0.45% total): Value mismatch. Solid opens (40% S1) with sub-1% replies and 0 meetings show ineffective copy/CTA.
- Cold Outbound - People Ops Step 3 (1.59%): Follow-up fatigue without new value.

RECOMMENDED CHANGES:
- Cold Outbound - HR Leaders: Overhaul body copy with persona ROI proof points and low-friction CTA.
- Cold Outbound - People Ops: Replace Step 3 follow-up with a tactical resource.
- Fix First: Cold Outbound - HR Leaders (1,785 sent, 0 meetings).
weekly-marketing-goals-rollupPASS 1.00

? The Question

Produce the weekly marketing goals update for the active quarter: SQMs, SQOs, DS2s, closed-lost MIA rate, same-quarter close count, active pipeline coverage against target. For each: QTD actual, target, delta, pace (on/behind/ahead given days elapsed). One paragraph on what moved this week.
cost $0.0258 · 27s · in 15,513 / out 3,766 tok
✓ metrics_present✓ deltas✓ coverage_ratio✓ pace✓ paragraph

✓ Correct Answer (ground truth)

days_elapsed: 66
days_in_quarter: 92
elapsed_fraction: 0.7174
metrics: {"SQMs": {"qtd_actual": 230, "target": 300, "delta": -70, "expected_at_day_elapsed": 215.22, "pace": "ahead"}, "SQOs": {"qtd_actual": 84, "target": 120, "delta": -36, "expected_at_day_elapsed": 86.09,
coverage_ratio_active_pipeline: 0.75
pace_rule: expected = target * days_elapsed/days_in_quarter; within +/-5% of expected = on, above = ahead, below = behind; lower_better metrics invert.
this_week_paragraph_required: True
graded checks: metrics_present · deltas · coverage_ratio · pace · paragraph

Model's actual answer

show the model's full answer
WEEKLY MARKETING GOALS UPDATE (Q3-2026)
Quarter Progress: Day 66 of 92 elapsed (66 / 92 = 71.74%)

============================================================
GOAL PERFORMANCE & PACING
============================================================

1. SQMs (Sales Qualified Meetings)
- QTD Actual: 230
- Target: 300
- Delta: 230 - 300 = -70
- Pace: Ahead
  Arithmetic: Expected pace = 300 * (66 / 92) = 215.22. Actual (230) > Expected (215.22); 76.67% of target achieved vs 71.74% quarter elapsed.

2. SQOs (Sales Qualified Opportunities)
- QTD Actual: 84
- Target: 120
- Delta: 84 - 120 = -36
- Pace: Behind
  Arithmetic: Expected pace = 120 * (66 / 92) = 86.09. Actual (84) < Expected (86.09); 70.00% of target achieved vs 71.74% quarter elapsed.

3. DS2s
- QTD Actual: 40
- Target: 75
- Delta: 40 - 75 = -35
- Pace: Behind
  Arithmetic: Expected pace = 75 * (66 / 92) = 53.80. Actual (40) < Expected (53.80); 53.33% of target achieved vs 71.74% quarter elapsed.

4. Closed-Lost MIA Rate
- QTD Actual: 0.20 (20.00%)
  Arithmetic: closed_lost_mia / closed_lost_total = 5 / 25 = 0.20
- Target: 0.10 (10.00%, lower_better)
- Delta: 0.20 - 0.10 = +0.10 (+10.00 percentage points)
- Pace: Behind
  Arithmetic: Direction is lower_better; actual (20.00%) exceeds target ceiling (10.00%) by 10.00 percentage points.

5. Same-Quarter Close Count
- QTD Actual: 10
- Target: 20
- Delta: 10 - 20 = -10
- Pace: Behind
  Arithmetic: Expected pace = 20 * (66 / 92) = 14.35. Actual (10) < Expected (14.35); 50.00% of target achieved vs 71.74% quarter elapsed.

6. Active Pipeline Coverage Against Target
- QTD Actual: $3,000,000
- Target: $4,000,000
- Delta: $3,000,000 - $4,000,000 = -$1,000,000
- Pipeline Coverage: $3,000,000 / $4,000,000 = 0.75x (75.00%)
- Pace:
  * Linear time pace: Ahead (Expected pacing = $4,000,000 * (66 / 92) = $2,869,565.22; actual $3,000,000 > $2,869,565.22, or 75.00% vs 71.74% quarter elapsed).
  * Absolute target coverage: Behind (75.00% coverage vs 100.00% full-quarter target, delta -$1,000,000).

============================================================
WHAT MOVED THIS WEEK
============================================================
Data on weekly movements is missing from the provided files. The dataset includes only cumulative QTD actuals (marketing_qtd.csv), target definitions (targets.csv), and quarter timing (quarter_meta.csv), with no prior-week snapshots, historical baselines, or weekly change logs provided. Consequently, specific week-over-week deltas or pipeline movements cannot be computed without inventing data.
communication
ceo-slack-compression0.80

? The Question

Compress the forecast analysis below into a single Slack message for the CEO. Rules: at most three sentences; under 80 words; lead with the decision or the so-what; keep every distinct number that matters and the one caveat that changes the conclusion; cut any sentence that does not carry a claim, a number, or a directive. No em dashes, no throat-clearing phrases.
cost $0.0287 · 26s · in 21,067 / out 3,208 tok
✓ max_3_sentences✓ under_80_words✓ weighted_present✗ caveat_present✓ no_em_dash

✓ Correct Answer (ground truth)

graded checks: max_3_sentences · under_80_words · weighted_present · caveat_present · no_em_dash

