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qwen

qwen/qwen3.7-flash

Mean 0.906 · 27/40 perfect tests · $0.08 total · each card: the question → the correct answer (gold) → this model's actual answer

data-crm
stage-hygiene-auditPASS 1.00

? 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 $0.0097 · 266s · in 121,376 / out 44,466 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

show the model's full answer
Here are the audit results:

=== DEFINITIONS ===

**Why-buys substantive**: value is not blank and not a placeholder (tbd, n/a, see notes, pending, unknown); LONG(n) counts as substantive; minimum length 15 characters of actual text.

**Regressed**: any pair of stage-entry timestamps where an earlier-stage date is chronologically after a later-stage date (e.g. t_ds2 > t_ds1).

**t_ds1 required**: every open deal must have a DS1 entry timestamp.

=== RESULTS ===

Stage distribution (156 total): DS1=32, DS2=39, DS3=61, DS4=14, DS5=10.

DS2+ deals: 124 (all except the 32 DS1 deals).

-- WHY-BUYS COMPLIANCE (DS2+) --
Compliant: 23 / 124 = 18.5%
Non-compliant: 101 / 124 = 81.5%

By owner:
  Bryce Harmon:   3/18 compliant (16.7%) | 15/18 non-compliant
  Dana Mercer:   4/22 compliant (18.2%) | 18/22 non-compliant
  Alex Franklin:13/61 compliant (21.3%)| 48/61 non-compliant
  Cole Ingram: 3/22 compliant (13.6%) | 19/22 non-compliant
  Farid Osman:  0/1 compliant (0.0%)   | 1/1 non-compliant

Every non-compliant deal had a blank why_buys field -- none contained placeholders, truncated LONG() entries, or short text.

-- MISSING t_ds1 TIMESTAMP --
Deals with no t_ds1: 0 / 156 (0%). Every deal has a DS1 entry timestamp.

-- REGRESSION DETECTION --
Regressed deals: 0 / 156 (0%). No deal had an earlier-stage timestamp later than a later-stage timestamp.

=== THREE PROCESS FIXES ===

1. **Gate stage advancement behind why_buys entry.** Make the why_buys field mandatory (15+ chars, rejecting blanks/placeholders/TBD/N/A) at the moment a rep tries to move a deal out of DS1. This is where the failure is concentrated: 81.5% of DS2+ deals have no why-buys recorded, meaning reps advance deals through the pipeline without documenting rationale.

2. **Add a weekly pipeline quality report run against this exact logic.** A scheduled script that flags DS2+ deals missing substantive why_buys, assigns the owner and their manager, and creates remediation tasks. Without automated enforcement, manual cleanup is the only recourse -- which is what we're doing now, reactively.

3. **Make t_ds1 auto-populate from create_date on new deal creation.** While currently all 156 deals have t_ds1, the fact that the field exists separately from create_date creates a gap where it can be overlooked. Defaulting t_ds1 = create_date eliminates the risk going forward and frees reps from redundant admin work.
owner-resolution0.83

? 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 $0.0016 · 24s · in 37,946 / out 1,837 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

show the model's full answer
Here are the results:

---

**Deals resolved: 156 out of 156**

All 6 unique owner IDs in the deals file have a matching entry in the owners table. Zero unmatched IDs.

---

**Unmatched owner IDs: None**

No deal row references an owner ID that is missing from the owners table.

---

**Archived/deactivated owners with deals: None**

Two owners in the owners table are archived (Gavin Porter, 1520255671; Hugo Lindqvist, 77260721), but neither has any open deals. Every owner ID on every deal row maps to an active owner.

---

**Total pipeline per resolved owner:**

| Owner ID | Name | # Deals | Total Pipeline |
|---|---|---|---|
| 119337721 | Bryce Harmon | 35 | $1,054,144.00 |
| 84342457 | Alex Franklin | 67 | $624,310.00 |
| 83155923 | Dana Mercer | 24 | $341,195.00 |
| 83155924 | Cole Ingram | 22 | $288,161.43 |
| 716654662 | Farid Osman | 7 | $4,134.00 |
| 701163055 | Elena Sinclair | 1 | $2,100.00 |
| | **TOTAL** | **156** | **$2,313,944.43** |

Note: Cole Ingram's total includes several fractional-dollar amounts (e.g., $7225.40, $7781.20, $6947.50, $2480.40, $4779.88, $3334.80, $58529.25) sourced from organic/other-campaigns deals.
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 $0.0024 · 31s · in 53,578 / out 2,419 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

show the model's full answer
Here are the three findings:

---

**(a) Orphan count — gong rows with no matching open deal**

30 of 67 gong rows (44.8%) reference a HubSpot deal_id that is **not in deals_open**. These represent closed/won, closed/lost, or otherwise exited opportunities that still have call history but no active pipeline presence. Together they account for **304 calls** in the last 90 days.

Sample orphan aliases (sorted by calls desc):
- Deal-8FA85D (46 calls) — Diversified Financial Services
- Deal-8FC3F9 (24 calls) — Consumer Durables & Apparel
- Deal-3B7945 (21 calls) — Pharmaceuticals, Biotechnology & Life Sciences
- Deal-42B265 (21 calls) — Software & Services
- Deal-9CCC42 (17 calls) — Food, Beverage & Tobacco
- Deal-36EA09 (17 calls) — Software & Services
- Deal-9A43B4 (14 calls) — Software & Services
- Deal-605F3C (12 calls) — Software & Services
- Deal-76821A (11 calls) — Software & Services
- Deal-D84A2D (9 calls) — Software & Services

Full list: Deal-8FA85D, Deal-8FC3F9, Deal-3B7945, Deal-42B265, Deal-9CCC42, Deal-36EA09, Deal-9A43B4, Deal-605F3C, Deal-E2D34B, Deal-76821A, Deal-D84A2D, Deal-1A0416, Deal-228783, Deal-3F86A0, Deal-9897FA, Deal-422BA6, Deal-344163, Deal-B038F0, Deal-5CA5AF, Deal-D3BD1C, Deal-5592CC, Deal-1E8CFB, Deal-AC944F, Deal-DECCF3, Deal-51EA1A, Deal-38CA53, Deal-32088A, Deal-7C4130, Deal-C00480, Deal-3B6668.

---

**(b) Duplicate conversation keys — calls_90d > distinct_conversation_keys**

**None.** Zero rows have `calls_90d` exceeding `distinct_conversation_keys`. In every row the two values are equal — meaning each call maps to one unique conversation key, and there's no evidence of duplicated or collapsed conversation keys. Note this also means there's no case where multiple calls share the same conversation ID; the data is internally consistent on this dimension.

---

**(c) Share of DS3+ open deals with at least one logged call**

Out of **85 open deals** at stage DS3 or later (DS3, DS4, or DS5), **25 have >= 1 call** in the Gong log and **60 have zero calls**.

Share with at least one call: **25/85 = 29.4%** (roughly 3 in 10).

Sample DS3+ deals with NO logged calls:
- Deal-9AAE5F (DS4, $11,250)
- Deal-403845 (DS5, $9,000)
- Deal-6787C2 (DS4, $7,000)
- Deal-2465CE (DS5, $5,400)
- Deal-62D607 (DS4, $4,800)
- Deal-7B3B0F (DS4, $2,760)
- Deal-F9A08A (DS4, $2,484)
- Deal-0660B4 (DS4, $1,920)
- Deal-1FC049 (DS4, $1,920)
- Deal-FD9F4E (DS5, $1,330)
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 $0.0003 · 23s · in 1,507 / out 1,189 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
/*
 * Per-company first-calendar-month metrics:
 *   unique_givers        -> PLG.COMPANY_COHORT_SUMMARY.M1_UNIQUE_GIVERS (only source listing
 *                            distinct giver counts; REDEMPTION_RECORDS_V2 lacks giver identity).
 *   recognition_count    -> PLG.COMPANY_COHORT_SUMMARY.M1_GIVES     (gives in M1).
 *   successful_redemption-> REDEMPTION_RECORDS_V2 WHERE STATE = 'succeeded' COUNTED back to M1
 *                            via ACTIVATED_DATE truncation (the catalog's documented redemption
 *                            source despite DEPRECATED schema name — confirmed by catalog note).
 */
SELECT
    ccs.ALIAS                                           AS company_alias,
    TO_CHAR(DATE_TRUNC('MONTH', ccs.ACTIVATED_DATE), 'YYYY-MM')  AS first_calendar_month,
    ccs.M1_UNIQUE_GIVERS                                AS unique_givers,
    COALESCE(rc.redemption_count, 0)                    AS recognition_count,
    ccs.M1_REDEMPTIONS                                  AS successful_redemption_count

FROM PRODUCTION.PLG.COMPANY_COHORT_SUMMARY AS ccs

LEFT JOIN (
    /*
     * REDEMPTION_RECORDS_V2 – only 'succeeded' rows count per business rules.
     * Grouped to ACTIVATED_DATE-equivalent month of the company (ACTIVATED_DATE from
     * COMPANY_COHORT_SUMMARY is joined above); here we pre-aggregate redemptions per
     * approximate activation month for the LEFT JOIN.
     */
    SELECT
        DATE_TRUNC('MONTH', CREATED_AT)::DATE                        AS act_month,
        COUNT(*)                                                     AS redemption_count
    FROM PRODUCTION.DEPRECATED_RECOGNITION.REDEMPTION_RECORDS_V2
    WHERE STATE = 'succeeded'                                        /* only succeeded redemptions */
    GROUP BY DATE_TRUNC('MONTH', CREATED_AT)
) AS rc
    ON DATE_TRUNC('MONTH', ccs.ACTIVATED_DATE) = rc.act_month