Model's actual answer

show the model's full answer
Q3 weighted forecast stands at 115,976.75, derived from 44,729 across 7 COMMIT deals plus 35% of 203,565 across 24 BEST_CASE deals (44,729 + 71,247.75 = 115,976.75). However, all 7 in-quarter COMMIT deals lack documented why-buys (empty on 71 of 86 total pipeline deals), making the entire 44,729 committed baseline unverified. Additionally, 32 deals totaling 227,575 sit pushed into October, including one excluded COMMIT deal (Deal-D348E1, 13,770).
stalled-deal-followup-emailPASS 1.00

? The Question

Write a follow-up email for this stalled deal. Under 120 words. Reference one specific prior touch from the engagement history, make one clear ask, and match the tone of the prior thread. No invented details.
cost $0.0234 · 24s · in 15,657 / out 3,097 tok
✓ word_cap✓ references_touch✓ single_ask

✓ Correct Answer (ground truth)

deal_alias: Deal-0D2F7A
amount: 5100
stage: DS3
owner: Alex Franklin
days_since_last_engagement: 31
word_cap: 120
must_reference: engagement_id=ENG-00002; date=2026-07-28; type=meeting; why=most substantive prior touch; concrete prospect reactions to milestone awards and points catalog
single_cta: True
tone: warm, concise, professional (matches prior thread)
allowed_facts: 14 items — e.g. Deal-0D2F7A, $5,100, DS3, Alex Franklin
allowed_new: ENG-00001, ENG-00002, ENG-00003, ENG-00004, ENG-00005
graded checks: word_cap · references_touch · single_ask

Model's actual answer

show the model's full answer
Missing data: Contact name, contact email, and company name are not provided in the dataset.

Arithmetic:
Total amount / seats: $5,100 / 150 seats = $34.00 per seat.
Email word count: 48 words (< 120 words).

Subject: Following up: 150-seat pricing recap for Deal-0D2F7A

Hi team,

Following up on my August 5 email recapping our July 28 demo and the $5,100 pricing for 150 seats. 

Do you have 15 minutes this week to discuss any questions on the proposal and next steps?

Best,
Alex Franklin
funnel-digest-paragraphPASS 1.00

? The Question

Write the weekly GTM digest: one paragraph per team (Marketing, Sales, CS, PLG) in Bonusly brand voice. Every number must trace to the extracts. No customer contact names, no employee performance commentary, no compensation. Four paragraphs, under 400 words total.
cost $0.0207 · 22s · in 15,464 / out 2,429 tok
✓ four_paragraphs✓ word_cap✓ teams

✓ Correct Answer (ground truth)

required_paragraphs: Marketing, Sales, CS, PLG
allowed_numbers: 16 items — e.g. 2, 3, 9, 08
word_cap: 400
banned: customer contact names, employee performance commentary, compensation
graded checks: four_paragraphs · word_cap · teams

Model's actual answer

show the model's full answer
Marketing generated 46 SQMs against a target of 52, reaching 88.5% attainment (46 / 52 = 0.8846) with a 6-SQM shortfall (52 - 46 = 6). The webinar channel delivered strong engagement, contributing 18 of the 46 SQMs (18 / 46 = 39.13%), while paid search efficiency held steady at 150 dollars per SQM. Overall marketing spend and conversion rates from SQM to opportunity were not provided.

Sales created 310000 dollars in new pipeline and locked in 3 same-quarter closes to fuel revenue momentum. The team advanced pipeline qualification by converting 14 SQOs and securing 9 DS2 meetings, yielding a DS2-to-SQO progression rate of 64.29% (9 / 14 = 0.6429). Pipeline targets, quota attainment, and average deal sizes were not provided.

Customer Success lifted team NPS to 61 and protected baseline retention by saving 2 renewals. Heading into next week, the team has 3 open red-flag accounts prioritized for proactive intervention and support. Churn rate benchmarks, total portfolio size, and revenue figures associated with saved and red-flag accounts were not provided.

PLG expanded top-of-funnel adoption with 412 new signups, achieving a 31% activation rate representing approximately 128 activated signups (412 * 0.31 = 127.72). Driving platform habit and culture impact, 38 companies reached the aha moment of 10 recognition gives, representing 9.22% of total signups (38 / 412 = 0.0922). Downstream conversion rates from activated signups to paid plans were not provided.
partner-digest-sectionPASS 1.00

? The Question

Write the partner digest for the period. Every partner in the roster appears once: active plays get status with numbers, quiet partners get one line. Partner-sourced pipeline count and amount must match the deal data. Nothing omitted, nothing added.
cost $0.0181 · 16s · in 15,690 / out 1,692 tok
✓ all_partners✓ pipeline_amount✓ quiet_one_line

✓ Correct Answer (ground truth)

roster: partner_name=Apex Rewards Co; status=active | partner_name=HRCloud Partners; status=active | partner_name=CultureBridge; status=active | partner_name=WorkWell Group; status=active | partner_name=Recogniq; status=quiet | partner_name=KudosWave; status=quiet … (+2 more)
per_partner_pipeline: {"Apex Rewards Co": {"count": 2, "amount": 275000}, "HRCloud Partners": {"count": 1, "amount": 140000}, "CultureBridge": {"count": 2, "amount": 135000}}
partner_sourced_pipeline: count=5; amount=550000
quiet_partners: Recogniq, KudosWave, PeopleFirst Advisors, TotalPerk
active_no_deals: WorkWell Group
rule: every roster partner named exactly once; quiet partners get one line; no partners outside roster.
graded checks: all_partners · pipeline_amount · quiet_one_line

Model's actual answer

show the model's full answer
Partner Digest (2026-08-24 to 2026-09-04)

Active Partners:

Apex Rewards Co
- Status: Active. Co-webinar locked for 09-15.
- Sourced Pipeline: 2 deals, $275,000
- Arithmetic: $180,000 (Deal-DDAAF2) + $95,000 (Deal-2CF33E) = $275,000
- Deal Details: Both deals logged with UTM Source = Partner, currently in DS1.

HRCloud Partners
- Status: Active. Integration referral closed security review and moved to DS2.
- Sourced Pipeline: 1 deal, $140,000
- Arithmetic: $140,000 (Deal-F1CDA5) = $140,000
- Deal Details: Deal-F1CDA5 logged with UTM Source = Partner, currently in DS2.

CultureBridge
- Status: Active. Lunch-and-learn produced two early-stage sourced opps.
- Sourced Pipeline: 2 deals, $135,000
- Arithmetic: $60,000 (Deal-096E1D) + $75,000 (Deal-067213) = $135,000
- Deal Details: Deal-096E1D ($60,000) and Deal-067213 ($75,000), both logged with UTM Source = Partner.

WorkWell Group
- Status: Active. Planning call booked for 09-09 to restart joint playbook in Q4.
- Sourced Pipeline: 0 deals, $0
- Arithmetic: $0 across 0 deals (no sourced deals this period).

Quiet Partners:

Recogniq: Quiet partner; no pipeline or activity recorded this period.
KudosWave: Quiet partner; no pipeline or activity recorded this period.
PeopleFirst Advisors: Quiet partner; no pipeline or activity recorded this period.
TotalPerk: Quiet partner; no pipeline or activity recorded this period.

Total Partner-Sourced Pipeline:
- Total Deal Count: 2 + 1 + 2 + 0 = 5 deals
- Total Amount Arithmetic: $180,000 + $95,000 + $140,000 + $60,000 + $75,000 = $550,000
rfp-answer-from-policyPASS 1.00

? The Question

Answer the 10 questionnaire items from the policy excerpts only. Cite the excerpt id for every answer. Where the excerpts do not answer the question, mark it unanswerable and say what document would; do not guess. Tone: vendor response, direct.
cost $0.0184 · 32s · in 15,668 / out 1,775 tok
✓ answered_count✓ unanswerable_flagged✓ citations✓ no_guess

✓ Correct Answer (ground truth)

answered_count: 7
answer_map: {"Q1": ["P1"], "Q2": ["P1"], "Q3": ["P2"], "Q4": ["P3"], "Q5": ["P4"], "Q6": ["P5"], "Q7": ["P6"]}
unanswerable: Q8, Q9, Q10
valid_citation_ids: P1, P2, P3, P4, P5, P6, P7, P8
rule: unanswerable items must be flagged and the document that would answer them named; do not guess.
graded checks: answered_count · unanswerable_flagged · citations · no_guess

Model's actual answer

show the model's full answer
Q1: Yes. All customer data is encrypted at rest using AES-256. [P1]

Q2: Yes. Data in transit is encrypted with TLS 1.2 or higher. [P1]

Q3: Yes. Bonusly maintains a SOC 2 Type II report, renewed annually, available to prospects under NDA. [P2]

Q4: Customer data is retained for 90 days after the duration of the contract, after which it is deleted from production systems. [P3]

Q5: The list is maintained at trust.bonusly.com. Customers receive 30 days notice before a new subprocessor is added. [P4]

Q6: Yes. SAML 2.0 single sign-on is supported and available on the Enterprise plan. [P5]

Q7: Production databases are backed up daily. Backups are encrypted and retained for 35 days. [P6]

Q8: Unanswerable from the provided excerpts. Relevant document: ISO 27001 Certificate / Information Security Compliance Summary.

Q9: Unanswerable from the provided excerpts. Relevant document: Service Level Agreement (SLA) / Master Services Agreement (MSA).

Q10: Unanswerable from the provided excerpts. Relevant document: Business Associate Agreement (BAA) / HIPAA Compliance Addendum.
ops-maintenance
skill-file-reconciliationPASS 1.00

? The Question

Reconcile the skill set below against its manifest. Find and report (with severity CRITICAL/WARNING/INFO and an action type TRIM_DESC, UPDATE_BODY, DELETE_SKILL, MERGE, or REVIEW): (1) any two skills whose ALWAYS-trigger phrases overlap/duplicate, (2) any circular delegation chain between skills (name it), (3) any delegation target that does not exist (dangling, name it), (4) any version conflict and which skill should survive, (5) how many manifest descriptions exceed 1,024 characters, (6) any hardcoded page ids, dates, or person names in skill bodies, (7) manifest drift in BOTH directions (files with no manifest row, and manifest rows with no file). Make one proposal per finding; rewrite nothing.
cost $0.1127 · 75s · in 85,670 / out 12,915 tok
✓ duplicate_trigger_found✓ circular_chain✓ dangling_target✓ oversized_desc_count✓ hardcoded_values✓ manifest_drift✓ severity_and_action

✓ Correct Answer (ground truth)

graded checks: duplicate_trigger_found · circular_chain · dangling_target · oversized_desc_count · hardcoded_values · manifest_drift · severity_and_action

Model's actual answer

show the model's full answer
RECONCILIATION REPORT: SKILL SET vs. MANIFEST

================================================================================
(1) OVERLAPPING / DUPLICATE ALWAYS-TRIGGER PHRASES
================================================================================

Finding 1A: comms-drafter <-> email-drafter
- Severity: CRITICAL
- Action: MERGE
- Details: Five verbatim trigger phrases overlap directly between comms-drafter and email-drafter:
  1. "write me an email"
  2. "draft a follow-up"
  3. "what should I say"
  4. "bump email"
  5. "contract nudge"
  Both skills also trigger on pasting an existing message/email for feedback, review, or rating.
- Proposal: Merge email-drafter into comms-drafter as a unified external communications drafting skill, and deprecate email-drafter.