ORDER BY company_alias, first_calendar_month;
```
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 $0.0095 · 378s · in 112,068 / out 34,223 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

show the model's full answer
Audit script ran clean. Here are the key findings from the actual data:

---

COMPLETENESS SUMMARY

COMPANIES (34 rows):
- domain:     34/34 (100%) -- perfect
- industry:   34/34 (100%) -- but 9 non-canonical value variants
- employee_count: 25/34 (73.5%) -- 9 blank: C-EC3025, C-96039F, C-44EA29, C-D04904, C-B23205, C-60C75F, C-7BBDFA, C-50D386, C-93C8BF
- hq_country: 28/34 (82.4%) -- 6 blank: C-2D1F1B, C-D73B89, C-44EA29, C-D04904, C-2C60E5, C-EE9FFB

CONTACTS (52 rows):
- email:      52/52 (100%) -- but 4 are malformed/incomplete (see below)
- title:      40/52 (76.9%) -- 12 blank
- persona:    37/52 (71.2%) -- 15 blank, plus 1 data-entry corruption

DUPLICATE COMPANY CLUSTERS (exact domain match):

  Cluster 1: acme-corp.com (2 rows)
    C-0A092931: Technology / 500 / US
    C-0A092932: tech / 510 / USA
    SURVIVOR: C-0A092931 (all 3 fields populated; older/better-formed)
    Action: merge C-0A092932 into C-0A092931, normalize country to "US", industry to "Technology", employee_count to max(500,510)=510 if enrichment confirms.

  Cluster 2: globex.io (2 rows)
    C-0A092933: SaaS / 200 / US
    C-0A092934: Technology / 200 / US
    SURVIVOR: C-0A092933 (first-seen, industry="SaaS" may be too narrow vs "Technology")
    Action: merge C-0A092934 into C-0A092933; prefer CRM's broader category for segment reporting.

INVALID EMAILS (4 of 52 contacts):
  CT-0010 (C-66D1FC): "user0@" -- no domain after @
  CT-0080 (C-92D97D): "user0@" -- no domain after @
  CT-0081 (C-92D97D): "user1@" -- no domain after @
  CT-0192 (C-425E2A): "user2@" -- no domain after @
  All follow pattern: first name + @ with no domain appended. Fix: append company domain (@92d97d.com etc.).

DOMAIN MISMATCHES:
  CT-0011 (C-66D1FC): email=CT-0011@other-domain.com but contact.domain=66d1fc.com
  This sends outreach to the wrong corporate inbox entirely. Must correct to CT-0011@66d1fc.com.

ENRICHMENT FILLS (8 fields fillable):
  8 company rows have blank employee_count; ZoomInfo has value=400 for all of them.
  Fills: C-EC3025, C-96039F, C-44EA29, C-D04904, C-B23205, C-60C75F, C-7BBDFA, C-50D386

CRM vs ENRICHMENT DISAGREEMENTS (21 across 16 companies):
  - Industry: CRM says "Technology"/"Tech"/"tech" while enrichment says "Computer Software" for 10 companies. These map to the same concept. Normalize to "Technology". Trust CRM's broader label for segment aggregation.
  - HQ Country: CRM uses "US"/"USA"/"United States" interchangeably (20 conflicts). All three mean the same place. Normalize to ISO code "US". Trust enrichment as vendor-sourced truth for country.
  - Employee Count (REAL conflict): C-B25F40 has CRM=50, enrichment=120. TRUST ZOOMINFO (external headcount data). Update CRM to 120.

DATA ENTRY CORRUPTION FOUND:
  CT-0080 (C-92D97D): persona field contains "92d97d.com" (a domain string, not a persona). Should be one of: economic buyer / champion / HR admin / IT security / finance. Needs manual lookup or inference from title ("HR admin" at CT-0080 suggests persona="HR admin").

CROSS-REFERENCE INTEGRITY:
  100% of contacts link to valid company aliases.
  14 of 34 companies (41.2%) have zero contacts assigned.

NO DEALS FILE PROVIDED. All pipeline $ amounts remain UNKNOWN.

TOP 10 FIXES BY RECORD IMPACT (pipeline $ unquantifiable without deal data):

  1. [CRITICAL] Supply deals extract -- blocks ALL pipeline valuation
  2. [HIGH] Merge duplicate clusters (acme-corp x2, globex.io x2) -- 2 redundant rows cause double-counting
  3. [HIGH] Fix 4 invalid emails -- sequences will bounce, outreach blocked
  4. [MEDIUM] Standardize industry labels -- 11 records with non-canonical variants corrupt segmentation
  5. [MEDIUM] Standardize country values -- 3 representations (US/USA/United States) break territory routing
  6. [MEDIUM] Fill 8 missing employee counts from ZoomInfo -- ABM targeting unusable
  7. [MEDIUM] Resolve 21 CRM vs enrichment disagreements -- inconsistent categorization corrupts reporting
  8. [MEDIUM] Fill 15 missing personas -- buying committee mapping incomplete
  9. [LOW] Correct 1 email/domain mismatch -- sends to wrong inbox
  10. [LOW] Fill 12 missing contact titles -- engagement scoring incomplete
deal-intelligence
closed-lost-classificationPASS 1.00

? 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 $0.0012 · 88s · in 6,916 / out 6,870 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

show the model's full answer
Here is the full classification, side assignments, and summary — based strictly on the `closed_lost_tag` and `closed_lost_reason_text` fields in the CSV. No external assumptions.

---

## DEAL CLASSIFICATION

| # | Deal Alias | Amount | Close Date | Closed_Lost_Tag | Primary Category | Side | Notes / Disagreement? |
|---|------------|--------|------------|-----------------|-----------------|------|----------------------|
| 1 | Deal-DB0AAC | $5,115 | 2026-09-30 | Lost- Timing (1 year+) | Timing | Buyer | No |
| 2 | Deal-F7F635 | $3,600 | 2026-09-03 | Competitor | Competitor | Unknown | Vague "another direction", no competitor named |
| 3 | Deal-AC944F | $3,400 | 2026-09-02 | MIA | Other | Buyer | N/A (MIA is its own bucket) |
| 4 | Deal-214060 | $2,880 | 2026-09-02 | MIA | Other | Buyer | N/A |
| 5 | Deal-91A056 | $2,975 | 2026-09-02 | Lost- Timing (1 year+) | Timing | Buyer | No |
| 6 | Deal-29326C | $6,300 | 2026-09-02 | Lost- Timing (1 year+) | Timing | Buyer | No |
| 7 | Deal-5DB9B0 | $10,800 | 2026-09-02 | Lost- Does not fit ICP | Other | Buyer | Spam — not a real GTM failure |
| 8 | Deal-831B7B | $7,200 | 2026-09-02 | Lost- Timing (1 year+) | Timing | Buyer | No |
| 9 | Deal-F97C37 | $4,320 | 2026-09-01 | Competitor | Product Gap | Buyer | Tag says competitor; text says "more diversified offerings" — that's a feature gap, not an active competitor selection |
| 10 | Deal-13E9CF | $33,750 | 2026-09-01 | Doing nothing/Cost/Budget | Timing | Buyer | Tag says cost/budget; text says "deprioritized by org" — budget exists but priority shifted; Timing is better fit |
| 11 | Deal-39E25C | $3,360 | 2026-09-01 | Lost- Timing (1 year+) | Timing | Buyer | No |
| 12 | Deal-7ED004 | $60,000 | 2026-09-01 | Lost- Budget/Price | Pricing | Buyer | No |
| 13 | Deal-21B045 | $11,700 | 2026-08-31 | MIA | Other | Buyer | N/A |
| 14 | Deal-B3ABED | $40,001 | 2026-08-31 | Lost- Timing (1 year+) | Timing | Buyer | No |
| 15 | Deal-422BA6 | $3,000 | 2026-08-28 | Competitor | Competitor | Buyer | Clear competitor win (ADP/Review) |
| 16 | Deal-ED9AE7 | $2,340 | 2026-08-28 | Lost DM | Timing | Buyer | Tag is non-standard; text confirms "Timing, budget, authority" — Timing first |
| 17 | Deal-988493 | $8,400 | 2026-08-28 | MIA | Other | Buyer | N/A |
| 18 | Deal-381C8C | $4,800 | 2026-08-27 | Competitor | Competitor | Unknown | Tag = Competitor; text = not moving forward w/ Bonusly but doesn't name a winner. Keep as Competitor loss per tag. |
| 19 | Deal-F308CA | $30,321 | 2026-08-27 | MIA | Other | Buyer | No |
| 20 | Deal-F1E8A6 | $3,150 | 2026-08-26 | Competitor | Competitor | Buyer | Generic "not moving forward" — tag carries |
| 21 | Deal-B6AC09 | $3,000 | 2026-08-25 | Lost- Timing (1 year+) | Timing | Buyer | No |
| 22 | Deal-70F704 | $3,000 | 2026-08-25 | Lost DM | Product Gap | Buyer | Text says "only wanted anniversary awards" — product scope too broad, not a fit. Tag = Lost DM is weak. |
| 23 | Deal-E6E80A | $24,000 | 2026-08-21 | Lost- Timing (1 year+) | Timing | Buyer | No |
| 24 | Deal-B038F0 | $2,340 | 2026-08-21 | Lost- Timing (1 year+) | Timing | Buyer | No |
| 25 | Deal-4664E1 | $12,000 | 2026-08-20 | MIA | Other | Buyer | N/A |
| 26 | Deal-175756 | $2,880 | 2026-08-20 | Lost- Timing (1 year+) | Timing | Buyer | No |
| 27 | Deal-E74A73 | $2,100 | 2026-08-20 | Doing nothing/Cost/Budget | Timing | Buyer | Tag = Cost; text = "test manually first, reconnect next year" — Timing is stronger signal |
| 28 | Deal-DDAB52 | $4,000 | 2026-08-20 | Competitor | Competitor | Buyer | Rippl named, same price, exchange-rate advantage — clear competitor loss |
| 29 | Deal-ACE061 | $3,600 | 2026-08-19 | Competitor | Competitor | Buyer | Likely went HeyTaco (rep suspects it) |
| 30 | Deal-BB78F3 | $6,600 | 2026-08-18 | Lost- Timing (1 year+) | Timing | Buyer | No |
| 31 | Deal-D48E0B | $14,931 | 2026-08-18 | MIA | Other | Buyer | No |
| 32 | Deal-15DA99 | $19,600 | 2026-08-18 | Lost- Timing (1 year+) | Timing | Buyer | No |
| 33 | Deal-F4AF5D | $5,760 | 2026-08-18 | Lost- Timing (1 year+) | Timing | Buyer | No |
| 34 | Deal-79B7A1 | $25,000 | 2026-08-18 | Lost- Timing (1 year+) | Timing | Buyer | No |
| 35 | Deal-583ADB | $3,600 | 2026-08-18 | MIA | Other | Buyer | N/A |
| 36 | Deal-8E27DA | $21,000 | 2026-08-18 | Feature Request | Other | Buyer | Moved forward with swag provider, dropped R&R interest. Neither product gap nor pure competitor. |
| 37 | Deal-2D2F8D | $4,800 | 2026-08-18 | Competitor | Competitor | Unknown | "Different direction" — tag carries, no specifics |
| 38 | Deal-E0441F | $2,405 | 2026-08-17 | MIA | Other | Buyer | Stale before handoff |
| 39 | Deal-7CB44D | $31,860 | 2026-08-17 | MIA | Other | Buyer | No |
| 40 | Deal-0F96AA | $76,800 | 2026-08-17 | Competitor | Competitor | Buyer | Full RFP, didn't make finalist demo — clear competitor win |
| 41 | Deal-1BCA50 | $15,000 | 2026-08-17 | Competitor | Competitor | Buyer | Other stakeholder already deep with different vendor |
| 42 | Deal-7CC678 | $11,116 | 2026-08-17 | Competitor | Competitor | Unknown | "Nothing specific provided" — tag carries |
| 43 | Deal-FAC17C | $2,100 | 2026-08-17 | Lost DM | Timing | Buyer | Contract out 2 months, no exec IT approval — stalled decision |
| 44 | Deal-242273 | $60,000 | 2026-08-14 | Competitor | Product Gap | Buyer | Tag = Competitor; text = "digitize internal points currency / onsite facilities spend" — that's a capability gap, not that they picked a rival |
| 45 | Deal-50E5D8 | $4,800 | 2026-08-14 | Doing nothing/Cost/Budget | Timing | Buyer | "Pause for now" — Timing, not cost |
| 46 | Deal-A2C349 | $21,600 | 2026-08-14 | Competitor | Competitor | Buyer | Stick with Awardco + add surveys |
| 47 | Deal-9F176A | $54,600 | 2026-08-13 | Lost- Timing (1 year+) | Timing | Buyer | No |
| 48 | Deal-7B2236 | $72,000 | 2026-08-12 | Doing nothing/Cost/Budget | Pricing | Buyer | "preference simpler and cheaper" — Pricing |
| 49 | Deal-AFA56C | $3,000 | 2026-08-12 | MIA | Other | Buyer | No |
| 50 | Deal-C7156E | $13,818 | 2026-08-12 | Competitor | Competitor | Buyer | Selected another vendor |
| 51 | Deal-C33D91 | $7,200 | 2026-08-11 | Lost- Budget/Price | Pricing | Buyer | No |
| 52 | Deal-9048EB | $41,790 | 2026-08-10 | MIA | Product Gap | Buyer | Tag=MIA; text= "bad fit, multiple feature gaps" — Product Gap is correct. **DISAGREEMENT** |
| 53 | Deal-5E64CE | $33,600 | 2026-08-10 | Doing nothing/Cost/Budget | Timing | Buyer | In Nectar exit window until Oct 2027; timing play |
| 54 | Deal-8A0992 | $7,336.56 | 2026-08-10 | Competitor | Competitor | Buyer | Canadian provider — geographic alignment |
| 55 | Deal-D0C698 | $2,000 | 2026-08-10 | Competitor | Competitor | Unknown | Past Kudos user, going back to Kudos |
| 56 | Deal-69CF3D | $11,520 | 2026-08-07 | Lost- Timing (1 year+) | Timing | Buyer | No |
| 57 | Deal-ECBF89 | $7,200 | 2026-08-07 | Lost- Timing (1 year+) | Timing | Buyer | No |
| 58 | Deal-3618CC | $15,600 | 2026-08-07 | Lost DM | Product Gap | Buyer | "Wanted Surveys" — product scope mismatch |
| 59 | Deal-EECC02 | $66,690 | 2026-08-07 | Competitor | Competitor | Buyer | "Went another direction" — tag carries |
| 60 | Deal-5AD03E | $24,000 | 2026-08-07 | Competitor | Product Gap | Buyer | Tag=Competitor; text="wanted more defined budget access" — feature gap, not active competitor win. **DISAGREEMENT** |
| 61 | Deal-D1A623 | $25,200 | 2026-08-06 | Lost- Timing (1 year+) | Timing | Buyer | No |
| 62 | Deal-413C56 | $2,760 | 2026-08-06 | Doing nothing/Cost/Budget | Timing | Buyer | "Back to school priority, CEO not ready" — Timing |
| 63 | Deal-47F1A1 | $10,004 | 2026-08-06 | Competitor | Competitor | Buyer | Staying with WorkTango 12 more months |
| 64 | Deal-BF2A98 | $8,400 | 2026-08-05 | Competitor | Competitor | Buyer | HiThrive recently deployed |
| 65 | Deal-2A292B | $6,000 | 2026-08-05 | Doing nothing/Cost/Budget | Other | Buyer | "Build internally" — neither competitor nor pricing driver stated |
| 66 | Deal-D1AABF | $23,400 | 2026-08-05 | MIA | Other | Buyer | No |
| 67 | Deal-FEDBCB | $2,000 | 2026-08-05 | Doing nothing/Cost/Budget | Timing | Buyer | "Reconnect closer to end of year" — Timing |
| 68 | Deal-1E7DA9 | $26,400 | 2026-08-04 | Competitor | Competitor | Buyer | Selected another platform |
| 69 | Deal-2BBA21 | $2,310 | 2026-08-04 | MIA | Other | Buyer | No |
| 70 | Deal-286F9C | $13,860 | 2026-08-04 | Competitor | Competitor | Buyer | "Not a good fit" — tag carries but ambiguous |
| 71 | Deal-7FBAC6 | $7,200 | 2026-08-04 | Doing nothing/Cost/Budget | Timing | Buyer | "Leadership paused (again)" — Timing |
| 72 | Deal-369281 | $2,400 | 2026-08-04 | Competitor | Competitor | Buyer | Went with Paylocity in-house solution |
| 73 | Deal-386F6E | $13,895 | 2026-08-04 | MIA | Other | Buyer | No |
| 74 | Deal-9FCD0D | $4,300 | 2026-08-04 | Competitor | Competitor | Buyer | Canadian company chosen |
| 75 | Deal-55867E | $7,200 | 2026-08-04 | Lost- Timing (1 year+) | Timing | Buyer | No |
| 76 | Deal-DAFB82 | $30,000 | 2026-08-03 | Lost- Budget/Price | Timing | Buyer | Tag=Price; text="priorities now, won't be budgeted until 2028" — Timing is stronger signal |
| 77 | Deal-2FEDDB | $2,200 | 2026-08-03 | Doing nothing/Cost/Budget | Timing | Buyer | "Unsure on timing" — Timing |
| 78 | Deal-64B19A | $3,240 | 2026-08-03 | Competitor | Competitor | Buyer | Likely Motivosity |
| 79 | Deal-3F86A0 | $3,840 | 2026-08-03 | MIA | Other | Buyer | No |
| 80 | Deal-096750 | $2,880 | 2026-07-31 | MIA | Other | Buyer | No |
| 81 | Deal-F325A5 | $14,400 | 2026-07-31 | Lost DM | Timing | Buyer | Layoffs + leadership change killed priority |
| 82 | Deal-ABD14C | $5,002 | 2026-07-31 | Doing nothing/Cost/Budget | Other | Buyer | "Not interested" — vague, no driver stated |
| 83 | Deal-79E61A | $7,020 | 2026-07-31 | MIA | Other | Buyer | No |
| 84 | Deal-8A119B | $3,250 | 2026-07-31 | Lost- Budget/Price | Pricing | Buyer | No |
| 85 | Deal-AE7C4E | $2,800 | 2026-07-31 | MIA | Other | Buyer | No |
| 86 | Deal-DAB4F1 | $3,450 | 2026-07-31 | MIA | Other | Buyer | No |
| 87 | Deal-B4B50F | $21,060 | 2026-07-31 | MIA | Other | Buyer | No |
| 88 | Deal-981AD4 | $36,855 | 2026-07-30 | Feature Request | Product Gap | Buyer | "Doesn't fit UI, not UK focused" — genuine product gap |
| 89 | Deal-DC77FE | $8,000 | 2026-08-06* | Competitor | Product Gap | Buyer | Tag=Competitor; text="competitive in price, found system with more customization" — that's a product gap. Price was NOT the factor. **DISAGREEMENT** |
| 90 | Deal-5885B9 | $7,200 | 2026-07-29 | MIA | Other | Buyer | N/A |

*\*Date appears after some Aug 07 deals but is within the 6-month window.*

---

## CATEGORY COUNTS

| Category | Count | % of Total |
|----------|-------|-----------|
| **Timing** | 33 | 37% |
| **Competitor** | 18 | 20% |
| **Other** | 18 | 20% |
| **Pricing** | 4 | 4% |
| **Product Gap** | 4 | 4% |
| **Champion Left** | 0 | 0% |
| **No Decision** | 0 | 0% |
| **MIA (counted as Other above)** | — | included in Other |

*(Note: MIA is treated as "Other" rather than its own top-level category. If MIA were separated: Timing=33, Competitor=18, MIA=20, Pricing=4, Product Gap=4, Other=10, No Decision/Champion Left=0.)*

---

## SIDE SPLIT

| Side | Count |
|------|-------|
| **Buyer** | 81 |
| **Unknown** | 7 |
| **Bonusly** | 0 |

*Unknown applies to deals where the buyer never made a clear decision but the rep also couldn't pinpoint a reason (e.g., generic "another direction" without naming a competitor).*

---

## TAG vs. REASON DISAGREEMENTS

The structured `closed_lost_tag` disagrees with what the free-text `closed_lost_reason_text` actually describes. These are cases where the tag led to a different category than the text supports:

1. **Deal-DC77FE** ($8,000): Tag = Competitor. Text = *"competitive in price so that wasn't a factor…found a system that offered more customization."* This is a **product gap** — price was ruled out, not a competitor win.
2. **Deal-242273** ($60,000): Tag = Competitor. Text = *"top two vendors helped digitize internal points currency / onsite spending."* This is a **product gap** — the capability to build a custom internal currency/onsite marketplace wasn't offered. They may not have selected anyone yet.
3. **Deal-5AD03E** ($24,000): Tag = Competitor. Text = *"Wanted more defined budget access."* This is a **product gap**, not an active competitor selection.
4. **Deal-9048EB** ($41,790): Tag = MIA. Text = *"bad fit based on desired setup and multiple feature gaps."* The rep independently determined this was a **product gap**. Tag missed the real reason.
5. **Deal-F97C37** ($4,320): Tag = Competitor. Text = *"other vendor had more diversified offerings."* This is a **product gap** — lack of breadth, not necessarily a rival they chose.
6. **Deal-13E9CF** ($33,750): Tag = Doing Nothing/Cost/Budget. Text = *"R&R deprioritized by org, reach out next year."* Better classified as **Timing** — priority shift, not active budget constraint.
7. **Deal-70F704** ($3,000): Tag = Lost DM. Text = *"only wanted anniversary awards."* Better as **Product Gap** — narrow use case outside Bonusly's value prop.

**Total disagreements: 7 out of ~90 deals (~8%).** The strongest disagreements are in high-value deals (#2 and #3 above = $84K combined).

---

## TWO PATTERNS MOST WORTH ACTING ON

### Pattern 1: "Competitor" tag used as a catch-all for product-gap losses — losing visibility on what we actually need to fix

~5 of the "Competitor" classified deals are actually product gaps (customization, breadth, currency model, UK focus/UI). When reps mark Competitor for anything that isn't a clean "we went with X vendor," we lose the ability to differentiate between competitive threats we can fight (price, integrations, partnerships) and genuine capability gaps we need to build.

**Action:** Add a required free-text field to any Competitor close — *"Which vendor did they choose?"* If they can't name one, the deal defaults to Product Gap. This alone would recover the 5 flagged deals above and prevent future miscategorization.

### Pattern 2: Timing kills disproportionately — 37% of losses are deferred, not rejected

33 deals (37%) are Timing. But many of these are **re-prioritization** losses, not just "budget cycle." The texts repeatedly say "deprioritized by org," "layoffs/leadership change," "back to school priority," "survey action items first." These are signals that Bonusly's sales cycle is longer than the time it takes for leadership attention shifts.

**Action:** Two-pronged:
- **Short-term:** Build a lightweight "priority trigger" score — if a prospect mentions layoffs, restructuring, new hire leader, or competing initiative, flag for a 30-day touch-point cadence rather than letting it sit.
- **Mid-term:** Develop a lighter-tier or modular entry point. Deals like Deal-70F704 ("only want anniversary"), Deal-E74A73 ("test manually first"), Deal-8E27DA ("just swag") suggest buyers who want fragments of R&R. A "starter" product could capture these instead of losing to timing or Build Internally (Deal-2A292B).
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 $0.0035 · 92s · in 40,500 / out 8,596 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
Here is the compact JSON scoring result:

```json
{
  "tier_counts": {
    "ACTION": 12,
    "BUILD": 63,
    "LOCK": 2,
    "REVIVE": 2,
    "RISKY": 4,
    "WATCH": 73
  },
  "tier_examples": {
    "ACTION": ["Deal-25F752", "Deal-5408B0", "Deal-944310"],
    "BUILD": ["Deal-C6FE92", "Deal-D73B89", "Deal-40522D"],
    "LOCK": ["Deal-D348E1", "Deal-C26D20"],
    "REVIVE": ["Deal-9AAE5F", "Deal-F9A08A"],
    "RISKY": ["Deal-547B2B", "Deal-A2B47C", "Deal-2465CE", "Deal-FD9F4E"],
    "WATCH": ["Deal-E53952", "Deal-6787C2", "Deal-62D607"]
  },
  "risky_deals": [
    "Deal-547B2B",
    "Deal-A2B47C", 
    "Deal-2465CE",
    "Deal-FD9F4E"
  ],
  "lock_violations": 0,
  "pipeline_shape": "This is a classic top-heavy funnel — 73 deals in WATCH and 63 in BUILD make up 85% of the pipeline, meaning most opportunities sit in early stages (DS1-DS2) with PIPELINE forecast categories and thin contact maps (mostly 2-3 n_contacts). Only 14 deals carry strong signals across stage, forecast, and recent engagement combined; 2 landed in LOCK, while 12 earned ACTION status. Four DEALS are flagged RISKY because their forecast label (COMMIT or BEST_CASE) contradicts stale engagement evidence — commitment claims not backed by recent meetings or calls. Two deals marked REVIVE had high historical engagement_90d (>20) but are now dormant >60 days. The weighted revenue concentrates in the large PIPELINE-stage deals ($240K Deal-2D1F1B, $99K Deal-66D1FC), so despite low near-term closure probability, upside depends on converting these early-funnel leads."
}
```

**Arithmetic verification:**
- Tier sum: 12 + 63 + 2 + 2 + 4 + 73 = **156** (matches deal row count)
- Lock violations: 0 (both LOCK deals — Deal-D348E1 and Deal-C26D20 — each have >= 1 meeting in 30d)
- Inbound_emails defect: acknowledged; meetings_30d used as primary inbound signal
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 $0.0005 · 37s · in 2,618 / out 2,620 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
Here is the extracted CRM write-back data per deal:

```json
{
  "tx_001_deal_cfe7f4": {
    "why_buys": [
      "Automating anniversary and birthday awards",
      "HR team of three cannot keep up manually",
      "People slipping through cracks in spreadsheet tracking"
    ],
    "pain_points": [
      "Manual tracking via spreadsheet",
      "Staffing gap (HR team of 3)",
      "Items slipping through cracks"
    ],
    "stakeholders_from_speaker_list": [
      "Prospect (VP People)",
      "Prospect (HR Admin)"
    ],
    "budget_signal": "$40k earmarked for engagement tools this fiscal year",
    "timeline_signal": "Ideally live before open enrollment in November",
    "competitor_mentioned_by_prospect": "Achievers (looked at last year, deemed too heavy)",
    "next_step_agreed": "Security review scheduled for September 12",
    "objections": [
      "Need SSO and audit logs for IT sign-off (security requirement from HR Admin)"
    ],
    "confidence": "HIGH — stated budget ($40k), committed date (Sep 12), named competitor"
  },
  "tx_002_deal_70bb30": {
    "why_buys": [
      "Tie recognition to retention for hourly workforce",
      "Regretted turnover over 30% among hourly staff"
    ],
    "pain_points": [
      "High regretted turnover (>30%) in hourly workforce"
    ],
    "stakeholders_from_speaker_list": [
      "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_by_prospect": null,
    "next_step_agreed": "Pilot agreement will be sent; routed to legal this week",
    "objections": [
      "Integration with Workday must be rock solid — stated as CFO's one condition"
    ],
    "confidence": "HIGH — budget approved, firm timeline, next step confirmed (pilot agreement + legal route)"
  },
  "tx_003_deal_530b50": {
    "why_buys": [
      "Make recognition visible across 12 retail locations",
      "Store managers have zero budget autonomy for on-the-spot recognition today"
    ],
    "pain_points": [
      "Inconsistent visibility of recognition across multi-site locations",
      "No decentralized spending authority for store managers"
    ],
    "stakeholders_from_speaker_list": [
      "Prospect (People Ops Manager)",
      "Prospect (CEO) — referenced but not present on speaker list"
    ],
    "budget_signal": null,
    "timeline_signal": "No rush until Q1",
    "competitor_mentioned_by_prospect": "Bucketlist (CEO used at last company, liked it)",
    "next_step_agreed": "Schedule a call with the CEO — People Ops Manager will send two times",
    "objections": [
      "CEO must be sold first — she decides anything people-related (single point of approval)"
    ],
    "confidence": "MEDIUM — no budget stated, soft timeline (Q1), gatekeeper is absent CEO"
  },
  "tx_004_deal_180d02": {
    "why_buys": [
      "Consolidate three separate recognition tools into one",
      "Currently paying for three tools, none integrate with HRIS"
    ],
    "pain_points": [
      "Tool sprawl (three active tools)",
      "Zero HRIS integration across current stack",
      "Duplicate spend"
    ],
    "stakeholders_from_speaker_list": [
      "Prospect (VP People)",
      "Prospect (IT Security Lead)"
    ],
    "budget_signal": "Under $15k annually = VP People can approve without board involvement",
    "timeline_signal": "Procurement cycle runs six to eight weeks minimum; security review took three months for last vendor",
    "competitor_mentioned_by_prospect": null,
    "next_step_agreed": null,
    "objections": [
      "Six-to-eight-week procurement cycle creates friction",
      "Three-month security review history creates hesitation"
    ],
    "confidence": "LOW — next step was tentative (\"no promises\") and not confirmed; long procurement/security process ahead"
  },
  "tx_005_deal_f8767a": {
    "why_buys": [
      "Automate service milestones",
      "Provide analytics on recognition equity across departments"
    ],
    "pain_points": [
      "Night-shift teams feel invisible",
      "Night-shift engagement scores run 20 points lower than other shifts",
      "Service milestone processing is manual/admin-heavy"
    ],
    "stakeholders_from_speaker_list": [
      "Prospect (HR Director)",
      "Prospect (People Ops Coordinator)"
    ],
    "budget_signal": "$12k approved under engagement line",
    "timeline_signal": "Need this running before January all-hands",
    "competitor_mentioned_by_prospect": "Nectar (mid-pilot; must beat that experience)",
    "next_step_agreed": "Present directly to exec team on October 2",
    "objections": [
      "Exec team skeptical after a failed rollout two years ago"
    ],
    "confidence": "MEDIUM — budget approved, firm date set, but active competitor in pilot and exec skepticism add risk"
  },
  "tx_006_deal_ee195f": {
    "why_buys": [
      "Cut admin time on service awards"
    ],
    "pain_points": [
      "HR Manager spends 5 hours/month ordering and shipping plaques manually"
    ],
    "stakeholders_from_speaker_list": [
      "Prospect (HR Manager)"
    ],
    "budget_signal": null,
    "timeline_signal": "Fiscal year resets in February; Q1 start is realistic",
    "competitor_mentioned_by_prospect": null,
    "next_step_agreed": "Send one-page overview; HR Manager will forward to COO this week",
    "objections": [
      "COO usually prefers building things in-house (potential blocker)"
    ],
    "confidence": "MEDIUM — clear pain, actionable next step, but single stakeholder and unknown COO stance"
  }
}
```

Key methodology notes:

- **budget_signal**: Only populated when the prospect explicitly stated a dollar amount or budget source. Where the rep offered pricing (TX-003: \"$8/emp/mo\") or the prospect said \"budget isn't the issue\" (TX-006), the field is `null`.
- **next_step_agreed**: Only included when both parties confirmed a specific action. TX-004's \"I'll follow up\" was acknowledged but never confirmed by the prospect, so it is excluded.
- **next_step NOT agreed (TX-004)**: The prospect said \"Maybe — I need to check her calendar, no promises.\" This is a non-commitment; the next step was not closed.
- **competitor_mentioned_by_prospect**: Only competitors raised by the prospect count. Rep-suggested alternatives do not qualify.
next-to-close0.75

? 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 $0.0003 · 22s · in 2,047 / out 1,243 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
**1. Deal-547B2B — $11,200**
Alex confirms redlines came back clean, the signing page is deployed, and the client's VP People said they're signing tomorrow. Close date 2026-09-11, DS5/COMMIT. Left: nothing — signature day.

**2. Deal-403845 — $9,000**
Dana says the order form is with the client's finance team. Still DS5/COMMIT, close 2026-09-11. Slack status "moving fine" but no signature trigger yet. Left: finance approval cycle on the order form.

**3. Deal-A2B47C — $6,360**
Alex reports "normal legal-review pace," no blockers flagged. DS5/COMMIT, close 2026-09-11. Less certain than #1–#2 but confirmed warm with no known issues. Left: legal review still in flight.

Note: **Deal-2465CE** ($5,400, committed in CRM) dropped out — Dana reports their champion left and procurement froze new vendors. She's pulling it out of commit; realistically a Q4 deal now.

*Top-of-book sum (these 3): $26,560.*
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 $0.0003 · 25s · in 1,300 / out 1,559 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
Here is the analysis against your constraints. Dollar amounts are not present in the supplied data, so those fields read as such.

---

## Transcript-Gap Analysis

### Candidate 1 — TG-001 | Deal-EC3025

**Prospect quote:** *"We need SCIM user provisioning — our security team will not approve any tool that requires manual account management."*

**Product-doc check:** Docs list SSO/SAML + audit logs under Enterprise only. The Supported Integrations section explicitly states: *"SCIM user provisioning … are NOT currently listed as supported capabilities."*

**Classification: Real Gap.** SCIM provisioning does not exist in any tier — not a plan upgrade path, not an enablement miss. It is a hard capability absence that blocks IT-security approval.

| Attribute | Value |
|---|---|
| Deal alias | Deal-EC3025 |
| Dollar amount | Not present in supplied data |
| Prospect persona | IT Security Lead |
| Obstacle level | Security-block (compliance) |

---

### Candidate 2 — TG-002 | Deal-D0D6B5

**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."*

**Product-doc check:** HRIS integrations table lists Workday, BambooHR, Gusto, Rippling (Pro+). The same paragraph confirms: *"ADP Workforce Now integrations are NOT currently listed as supported capabilities."*

**Classification: Real Gap.** ADP WFN is absent from every tier. No higher-plan row adds it. It is a hard capability gap.

| Attribute | Value |
|---|---|
| Deal alias | Deal-D0D6B5 |
| Dollar amount | Not present in supplied data |
| Prospect persona | HRIS Manager |
| Obstacle level | Operational-block (payroll dependency) |

---

### Candidates excluded from the final summary

**TG-003 — Deal-CFE7F4**

Prospect: *"I tried to build a custom report combining recognition frequency with turnover and couldn't — is that possible on our current plan?"*
Rep: *"The custom report builder sits on our Enterprise tier — I can walk you through upgrade paths."*
Docs: Custom report builder is listed under Enterprise. → **Plan Gate.** Feature exists, just above the prospect's current tier. Fix = upsell; not a product gap.

**TG-004 — Deal-84DBA6**

Prospect: *"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."*
Docs: Slack integration is available on all plans; no mention of training modules. → **Rollout / Enablement Issue.** The product delivers the feature; the org lacks adoption processes (training/comms). Not a gap in the software itself.

**TG-005 — Deal-36C33F**

Rep: *"...we don't have a native mobile app for hourly workers yet, though it's on the roadmap."*
Prospect: *"Good to know. The web version should be fine for our office staff for now."*
Prospect did not voice this concern — the rep volunteered it. Rep language does not count per your rules. Moreover, the product docs make no mention of mobile apps, so we cannot independently classify it here. → **Excluded** (rep-only signal + no verification).

---

## Final Summary — True Product Gaps

Only two candidates cleared the "real gap" bar:

1. **SCIM user provisioning** — absent from all tiers. Raised by an IT Security Lead at **Deal-EC3025** (amount: not in supplied data). Blocks compliance/security sign-off.
2. **ADP Workforce Now integration** — absent from all tiers. Raised by an HRIS Manager at **Deal-D0D6B5** (amount: not in supplied data). Described as a "dealbreaker."

Both are confirmed by the product docs excerpt as unsupported capabilities. Neither can be resolved by moving the prospect to a higher plan. Remediation requires adding them to the product roadmap.
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 $0.0039 · 76s · in 63,815 / out 7,886 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
Here are the results using snapshot date **2026-09-05** and threshold **2026-08-29** (any deal whose most recent email/call/meeting predates Aug 29 is stale). Two deals had no engagement row at all -- marked "never".

---

## Summary by Owner

| Owner | Stale Deals | Stale Amount |
|---|---|---|
| Bryce Harmon | 13 | $626,243.00 |
| Cole Ingram | 18 | $252,905.03 |
| Dana Mercer | 14 | $261,645.00 |
| Alex Franklin | 19 | $109,536.00 |
| Elena Sinclair | 1 | $2,100.00 |
| **TOTAL** | **65** | **$1,252,429.03** |

Farid Osman carries **zero** stale deals -- all 7 of his open deals had activity on or after Aug 29.

---

## Detail by Owner (amount descending within each group)

=== Bryce Harmon (13 stale deals, $626,243.00) ===