Finding 1B: weekly-pipeline-report <-> pipeline-intelligence-report
- Severity: WARNING
- Action: TRIM_DESC
- Details: Three pipeline status triggers overlap between weekly-pipeline-report and pipeline-intelligence-report:
  1. "what's the pipeline look like" (pipeline-intelligence-report) vs. "what does pipeline look like" (weekly-pipeline-report)
  2. "pipeline update" (pipeline-intelligence-report) vs. "run the pipeline update" / "update the pipeline" (weekly-pipeline-report)
  3. "run the pipeline report" (pipeline-intelligence-report) vs. "do the pipeline report" / "generate the pipeline report" (weekly-pipeline-report)
- Proposal: Trim trigger descriptions in weekly-pipeline-report to restrict its scope strictly to demand generation pacing, business day cadence, and MTD/QTD SQM/SQO metrics, leaving macro pipeline health and scored tiering to pipeline-intelligence-report.

================================================================================
(2) CIRCULAR DELEGATION CHAIN
================================================================================

Finding 2: deal-strategy-coach <-> email-drafter
- Severity: WARNING
- Action: REVIEW
- Details: Circular delegation exists between deal-strategy-coach and email-drafter:
  - deal-strategy-coach delegates drafting to email-drafter: "When drafting manager-to-prospect emails, use the email-drafter skill which automatically retrieves your Gmail signature..."
  - email-drafter delegates diagnosis back to deal-strategy-coach: "If the user needs strategic deal coaching (stalled deal diagnosis, objection handling strategy, multithreading plans, forecast risk), point them to the deal-strategy-coach skill. If they need both strategy and a draft, do the draft here and suggest they use deal-strategy-coach for the deeper analysis."
  (Note: pipeline-intelligence-report delegates to closed-lost-analysis via Mode 4, but that chain is strictly one-way.)
- Proposal: Break the circular dependency by allowing deal-strategy-coach to draft directly with signature extraction or enforcing a one-way handoff without reciprocal referral.

================================================================================
(3) DANGLING DELEGATION TARGETS (NON-EXISTENT SKILLS)
================================================================================

Finding 3: References to Unmanifested / Missing Skills
- Severity: CRITICAL
- Action: UPDATE_BODY
- Details: Multiple skills instruct delegation to target skills that do not exist in the manifest or file set:
  1. bonusly-brand (referenced in comms-drafter, email-drafter, sales-forecast, and signalforge-claim-compressor)
  2. prospect-research-multithreading (referenced in comms-drafter, deal-strategy-coach, and email-drafter)
  3. skill-orchestrator (referenced in analysis-validator and signalforge-feedback)
  4. signalforge-reports (referenced in pipeline-intelligence-report and weekly-pipeline-report)
  5. Eight specialist skills in analysis-validator Section 12.4:
     - bonusly-data-questions
     - bonusly-product-questions
     - bonusly-business-reporting-questions
     - bonusly-rewards-questions
     - bonusly-ppp-questions
     - bonusly-feature-flag-questions
     - bonusly-deal-desk-questions
     - bonusly-datadog-questions
- Proposal: Update the bodies of referring skills to remove invalid skill invocations, substitute them with direct API/query instructions, or create the missing skill definitions in the repository.

================================================================================
(4) VERSION CONFLICT
================================================================================

Finding 4: analysis-validator (v3.5 vs. v3.6)
- Severity: WARNING
- Action: UPDATE_BODY
- Details: analysis-validator contains conflicting version declarations. The changelog lists both v3.5 and v3.6 released on the same date (May 9, 2026). The document header declares v3.6, the footer declares v3.6, but the Section 7 Validation Trail template hardcodes "Validator: analysis-validator v3.2".
- Surviving Skill: Version 3.6 survives, as it incorporates Gate 2-F (ID Resolution) while retaining Gate 1-L (Engagement Coverage Check) introduced in v3.5.
- Proposal: Update the Section 7 Validation Trail template string in analysis-validator from v3.2 to v3.6 to resolve the internal version discrepancy.

================================================================================
(5) MANIFEST DESCRIPTIONS EXCEEDING 1,024 CHARACTERS
================================================================================

Finding 5: Description Character Length Audit
- Severity: INFO
- Action: REVIEW
- Arithmetic:
  Total manifest rows evaluated: 14
  Length per declared skill:
  - analysis-validator: 656 chars
  - closed-lost-analysis: 897 chars
  - comms-drafter: 996 chars
  - deal-strategy-coach: 792 chars
  - email-drafter: 965 chars
  - model-selection: 676 chars
  - next-to-close: 945 chars
  - partner-digest: 1,004 chars
  - pipeline-intelligence-report: 1,006 chars
  - sales-forecast: 962 chars
  - signalforge-claim-compressor: 1,006 chars
  - signalforge-feedback: 708 chars
  - stale-pipeline-report: 762 chars
  - weekly-pipeline-report: 656 chars

  Maximum description length = 1,006 characters (pipeline-intelligence-report, signalforge-claim-compressor).
  Comparison: 1,006 <= 1,024.
  Total manifest descriptions exceeding 1,024 characters: 0 (zero).
- Proposal: Maintain current descriptions as all 14 manifest entries are compliant with the 1,024-character threshold.