```
Deal-2D1F1B          | DS1      |  $240,000.00 |  81 days since last contact (last: 2026-06-16)
Deal-66D1FC          | DS1      |   $99,000.00 |  16 days since last contact (last: 2026-08-20)
Deal-950043          | DS1      |   $70,000.00 |  19 days since last contact (last: 2026-08-17)
Deal-B23205          | DS1      |   $45,000.00 |  16 days since last contact (last: 2026-08-20)
Deal-7BBDFA          | DS3      |   $37,440.00 |  46 days since last contact (last: 2026-07-21)
Deal-332637          | DS2      |   $36,000.00 |   9 days since last contact (last: 2026-08-27)
Deal-1BEEBF          | DS1      |   $31,500.00 |  19 days since last contact (last: 2026-08-17)
Deal-C5658B          | DS1      |   $23,400.00 |  16 days since last contact (last: 2026-08-20)
Deal-40522D          | DS3      |   $21,000.00 |  19 days since last contact (last: 2026-08-17)
Deal-F0EBBB          | DS3      |   $11,400.00 |  24 days since last contact (last: 2026-08-12)
Deal-E25A09          | DS1      |    $6,000.00 |   9 days since last contact (last: 2026-08-27)
Deal-C9C286          | DS2      |    $5,502.00 |   9 days since last contact (last: 2026-08-27)
Deal-012CB1          | DS1      |       $1.00 |  23 days since last contact (last: 2026-08-13)
```

=== Dana Mercer (14 stale deals, $261,645.00) ===

```
Deal-44EA29          | DS2      |   $60,000.00 |  10 days since last contact (last: 2026-08-26)
Deal-E51FB7          | DS2      |   $43,875.00 |  12 days since last contact (last: 2026-08-24)
Deal-B42F46          | DS1      |   $27,000.00 |  19 days since last contact (last: 2026-08-17)
Deal-BA3DDC          | DS3      |   $23,400.00 |  15 days since last contact (last: 2026-08-21)
Deal-9DDE86          | DS2      |   $20,000.00 |  15 days since last contact (last: 2026-08-21)
Deal-215CCA          | DS3      |   $18,900.00 |  17 days since last contact (last: 2026-08-19)
Deal-5EED42          | DS3      |   $16,250.00 |  11 days since last contact (last: 2026-08-25)
Deal-57887A          | DS2      |   $15,000.00 |   8 days since last contact (last: 2026-08-28)
Deal-B7EBD1          | DS5      |    $9,000.00 |  16 days since last contact (last: 2026-08-20)
Deal-3974EB          | DS4      |    $9,000.00 |   8 days since last contact (last: 2026-08-28)
Deal-F40F04          | DS2      |    $8,100.00 |  15 days since last contact (last: 2026-08-21)
Deal-87DDD1          | DS1      |    $5,000.00 |  19 days since last contact (last: 2026-08-17)
Deal-F336B6          | DS3      |    $4,200.00 |  15 days since last contact (last: 2026-08-21)
Deal-0660B4          | DS4      |    $1,920.00 |  16 days since last contact (last: 2026-08-20)
```

=== Alex Franklin (19 stale deals, $109,536.00) ===

```
Deal-CC08D1          | DS1      |   $24,000.00 |  16 days since last contact (last: 2026-08-20)
Deal-E73427          | DS3      |   $18,000.00 |  10 days since last contact (last: 2026-08-26)
Deal-885F45          | DS2      |    $9,300.00 |  12 days since last contact (last: 2026-08-24)
Deal-C2FF3C          | DS1      |    $8,316.00 |  10 days since last contact (last: 2026-08-26)
Deal-3EED2C          | DS2      |    $7,200.00 |     N/A last contact (no engagement row)
Deal-0D2F7A          | DS3      |    $5,100.00 |  12 days since last contact (last: 2026-08-24)
Deal-6C60D4          | DS3      |    $4,800.00 |  12 days since last contact (last: 2026-08-24)
Deal-13FEBD          | DS2      |    $4,680.00 |  12 days since last contact (last: 2026-08-24)
Deal-9D0060          | DS3      |    $3,840.00 |  12 days since last contact (last: 2026-08-24)
Deal-690476          | DS2      |    $3,600.00 |  18 days since last contact (last: 2026-08-18)
Deal-C6D97A          | DS4      |    $3,240.00 |   8 days since last contact (last: 2026-08-28)
Deal-EE195F          | DS3      |    $3,120.00 |   8 days since last contact (last: 2026-08-28)
Deal-278DEC          | DS3      |    $2,700.00 |   8 days since last contact (last: 2026-08-28)
Deal-635B8E          | DS3      |    $2,600.00 |  18 days since last contact (last: 2026-08-18)
Deal-6883F3          | DS1      |    $2,400.00 |  16 days since last contact (last: 2026-08-20)
Deal-4A13AD          | DS3      |    $2,160.00 |  26 days since last contact (last: 2026-08-10)
Deal-F67D31          | DS2      |    $1,800.00 |   8 days since last contact (last: 2026-08-28)
Deal-5FDCE4          | DS3      |    $1,600.00 |  12 days since last contact (last: 2026-08-24)
Deal-BA571A          | DS4      |    $1,080.00 |  18 days since last contact (last: 2026-08-18)
```

=== Cole Ingram (18 stale deals, $252,905.03) ===

```
Deal-D04904          | DS2      |   $58,529.25 |  11 days since last contact (last: 2026-08-25)
Deal-B25F40          | DS3      |   $40,000.00 |   8 days since last contact (last: 2026-08-28)
Deal-813836          | DS2      |   $32,175.00 |  11 days since last contact (last: 2026-08-25)
Deal-1BA595          | DS2      |   $31,750.00 |  11 days since last contact (last: 2026-08-25)
Deal-CFE1E8          | DS3      |   $18,000.00 |  11 days since last contact (last: 2026-08-25)
Deal-CD47A6          | DS2      |   $12,168.00 |  11 days since last contact (last: 2026-08-25)
Deal-627646          | DS3      |   $11,193.00 |  11 days since last contact (last: 2026-08-25)
Deal-FF809F          | DS2      |    $7,781.20 |  11 days since last contact (last: 2026-08-25)
Deal-AF932D          | DS2      |    $7,225.40 |  11 days since last contact (last: 2026-08-25)
Deal-A71728          | DS2      |    $6,947.50 |  11 days since last contact (last: 2026-08-25)
Deal-8BC9F5          | DS2      |    $5,616.00 |  10 days since last contact (last: 2026-08-26)
Deal-175395          | DS3      |    $4,779.88 |  11 days since last contact (last: 2026-08-25)
Deal-481E24          | DS3      |    $4,140.00 |  10 days since last contact (last: 2026-08-26)
Deal-C7F9BF          | DS2      |    $3,360.00 |  11 days since last contact (last: 2026-08-25)
Deal-2F3A66          | DS3      |    $3,334.80 |  11 days since last contact (last: 2026-08-25)
Deal-342E96          | DS2      |    $2,700.00 |  24 days since last contact (last: 2026-08-12)
Deal-E568D5          | DS3      |    $1,875.00 |  11 days since last contact (last: 2026-08-25)
Deal-FD9F4E          | DS5      |    $1,330.00 |  10 days since last contact (last: 2026-08-26)
```

=== Elena Sinclair (1 stale deal, $2,100.00) ===

```
Deal-57FF13          | DS1      |    $2,100.00 |     N/A last contact (no engagement row)
```

---

## Key observations

- **Arithmetic**: All 65 stale deals sum to $1,252,429.03 across 5 of 8 active owners. Farid Osman (716654662) has zero stale deals -- every one of his 7 open deals had engagement activity on or after Aug 29.
- **Oldest stale**: Bryce Harmon's Deal-2D1F1B ($240k) -- last contacted Jun 16, 81 days ago.
- **No engagement row**: 2 deals (Deal-3EED2C / Alex and Deal-57FF13 / Elena) appear in the open deals CSV but have no row in the engagements file, meaning their last contact cannot be determined from available data. Both are classified stale for this reason.
- **Concentration**: Bryce Harmon alone accounts for 50% of the stale pipeline value ($626k of $1.25M), driven by three big-ticket deals in DS1/PIPELINE not touched in over a month.
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 $0.0095 · 259s · in 68,896 / out 35,513 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
Here are the results.

WINDOW: last 30 days from snapshot 2026-09-05 = 2026-08-06 through 2026-09-05. Only active owners included. Gavin Porter and Hugo Lindqvist excluded (archived). Elena Sinclair appears with zeros (her single deal, 64524667574, has no engagement rows).

PER-REP ACTIVITY & DS2 SUMMARY
======================================
Rep                    Emails   Calls   Meet  Total  %Email  %Call  %Meet  DS2  Act/DS2
--------------------------------------

Bryce Harmon               162       0      43    205    79.0    0.0   21.0    4     51.2
Elena Sinclair              0       0       0      0      --     --     --    0       N/A
Farid Osman               38       0      34     72    52.8    0.0   47.2    1     72.0
Dana Mercer                84      18      11    113    74.3   15.9    9.7    1    113.0
Cole Ingram        96      14       1    111    86.5   12.6    0.9    2     55.5
Alex Franklin            307      36      41    384    79.9    9.4   10.7   18     21.3

NOTE on percentages: share of that rep's total activities (emails + calls + meetings).


DEALS ENTERING DS2 IN LAST 30 DAYS PER REP
======================================

| Deal alias       | Owner             | t_ds2    |
|------------------|-------------------|----------|
| Deal-25F752      | Bryce Harmon        | 2026-08-10 |
| Deal-D73B89      | Bryce Harmon        | 2026-09-03 |
| Deal-CA7DC0      | Bryce Harmon        | 2026-08-12 |
| Deal-1CCE5C      | Bryce Harmon        | 2026-08-06 |
=> Bryce Harmon: 4 deals entered DS2

| Deal-499BF6      | Farid Osman       | 2026-08-26 |
=> Farid Osman: 1 deal entered DS2

| Deal-57887A      | Dana Mercer        | 2026-08-07 |
=> Dana Mercer: 1 deal entered DS2

| Deal-42326B      | Cole Ingram| 2026-08-26 |
| Deal-1BA595      | Cole Ingram| 2026-08-12 |
=> Cole Ingram: 2 deals entered DS2

| Deal-403845      | Alex Franklin     | 2026-09-02 |
| Deal-1FC049      | Alex Franklin     | 2026-09-03 |
| Deal-3EED2C      | Alex Franklin     | 2026-09-03 |
| Deal-7FA0C3      | Alex Franklin     | 2026-08-07 |
| Deal-E531A6      | Alex Franklin     | 2026-08-07 |
| Deal-5296C9      | Alex Franklin     | 2026-08-28 |
| Deal-36C33F      | Alex Franklin     | 2026-08-11 |
| Deal-EE195F      | Alex Franklin     | 2026-08-06 |
| Deal-F436DA      | Alex Franklin     | 2026-08-19 |
| Deal-317E6F      | Alex Franklin     | 2026-08-12 |
| Deal-D1E6C2      | Alex Franklin     | 2026-08-11 |
| Deal-D9A72E      | Alex Franklin     | 2026-08-06 |
| Deal-CA5E44      | Alex Franklin     | 2026-08-24 |
| Deal-4F775F      | Alex Franklin     | 2026-08-17 |
| Deal-898FC5      | Alex Franklin     | 2026-08-28 |
| Deal-46988D      | Alex Franklin     | 2026-08-26 |
| Deal-E73427      | Alex Franklin     | 2026-08-28 |
| Deal-92D97D      | Alex Franklin     | 2026-09-02 |
=> Alex Franklin: 18 deals entered DS2

TOTAL: 26 deals across 5 reps (Elena Sinclair: 0).


EFFICIENCY RANKING (activities per DS2 entry, lowest = most efficient)
==================================================================
Rank  Rep                  Activities  DS2  Act/DS2
----------------------------------------------
  1   Alex Franklin             384    18     21.3
  2   Bryce Harmon                205     4     51.2
  3   Cole Ingram        111     2     55.5
  4   Farid Osman                72     1     72.0
  5   Dana Mercer                113     1    113.0


MOST EFFICIENT REP
======================================
Alex Franklin — 21.3 activities per DS2 entry (lowest ratio).
Activity mix: email 79.9%, call 9.4%, meeting 10.7%.
Total: 384 activities / 18 DS2 entries.


HIGHEST-VOLUME REP
======================================
Alex Franklin — 384 total activities in the last 30 days.
Activity mix: email 79.9%, call 9.4%, meeting 10.7%.


DO THEY DIFFER?
======================================
NO. The most efficient rep and the highest-volume rep are the same person: Alex Franklin. He also leads on absolute output (18 DS2 entries), which explains both his top volume and top efficiency simultaneously.
ae-qtd-scorecard0.50

? 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 $0.0010 · 69s · in 13,170 / out 4,051 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
```
================================================================================
                    ALEX FRANKLIN — Q3 2026 SCORECARD
                    As of 2026-09-05
================================================================================

--- BOOKINGS vs QUOTA ---

Q3 Closed-Won deals (close_date >= 2026-07-01 AND < 2026-10-01):

  Deal Alias    Amount   Date       Type
  -------------- ------ ---------- -------
  Deal-A1C3E5   $40,000  2026-07-15   New
  Deal-F2C7D8   $20,000  2026-07-24   Expansion
  Deal-B7D2F4   $35,000  2026-07-31   New
  Deal-C9E1A6   $21,000  2026-08-12   New
  Deal-A8B4D6   $12,000  2026-08-19   Expansion
  Deal-D4B8C2   $11,000  2026-08-21   New
  Deal-E6F3A9    $6,500  2026-09-02   New
  Deal-C5D9E2    $4,500  2026-09-03   Expansion
  -------------- ------ ---------- -------
  TOTAL WON     $120,000

Quota (2026-Q3): $200,000
Attainment:      $120,000 / $200,000 = 60.0%

New Pipeline:     $77,500 (5 deals; 64.6% of booked)
Expansion:        $42,500 (3 deals; 35.4% of booked)
Total Pipeline:  $120,000

Note: 1 prior closed-won deal excluded — Deal-B3E6F1 ($24,000, close 2026-06-20)
      falls outside Q3.


--- ACTIVE PIPELINE BY STAGE ---

Stage     Count  Total Amount    Weighted @ Avg Win Rate*
DS1         11   $224,700         $22,470    (10%)
DS2         26   $334,158         $100,247   (30%)
DS3         20   $142,726         $71,363    (50%)
DS4          6    $40,824         $24,494    (60%)
DS5          4    $28,960         $17,376    (80%)
--------    ---- -----------
TOTAL       67   $771,368         $235,950

*Standard weightings applied to unweighted total.


--- ROLLING 90-DAY DS2-TO-WON RATE ---

Window: 90 days through 2026-09-05 (any deal that entered DS2
between ~2026-06-07 and 2026-09-05).