================================================================================
(6) HARDCODED PAGE IDS, DATES, AND PERSON NAMES
================================================================================

Finding 6: Embedded Hardcoded Identifiers Across Skill Bodies
- Severity: WARNING
- Action: UPDATE_BODY
- Details:
  1. Confluence / Cloud / External IDs:
     - Cloud ID: 73fe98de-a4a3-4869-9f8a-bb1eeed4cf7f (partner-digest, sales-forecast, signalforge-feedback)
     - Space IDs: 1958248479 (partner-digest), 2232811524 (sales-forecast, signalforge-feedback)
     - Page & Folder IDs: 2286616609, 2286321666, 2265382925, 2236940297, 2237825028, 2239365136, 2238283777 (partner-digest); 2257879045 (deal-strategy-coach); 2232582148 (sales-forecast); 2295136266, 2234417154, 2247295002 (signalforge-feedback)
     - Google Sheet IDs: 1CLZeOsElVDF_LF0ZG_t2nfwvhnZ6bpwqM_nX3WEYzcw, 1ENuaEcCuLjdKhMvp8FK3Ys1ek5Aw9ZuOZhsHJJFoB_k (weekly-pipeline-report)
     - HubSpot Portal ID: 1973303 (pipeline-intelligence-report, next-to-close, stale-pipeline-report)
     - Slack IDs: Channel C0561C1JCPJ (stale-pipeline-report), User <@U03QLMBL7AR> (partner-digest)
     - HubSpot Stage IDs: 150582536 (DS1), 150582537 (DS2), 150582538 (DS3), 150582539 (DS4), 1175632767 (DS5) (analysis-validator, deal-strategy-coach, next-to-close, pipeline-intelligence-report, stale-pipeline-report, weekly-pipeline-report)
  2. Dates & Historical Snapshots:
     - Static dates: March 28, 2023; May 4, 2026; May 9, 2026; May 16, 2026; May 19, 2026; April 26, 2026; April 27, 2026; 2026-06-10; Q1 2026; Q2 2026 (April 1 - June 30, 2026)
     - Static benchmarks & actuals: Q1 2026 actuals ($365,152 vs $475,000; $2,490,532 vs $3,288,000 in weekly-pipeline-report); Pricing - 2026 matrix (deal-strategy-coach); population anchors ~452K / ~110,097 (analysis-validator)
  3. Person Names & Roster IDs:
     - Core AEs: Bryce Harmon (119337721), Hugo Lindqvist (77260721), Dana Mercer (83155923), Alex Franklin (84342457), Cole Ingram (83155924), Gavin Porter (1520255671) (analysis-validator, pipeline-intelligence-report)
     - CSMs: Colleen Perry (77938470), Ellie Barton (79580306), Ashley Reyer (81969994), Megan Franz (321546903), Elena Sinclair (701163055), Youssef Elkhateeb (725397794), Amanda Czenkus (1556884388) (analysis-validator)
     - Leadership & Ops: Alaina Loori (82535637), Shealagh Coughlin (119069206), Ben Castelli (348210196), Amani Phipps (210200121), John Thomas (78303262), Yasmin Wahid (89062643), Manish, Ben Lavin, Farid, Perseus, Elena (analysis-validator, sales-forecast, weekly-pipeline-report, deal-strategy-coach)
     - External Partner Reps: Kelli, Jen Lee, Hani, Bryce, Sara (partner-digest)
- Proposal: Update skill bodies to dynamically resolve rosters, dates, and page targets via live API queries (e.g., HubSpot owner search, relative date calculations), moving static constants into dedicated configuration tables.

================================================================================
(7) MANIFEST DRIFT IN BOTH DIRECTIONS
================================================================================

Finding 7: Manifest vs. File Set Parity
- Severity: INFO
- Action: REVIEW
- Arithmetic:
  - Total files in manifest: 14
  - Total .SKILL.md files provided: 14
  - Files with no manifest row: 14 - 14 = 0 (none)
  - Manifest rows with no file: 14 - 14 = 0 (none)
  Parity check: Exactly 14 manifest entries match 14 files on disk (1:1 correspondence).
- Proposal: Maintain existing parity and enforce automated CI file-to-manifest validation checks on future repository updates.
runbook-from-slack-threadPASS 1.00

? The Question

Turn the resolved incident thread into a runbook: ordered steps with the exact command or action taken, who ran it, how success was verified, and rollback for each step that changed state. Every step must trace to a message in the thread. Add nothing the thread does not contain; where the thread is unclear, mark the step as needing confirmation.
cost $0.0281 · 32s · in 15,920 / out 4,300 tok
✓ ordered_steps✓ rollback✓ needs_confirmation✓ sourced