Deals counted as winners (close_date within window):
  2026-09-02  Deal-E6F3A9    $6,500
  2026-09-03  Deal-C5D9E2    $4,500
  2026-08-21  Deal-D4B8C2   $11,000
  2026-08-19  Deal-A8B4D6   $12,000
  2026-08-12  Deal-C9E1A6   $21,000
  2026-07-31  Deal-B7D2F4   $35,000
  2026-07-24  Deal-F2C7D8   $20,000
  2026-07-15  Deal-A1C3E5   $40,000

Total won:    8 deals = $150,500
Total lost:   0 deals = $0
Rate:         8 / (8 + 0) = 100%

Warning: small numerator — only 1 deal in the cohort actually reached DS2
and closed. Rate reflects a thin funnel, not proven conversion skill.


--- WIN / LOSS COUNTS & TOP LOSS REASONS ---

Wins:  8 deals  |  $150,500
Losses: 0 dealt within 90-day DS2 window.

Loss reasons from ALL closed-lost deals in dataset:
  Loss Reason                          Count  Total Lost
  ----------------------------------- -----  ----------
  Competitor                               7     $66,931
  Lost-Timing (1 year or more)            11    $142,171
  MIA                                      5     $41,481
  Lost DM                                  2     $28,100
  Feature Request                          1     $21,000
  Does not fit ICP                         1     $10,800

Top 3 loss reasons by dollar impact:
  1. Lost-Timing       — $142,171 (61% of lost pipeline)
  2. Competitor         —  $66,931 (29%)
  3. MIA                —  $41,481 (18%)


--- ACTIVITY VOLUME — LAST 30 DAYS ---

Aggregated across all 67 open deals + 9 closed deals (data source: ae_engagements.csv,
column headers read "X_30d").

Activity Type    Count
-----------  ------
Emails           683
Calls             72
Meetings          48
Notes             49
-----------  ------
TOTAL            852


--- THREE COACHING OBSERVATIONS ---

1. LATE-QUARTER COLLAPSE — No deals won after 2026-09-02. With 28 days
remaining in Q3 and $80,000 still needed to hit quota, Alex must
force-prioritize top-of-funnel deals (DS4+ = $69,784) for immediate
closing and block-stall broken deals across every stage. Without an
accelerator in the final month, 60% attainment will harden.

2. TIMING IS THE REAL COMPETITOR — 11 of 26 closed-lost deals ($142,171,
61% of total lost) were lost to "1 year or more" timing objections.
This is not a product problem; it is a qualifying-and-pacing problem.
Alex is spending disproportionate effort on buyers who have no
budget cycle alignment. Before next outreach round, every deal must
carry a documented budget-cycle date or be moved to a nurture track.

3. HIGH-VOLUME ACTIVITY DISPARITY — Won deals average 42 touches in 30 days
(emails + calls + meetings + notes); the open pipeline averages 11.3.
Volume drives conversion. If the same intensity seen in closers were
applied across the board, especially the top 20 weighted deals
($512,298 combined), we would expect materially more DS3→DS4 movement
and fewer stalled mid-pipeline loops. Focus highest-touch cadences on
the 10 largest opportunities first.

================================================================================
END OF SCORECARD
================================================================================
```
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 $0.0033 · 412s · in 47,133 / out 8,197 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
3 of the 14 deals are well-threaded and not flagged. Here is the complete analysis of the 11 flagged deals:

---

**WELL-THREADED (not flagged):**
- Deal-4B0BEB (C-1BDFE0) — 4 active contacts, 4 personas (champion, economic buyer, HR admin, finance)
- Deal-84DBA6 (C-BE9BE0) — 3 active contacts, 3 personas (champion, economic buyer, IT security)
- Deal-D348E1 (C-804F29) — 5 active contacts, all 5 personas present

---

**FLAGGED DEALS (11 total), sorted by amount:**

1. **Deal-EC3025** (C-FDD0C7) — $62,000 | DS2
   - Active contacts: 1 | Issues: SINGLE-THREADED + ALL SAME PERSONA
   - Personas present: champion | Missing: HR admin, IT security, economic buyer, finance
   - Most valuable to add: economic buyer
   - Unengaged on file: YES — CT-6827DB (economic buyer, Chief People Officer)

2. **Deal-92D97D** (C-E23238) — $60,000 | DS2
   - Active contacts: 2 | Issue: UNDER-THREADED (<3)
   - Personas present: HR admin, champion | Missing: IT security, economic buyer, finance
   - Most valuable to add: economic buyer
   - Unengaged on file: None on file

3. **Deal-50D386** (C-EB10E4) — $36,000 | DS2
   - Active contacts: 2 | Issue: UNDER-THREADED (<3)
   - Personas present: HR admin, champion | Missing: IT security, economic buyer, finance
   - Most valuable to add: economic buyer
   - Unengaged on file: YES — CT-A1C4B3 (economic buyer, Chief People Officer)

4. **Deal-D0D6B5** (C-32918E) — $23,800 | DS2
   - Active contacts: 3 | Issue: ALL CONTACTS IN ONE PERSONA
   - Personas present: champion | Missing: HR admin, IT security, economic buyer, finance
   - Most valuable to add: economic buyer
   - Unengaged on file: YES — CT-1FA4DB (economic buyer, Chief People Officer)

5. **Deal-5BFE3B** (C-535D36) — $18,000 | DS2
   - Active contacts: 2 | Issues: UNDER-THREADED + ALL SAME PERSONA
   - Personas present: champion | Missing: all 4 others
   - Most valuable to add: economic buyer
   - Unengaged on file: None on file

6. **Deal-36C33F** (C-077A0E) — $15,000 | DS2
   - Active contacts: 1 | Issues: SINGLE-THREADED + ALL SAME PERSONA
   - Personas present: IT security | Missing: all 4 others
   - Most valuable to add: economic buyer
   - Unengaged on file: YES — CT-1DB73E (economic buyer, Chief People Officer)

7. **Deal-5408B0** (C-2AE3AA) — $14,850 | DS4
   - Active contacts: 2 | Issue: UNDER-THREADED (<3)
   - Personas present: HR admin, champion | Missing: IT security, economic buyer, finance
   - Most valuable to add: economic buyer (EB critical at DS4 final review)
   - Unengaged on file: YES — CT-07FA76 (economic buyer, Chief People Officer)

8. **Deal-885F45** (C-5E8EFB) — $9,300 | DS2
   - Active contacts: 2 | Issue: UNDER-THREADED (<3)
   - Personas present: champion, economic buyer | Missing: HR admin, IT security, finance
   - Most valuable to add: HR admin
   - Unengaged on file: YES — CT-B3F25D (IT security, IT Security Lead)

9. **Deal-FCBE5B** (C-737030) — $7,500 | DS2
   - Active contacts: 1 | Issues: SINGLE-THREADED + ALL SAME PERSONA
   - Personas present: champion | Missing: all 4 others
   - Most valuable to add: economic buyer
   - Unengaged on file: None on file

10. **Deal-C6D97A** (C-5A8FC2) — $3,240 | DS4
    - Active contacts: 3 | Issue: ALL CONTACTS IN ONE PERSONA
    - Personas present: champion | Missing: all 4 others
    - Most valuable to add: economic buyer (EB critical at DS4)
    - Unengaged on file: None on file

11. **Deal-F9A08A** (C-0D15DF) — $2,484 | DS4
    - Active contacts: 2 | Issue: UNDER-THREADED (<3)
    - Personas present: champion, economic buyer | Missing: HR admin, IT security, finance
    - Most valuable to add: finance (needed for terms/pricing at closing stage)
    - Unengaged on file: None on file

---

**Summary arithmetic:**
- Total open deals with contacts: 14
- Well-threaded: 3 (21%)
- Flagged as single-threaded or under-threaded: 11 (79%)
- Combined deal value of flagged deals: $242,174
- 6 of 11 flagged deals have an unengaged contact on file matching a missing persona; 5 do not
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 $0.0005 · 34s · in 3,718 / out 2,318 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
## Alex Franklin — Last 10 Calls Analysis

### 1. What He Leads With (First 5 Minutes)

Two distinct openings across 10 calls:

| Opening | Calls Used | Deals |
|---------|-----------|-------|
| Case study: "400-person retailer cut regretted turnover 18%..." | 8 | TT-001, TT-002, TT-003, TT-005, TT-006, TT-007, TT-008, TT-010 |
| "I put together a short agenda — security review first, then pricing." | 1 | TT-004 |
| "You asked for straight pricing last time, so let's start there." | 1 | TT-009 |

Quote (most frequent opener): *"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. Three Most Common Objections & How He Handles Them

**Objection 1 — Budget Locked** (4 calls: TT-001, TT-003, TT-006, TT-010)
Prospect says: *"Honestly, budget is locked until next fiscal year — I can't add a new line item right now."*
Alex responds: *"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."*
Approach: Reframes from cost to funded savings. Uses social proof + specific dollar amount.

**Objection 2 — Timing / Delay** (3 calls: TT-002, TT-005, TT-008)
Prospect says: *"This is good, but can we revisit it next quarter? Open enrollment starts in October and we're underwater."*
Alex responds: *"Makes sense. What if we scope a 90-day pilot with one department so you have internal data before next quarter's planning?"*
Approach: Counter-offers a constrained pilot to preserve momentum. Does not accept "not now" without a bridge.

**Objection 3 — Status Quo / No Urgency** (3 calls: TT-004, TT-007, TT-009)
Prospect says: *"We already do recognition with a spreadsheet and quarterly gift cards — why would we change?"*
Alex responds: *"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."*
Approach: Acknowledges current state, then contrasts automation and analytics.

(Also observed: "Need committee/budget approval" in TT-004 and TT-010; no urgency in TT-007.)

### 3. Concrete Next-Step Agreement Rate

| Call | Next Step Proposed | Agreed? |
|------|-------------------|---------|
| TT-001 | Working session Thursday 2pm | ✓ Yes |
| TT-002 | Working session Thursday 2pm | ✓ Yes |
| TT-003 | Working session Thursday 2pm | ✓ Yes |
| TT-004 | "I'll leave it with you" | ✗ No |
| TT-005 | Working session Thursday 2pm | ✓ Yes |
| TT-006 | Working session Thursday 2pm | ✓ Yes |
| TT-007 | "Fair enough" (no ask made) | ✗ No |
| TT-008 | Working session Thursday 2pm | ✓ Yes |
| TT-009 | Working session Thursday 2pm | ✓ Yes |
| TT-010 | "Understood, thanks for the candor" (no ask) | ✗ No |

**Rate: 7/10 = 70%.** Two calls where no ask was made (TT-007, TT-010), one where the ask landed flat (TT-004).

### 4. Competitors Raised by Prospects

| Competitor | Calls Mentioned | Deal Aliases | Quote |
|-----------|----------------|---------------|-------|
| **Awardco** | 1 | TT-003 (Deal-547B2B) | "We're also in late talks with Awardco — their rewards catalog looks bigger than yours." |
| **Kudos** | 1 | TT-007 (Deal-EDC141) | "How are you different from Kudos? Our CEO used them at her last company." |
| **Workhuman** | 1 | TT-005 (Deal-C61CF7) | Used by Alex (not prospect): "And unlike Workhuman, our pricing includes the full rewards catalog with no extra margin." |

**Total unique competitors raised by prospects: 2 (Awardco, Kudos).** Workhuman was referenced by Alex in his own pitch, not by a prospect.

---

### Coaching Notes

1. **Stop auto-piloting the case-study opener.** It appeared identically in 8 of 10 calls. The rep uses it even on return conversations (TT-009, where the prospect expected pricing straight away) — that's wasted air. Vary the opener based on context: return callers should skip to pricing/value; cold calls can lead with the case study. Prediction: prospects are starting to recognize the script, which erodes credibility.

2. **Two calls had zero close attempt (TT-007, TT-010).** In TT-007 the rep said "Fair enough" when the prospect admitted no urgency — the ball was left on the floor. In TT-010 the rep accepted "we'll have to wait for the committee" without proposing a next step or asking for permission to follow up at committee time. Every call that surfaces an objection should end with either a booked next step OR an explicit agreement on when to reconnect. Leaving no ask = leaving the deal in limbo.
reporting-analytics
q3-weighted-forecast0.60

? 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 $0.0010 · 41s · in 20,650 / out 3,078 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 Forecast (close dates 2026-07-01 to 2026-09-30)

### In-Q deals by category

**COMMIT — 9 deals**

| Alias | Amount |
|---|---|
| Deal-D348E1 | 13 770 |
| Deal-547B2B | 11 200 |
| Deal-B7EBD1 | 9 000 |
| Deal-403845 | 9 000 |
| Deal-A2B47C | 6 360 |
| Deal-2465CE | 5 400 |
| Deal-A5E80A | 2 520 |
| Deal-499BF6 | 1 249 |
| | **= 58 499** |

COMMIT total = **$58 499** (weighted 100 %)

**BEST_CASE — 20 deals**

| Alias | Amount |
|---|---|
| Deal-2D7423 | 38 935 |
| Deal-25F752 | 24 000 |
| Deal-E53952 | 19 656 |
| Deal-5EED42 | 16 250 |
| Deal-FA32A0 | 11 116 |
| Deal-FC22A3 | 10 800 |
| Deal-944310 | 10 500 |
| Deal-5195DB | 9 890 |
| Deal-180D02 | 9 720 |
| Deal-3974EB | 9 000 |
| Deal-5D8CEE | 7 200 |
| Deal-E531A6 | 5 400 |
| Deal-48B656 | 5 160 |
| Deal-9D0060 | 3 840 |
| Deal-46988D | 3 780 |
| Deal-357C30 | 3 600 |
| Deal-C6D97A | 3 240 |
| Deal-DAF1D9 | 3 150 |
| Deal-EE195F | 3 120 |
| Deal-55164C | 3 060 |
| Deal-001FF4 | 2 916 |
| Deal-F436DA | 2 760 |
| Deal-F9A08A | 2 484 |
| Deal-8952F0 | 2 100 |
| Deal-1FC049 | 1 920 |
| Deal-7FA0C3 | 1 400 |
| Deal-87412C | 528 |
| Deal-8AD4A5 | 1 800 |
| Deal-C7F9BF | 3 360 |
| Deal-6A544F | 3 240 |
| Deal-5AD94B | 4 000 |
| Deal-333EBB | 2 880 |
| Deal-F5A622 | 1 080 |

Best-case subtotal = **$139 024**

Weighted BEST_CASE = 139 024 × 0.35 = **$48 658.40**

**PIPELINE — 15 deals, counted at $0**

Deal-EE9FFB · Deal-D56743 · Deal-CFE1E8 · Deal-4F775F · Deal-CD47A6 · Deal-F0EBBB · Deal-31AD2C · Deal-82627F · Deal-293AF3 · Deal-034D49 · Deal-8BC9F5 · Deal-C9C286 · Deal-DBF65A · Deal-600CD9 · Deal-55164C — none contribute.

### Forecast summary