✓ Correct Answer (ground truth)

steps: order=1; msg=M01; actor=Bryce Harmon; action=acknowledge PagerDuty alert, take IC; state_change=False; verified_by=M01 | order=2; msg=M02; actor=Farid Osman; action=check queue depth; command=bundle exec rake sidekiq:queue_depth; state_change=False; verified_by=M02 (48,213 pending) | order=3; msg=M04; actor=Farid Osman; action=pause auto-enqueue via feature flag; command=bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'; state_change=True; rollback=bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'; verified_by=M07 (queue depth falling) | order=4; msg=M05; actor=Elena Sinclair; action=cleared the dead set; state_change=True; needs_confirmation=thread gives no command or method for clearing the dead set | order=5; msg=M06; actor=Bryce Harmon; action=scale reward-worker to 6 replicas; command=kubectl scale deployment/reward-worker --replicas=6; state_change=True; rollback=kubectl scale deployment/reward-worker --replicas=3; verified_by=M07/M08 | order=6; msg=M08; actor=Cole Ingram; action=verify queue drained and error rate baseline; command=bundle exec rake sidekiq:queue_depth; state_change=False; verified_by=M08 (returns 0) … (+2 more)
ambiguous_step: order=4; msg=M05; mark=needs-confirmation
rule: no steps beyond what the thread contains; every step traces to a message id.
graded checks: ordered_steps · rollback · needs_confirmation · sourced

Model's actual answer

show the model's full answer
INCIDENT RUNBOOK: #incident-2026-08-29-reward-queue

Step 1
- Thread Trace: [M01]
- Action Taken: Acknowledged PagerDuty alert for reward-worker queue depth > 10k; assumed Incident Commander (IC) role.
- Executed By: Bryce Harmon
- Verification: Not stated in thread (needs confirmation).
- State Change: No.
- Rollback: Not applicable.

Step 2
- Thread Trace: [M02]
- Exact Command / Action Taken: bundle exec rake sidekiq:queue_depth
- Executed By: Farid Osman
- Verification: Command output reported reward queue at 48,213 pending jobs (normal is under 500).
- State Change: No (read-only).
- Rollback: Not applicable.

Step 3
- Thread Trace: [M03]
- Action Taken: Checked Sidekiq dead set (exact command not specified in thread; needs confirmation).
- Executed By: Farid Osman
- Verification: Identified 112 jobs in dead set, all with Redis::TimeoutError from around 13:58.
- State Change: No (read-only).
- Rollback: Not applicable.

Step 4
- Thread Trace: [M04]
- Exact Command / Action Taken: bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'
- Executed By: Farid Osman
- Verification: Not stated in message [M04] (needs confirmation; subsequent queue drop reported in [M07]).
- State Change: Yes.
- Rollback: bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'

Step 5
- Thread Trace: [M05]
- Action Taken: Cleared out the dead set from the console (exact command not specified in thread; needs confirmation).
- Executed By: Elena Sinclair
- Verification: Not stated in thread (needs confirmation).
- State Change: Yes.
- Rollback: Not provided in thread (needs confirmation).

Step 6
- Thread Trace: [M06]
- Exact Command / Action Taken: kubectl scale deployment/reward-worker --replicas=6
- Executed By: Bryce Harmon
- Verification: Not stated in message [M06] (needs confirmation; queue drain reported in [M07]).
- State Change: Yes (scaled from 3 to 6 replicas).
- Rollback: kubectl scale deployment/reward-worker --replicas=3

Step 7
- Thread Trace: [M07]
- Action Taken: Checked queue depth and drain rate (exact command not specified in thread; needs confirmation).
- Executed By: Farid Osman
- Verification: Observed queue depth at 9,400 and falling at ~1,200/min.
- State Change: No (read-only).
- Rollback: Not applicable.

Step 8
- Thread Trace: [M08]
- Exact Command / Action Taken: bundle exec rake sidekiq:queue_depth (and checked Datadog error rate).
- Executed By: Cole Ingram
- Verification: bundle exec rake sidekiq:queue_depth returned 0; error rate in Datadog confirmed back to baseline.
- State Change: No (read-only).
- Rollback: Not applicable.

Step 9
- Thread Trace: [M09]
- Exact Command / Action Taken: bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'
- Executed By: Bryce Harmon
- Verification: Verified 40 new jobs processed cleanly in the next 3 minutes.
- State Change: Yes.
- Rollback: bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)' (defined in [M04]; not explicitly stated in [M09], needs confirmation).

Step 10
- Thread Trace: [M10]
- Exact Command / Action Taken: kubectl scale deployment/reward-worker --replicas=3
- Executed By: Bryce Harmon
- Verification: Queue confirmed stable at 0; incident resolved.
- State Change: Yes (scaled from 6 to 3 replicas).
- Rollback: kubectl scale deployment/reward-worker --replicas=6 (not explicitly stated in [M10], needs confirmation).
datadog-incident-timelinePASS 1.00

? The Question

From the log slice, sequence the failure: first error with timestamp, the cascade in order, the service and job involved, and the Datadog query that would confirm the first error. State what the logs do not show.
cost $0.0242 · 22s · in 17,315 / out 2,997 tok
✓ first_error_ts✓ service_named✓ cascade_ordered✓ query✓ gaps_stated

✓ Correct Answer (ground truth)

first_error: timestamp=2026-09-03T14:01:12Z; service=reward-service; message=Redis::TimeoutError: Connection to Redis at redis-primary:6379 timed out after 5s
cascade_order: service=reward-service; first_error_at=2026-09-03T14:01:12Z | service=sidekiq; first_error_at=2026-09-03T14:01:40Z | service=api-gateway; first_error_at=2026-09-03T14:03:05Z | service=web-app; first_error_at=2026-09-03T14:03:30Z
job_class_involved: RewardGiveJob
confirming_query: service:reward-service status:error "@message:Redis::TimeoutError"
logs_do_not_show: root cause of the Redis timeout, redis-server's own logs, infrastructure metrics (CPU/memory/network), any deploy or config change preceding the incident, who applied mitigation or when (no actor fields in logs)
clean_services: postgres
graded checks: first_error_ts · service_named · cascade_ordered · query · gaps_stated

Model's actual answer

show the model's full answer
FIRST ERROR WITH TIMESTAMP:
- Timestamp: 2026-09-03T14:01:12Z
- Service: reward-service
- Log Level: ERROR
- Message: Redis::TimeoutError: Connection to Redis at redis-primary:6379 timed out after 5s

CASCADE SEQUENCE IN ORDER:
1. 2026-09-03T14:01:12Z (reward-service): Connection to Redis at redis-primary:6379 times out after 5s.
2. 2026-09-03T14:01:20Z to 14:01:40Z (reward-service): Retries exhausted for RewardGiveJob (logged at 14:01:20Z, 14:01:30Z, 14:01:40Z).
3. 2026-09-03T14:01:40Z to 14:02:28Z (sidekiq): Sidekiq reports RewardGiveJob failed due to Redis::TimeoutError and initiates retries.
4. 2026-09-03T14:02:30Z (sidekiq): Queue backlog builds up; sidekiq logs WARN "Queue reward depth above 10,000".
5. 2026-09-03T14:03:05Z (api-gateway): Upstream calls fail; api-gateway logs ERROR "502 upstream timeout calling reward-service /gives" (with recurring 502s logged at 14:03:48Z, 14:04:13Z, 14:05:16Z, and 14:06:52Z).
6. 2026-09-03T14:03:30Z (web-app): User-facing failures begin; web-app logs ERROR "Give form submission failed: upstream 502 from api-gateway" (with subsequent errors at 14:04:45Z, 14:05:42Z, and 14:06:49Z).
7. 2026-09-03T14:22:10Z (reward-service): Recovery begins; reward-service logs INFO "Redis connection restored; resuming job processing".
8. 2026-09-03T14:24:45Z (sidekiq): Backlog clears; sidekiq logs INFO "Queue reward depth below 500".

SERVICES INVOLVED:
- reward-service (originating failure, upstream timeout target)
- sidekiq (background job runner, queue buildup)
- api-gateway (proxy encountering upstream 502 timeouts)
- web-app (client-facing application experiencing form submission failures)
- Note: postgres logged INFO checkpoints (14:10:56Z to 14:20:59Z) without error; redis-primary:6379 is the targeted endpoint, but has no direct service logs in this dataset.

JOBS INVOLVED:
- RewardGiveJob (logged in datadog_logs.csv and sidekiq_jobs.csv: jobs J-00001 through J-00012)
- RecognitionDigestJob (logged in sidekiq_jobs.csv: jobs J-00013 through J-00016)

ARITHMETIC:
- Outage duration (first error to Redis connection restored):
  14:22:10Z - 14:01:12Z = 20 minutes, 58 seconds (1,258 seconds).
- Time from first error to queue recovery (<500 depth):
  14:24:45Z - 14:01:12Z = 23 minutes, 33 seconds (1,413 seconds).
- Time from first error to first api-gateway 502 timeout:
  14:03:05Z - 14:01:12Z = 1 minute, 53 seconds (113 seconds).
- Time from first error to first web-app form failure:
  14:03:30Z - 14:01:12Z = 2 minutes, 18 seconds (138 seconds).
- Failed jobs count in sidekiq_jobs.csv:
  12 RewardGiveJob failures + 4 RecognitionDigestJob failures = 16 failed job records total.

DATADOG QUERY TO CONFIRM THE FIRST ERROR:
service:reward-service status:error "Redis::TimeoutError: Connection to Redis at redis-primary:6379 timed out after 5s"

WHAT THE LOGS DO NOT SHOW:
- Root cause of the Redis outage (no direct logs from redis-primary or infrastructure metrics showing why the 5s timeout occurred).
- Customer/user identifiers, account names, deal records, or company aliases (none are provided in the dataset).
- Total volume of end-user form submissions lost or impacted.
- Whether the 16 failed Sidekiq jobs succeeded automatically after 14:22:10Z or were dead-lettered.
feature-flag-state-summaryPASS 1.00

? The Question

Summarize the state of the listed feature flags: what each controls per the code excerpt, which companies or segments have it on, targeting rules, and any flag with no code reference or no targets. Company counts must match the export. Do not describe a flag that is not in the export.
cost $0.0255 · 23s · in 15,751 / out 3,645 tok
✓ orphans✓ no_targets✓ counts_match✓ no_invented

✓ Correct Answer (ground truth)

flags: {"recognition_streaks_v2": {"state": "on", "targeting_rules": "segment:beta_companies", "company_count": 42}, "points_budget_guardrails": {"state": "on", "targeting_rules": "all_companies", "company_c
code_referenced: recognition_streaks_v2, points_budget_guardrails, slack_dm_nudges, redeem_flow_redesign, analytics_dashboard_v3, ms_teams_app_v2
orphan_flags_no_code_reference: legacy_give_modal, survey_boosters_q3, paused_offboard_cleanup
flags_with_no_targets: paused_offboard_cleanup
rule: company counts must equal the export; do not describe flags absent from the export.
graded checks: orphans · no_targets · counts_match · no_invented

Model's actual answer

show the model's full answer
FEATURE FLAG STATUS SUMMARY

1. recognition_streaks_v2
- State: on
- Targeting Rules: segment:beta_companies
- Companies/Segments On: segment:beta_companies (company count: 42)
- Company Count: 42
- Code Implementation: app/models/recognition.rb
  Controls recording streaks: executes StreakTracker.record(give) when enabled.

2. points_budget_guardrails
- State: on
- Targeting Rules: all_companies
- Companies/Segments On: all_companies (company count: 220)
- Company Count: 220
- Code Implementation: app/services/budget_service.rb
  Controls budget enforcement: executes BudgetService.new(company).enforce!(giver, points) when enabled.

3. slack_dm_nudges
- State: on
- Targeting Rules: segment:region_na
- Companies/Segments On: segment:region_na (company count: 87)
- Company Count: 87
- Code Implementation: app/jobs/nudge_job.rb
  Controls sending Slack nudges: returns unless enabled; executes SlackDm.send_nudge(user).

4. redeem_flow_redesign
- State: off
- Targeting Rules: targeted_list
- Companies/Segments On: None (state is off; targeted_list has company count: 12)
- Company Count: 12
- Code Implementation: app/controllers/redeem_controller.rb
  Controls redemption UI: renders RedeemV2Component.new(company) if enabled; renders RedeemV1Component.new(company) if disabled.

5. analytics_dashboard_v3
- State: on
- Targeting Rules: segment:tier_three
- Companies/Segments On: segment:tier_three (company count: 65)
- Company Count: 65
- Code Implementation: app/controllers/analytics_controller.rb
  Controls dashboard initialization: sets @dashboard = AnalyticsV3.new(company) when enabled.

6. ms_teams_app_v2
- State: off
- Targeting Rules: targeted_list
- Companies/Segments On: None (state is off; targeted_list has company count: 9)
- Company Count: 9
- Code Implementation: app/services/teams_installer.rb
  Controls app installation: executes TeamsAppV2.install(company) when enabled.

7. legacy_give_modal
- State: off
- Targeting Rules: segment:legacy_plan
- Companies/Segments On: None (state is off; segment:legacy_plan has company count: 14)
- Company Count: 14
- Code Implementation: Missing (no code reference in flag_code.md).

8. survey_boosters_q3
- State: on
- Targeting Rules: segment:legacy_plan
- Companies/Segments On: segment:legacy_plan (company count: 7)
- Company Count: 7
- Code Implementation: Missing (no code reference in flag_code.md).

9. paused_offboard_cleanup
- State: off
- Targeting Rules: Missing / blank
- Companies/Segments On: None (state is off; company count: 0)
- Company Count: 0
- Code Implementation: Missing (no code reference in flag_code.md).


EXCEPTIONS SUMMARY

Flags with no code reference:
- legacy_give_modal
- survey_boosters_q3
- paused_offboard_cleanup

Flags with no targets:
- paused_offboard_cleanup (company count is 0; targeting_rules field is blank/empty).


ARITHMETIC & TOTALS
- Total flags in export: 9
- Flags with state 'on': 5 (recognition_streaks_v2, points_budget_guardrails, slack_dm_nudges, analytics_dashboard_v3, survey_boosters_q3)
- Flags with state 'off': 4 (redeem_flow_redesign, ms_teams_app_v2, legacy_give_modal, paused_offboard_cleanup)
- Sum of company counts for 'on' flags: 42 + 220 + 87 + 65 + 7 = 421
- Sum of company counts for 'off' flags: 12 + 9 + 14 + 0 = 35
- Total company count across all rows: 421 + 35 = 456
nda-triagePASS 1.00

? The Question

Triage each NDA as GREEN (standard approval), YELLOW (counsel review), or RED (full legal review). Cite the clause that drives each classification by quoting it. Catch embedded non-solicits, non-competes, missing carve-outs, and unusual term or governing law. One line of reasoning per NDA.
cost $0.0169 · 15s · in 15,714 / out 1,358 tok
✓ green✓ yellow✓ red✓ non_solicit_caught✓ clause_quoted✓ correct_labels

✓ Correct Answer (ground truth)

classifications: {"NDA-1": {"classification": "GREEN", "driving_reason": "standard mutual NDA: 2-year term, standard carve-outs, Delaware law", "driving_clause_quote": "Carve-outs: information that (a) is or becomes p
non_solicit_in: NDA-2
checks: classification_per_nda, clause_quotes_exist_in_document, non_solicit_caught, carveouts_checked
graded checks: green · yellow · red · non_solicit_caught · clause_quoted · correct_labels

Model's actual answer

show the model's full answer
**NDA-1 — [PARTY A] and Bonusly**
- **Classification:** GREEN (standard approval)
- **Driving Clause:** "1. Mutual confidentiality. Each party may disclose Confidential Information to the other for the purpose of evaluating a business relationship."
- **Reasoning:** Standard mutual agreement with standard carve-outs, Delaware governing law, a reasonable 2-year term with 3-year survival, and no restrictive covenants.

**NDA-2 — [PARTY B] and Bonusly**
- **Classification:** YELLOW (counsel review)
- **Driving Clause:** "4. Non-solicitation: 'During the term of this Agreement and for eighteen (18) months thereafter, neither party shall solicit for employment or hire any employee of the other party with whom it came into contact under this Agreement.'"
- **Reasoning:** Contains an embedded non-solicitation and no-hire covenant extending 18 months past termination that requires counsel review before approval.

**NDA-3 — [PARTY C] and Bonusly**
- **Classification:** RED (full legal review)
- **Driving Clause:** "2. Non-compete: 'For a period of three (3) years following the Effective Date, Recipient shall not, directly or indirectly, engage in or provide services to any business that competes with Discloser's business.'"
- **Reasoning:** Imposes a severe 3-year non-compete, lacks all standard confidentiality carve-outs, establishes one-way obligations with an unusually long 5-year term, and mandates foreign governing law and exclusive jurisdiction in the Republic of Ireland.