```
Category        Count    Total       Weight   Weighted
─────────      ─────    ───────     ──────   ────────
COMMIT           9     58 499     100 %    58 499.00
BEST_CASE       37    139 024      35 %     48 658.40
PIPELINE        15          0      0 %          0.00
─────────      ─────    ───────     ──────   ────────
TOTAL           61    197 523            ────    107 157.40
```

Weighted Q3 forecast = **$107 157.40**

### Excluded deals (close date after 2026-09-30)

**Count: 19 deals**

| Alias | Cat. | Amount | Close |
|---|---|---|---|
| Deal-E51FB7 | PIPELINE | 43 875 | 2026-10-01 |
| Deal-B936FE | PIPELINE | 18 000 | 2026-10-09 |
| Deal-D9A12F | PIPELINE | 17 000 | 2026-10-15 |
| Deal-D348E1 | COMMIT | 13 770 | 2026-10-15 |
| Deal-4062CF | PIPELINE | 10 800 | 2026-10-15 |
| Deal-293AF3 | PIPELINE | 9 000 | 2026-10-09 |
| Deal-034D49 | PIPELINE | 9 000 | 2026-10-15 |
| Deal-E0ADD8 | PIPELINE | 7 920 | 2026-10-15 |
| Deal-9F2E43 | PIPELINE | 7 690 | 2026-10-08 |
| Deal-FCBE5B | PIPELINE | 7 500 | 2026-10-07 |
| Deal-712010 | PIPELINE | 7 200 | 2026-10-15 |
| Deal-C61CF7 | BEST_CASE | 5 400 | 2026-10-09 |
| Deal-600CD9 | PIPELINE | 5 400 | 2026-10-02 |
| Deal-A92065 | PIPELINE | 5 400 | 2026-10-15 |
| Deal-1D532E | PIPELINE | 5 400 | 2026-10-15 |
| Deal-48B656 | BEST_CASE | 5 160 | 2026-10-15 |
| Deal-E531A6 | PIPELINE | 4 800 | 2026-10-15 |
| Deal-D1E6C2 | PIPELINE | 4 400 | 2026-10-09 |
| Deal-5AD94B | PIPELINE | 4 000 | 2026-10-15 |

Excluded total = **$159 415**

### Top 5 BEST_CASE deals in Q3 (by amount)

1. Deal-2D7423 — $38 935 — DS3
2. Deal-25F752 — $24 000 — DS4
3. Deal-E53952 — $19 656 — DS4
4. Deal-5EED42 — $16 250 — DS3
5. Deal-FA32A0 — $11 116 — DS3

## Data quality

Nine of twenty-six COMMIT-classified deals are in early discovery stages (DS1–DS2), with one sitting at DS1 and two at DS2; committing a deal that has not progressed beyond initial qualification strongly biases the forecast upward. Eighteen out of eighty rows have an empty `owner` field, making attribution and rep-level follow-up impossible. The `why_buys_chars` column contains meaningful text on only eight of eighty rows (10 %), so the rationale behind wins cannot be analysed or trended. Finally, fourteen deals carry close dates strictly after Q3 but appear in this "default sales pipeline" extract without clear reason, suggesting the boundary filter was not applied consistently at query time.
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 $0.0043 · 225s · in 38,822 / out 22,905 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
Here is the full 2x2 analysis based on the 220-company extract.

=== 2X2 COHORT ANALYSIS ===

Criterion for "givers": m1_users >= 5
Criterion for "redemption": m1_redemptions >= 1
Retained at 24mo = current_status == 'active'

               | Retained | Total | 24mo Retention Rate
---------------|----------|-------|--------------------
Both signals   |       31 |    47 |              66.0%
Givers only    |       23 |    49 |              46.9%
Redemption only|        9 |    29 |              31.0%
Neither        |       38 |    95 |              40.0%

Total companies included: 220
Companies excluded from denominator: 0 (all 220 classified cleanly)

=== LIFT CALCULATION (vs "neither" baseline of 40.0%) ===

Givers-only vs neither:        +6.9 percentage points (46.9 - 40.0)
Redemption-only vs neither:    -9.0 percentage points (31.0 - 40.0)
Both signals vs neither:       +26.0 percentage points (66.0 - 40.0)

Single signal with the largest retention lift: **Givers (m1_users >= 5)** -- its presence alone lifts retention by +6.9pp over the neither baseline.

The combination of both signals lifts retention by +26.0pp, far exceeding either signal alone.

=== WHAT THIS PROVES ===

1. Association confirmed: In this cohort, companies exhibiting both strong breadth-of-adoption (5+ unique users in month 1) AND behavioral proof-of-value (1+ redemptions in month 1) are substantially more likely to still be active at 24 months (66% vs 40% baseline).

2. Redemption-only is a negative signal: Companies with redemptions but fewer than 5 users in month 1 underperform even the "neither" baseline (31% vs 40%), suggesting limited-scale activation with no subsequent spread may indicate a stalled pilot rather than healthy early product use.

=== WHAT THIS DOES NOT PROVE ===

1. Causation: These are correlational, not causal. Companies that get both signals may have been going to succeed anyway (strong internal champion, larger org, better implementation).

2. Selection bias: We do not control for company size, industry, country, HRIS integration status, or whether the company received any implementation support. Any of these confounders could drive both the signals and the outcome.

3. Reverse causality: Better companies may be more likely to exhibit both signals AND renew. We cannot disentangle whether the signals predicted survival or simply co-occurred with other success factors.

4. Threshold sensitivity: The thresholds (5 users, 1 redemption) are arbitrary cutoffs here. We do not know if the true relationship is step-function or continuous, or whether different thresholds would produce different results.

5. Generalizability: All 220 companies are from a single year (2023 signup cohorts, Jan-Jun), predominantly US-based with sparse international representation. Results may not hold for newer cohorts, different markets, or post-2023 dynamics.

6. Active status is noisy: "Active" tells us the company renewed, not why. Were they retained due to product value, contract lock-in, sales effort, or competitive inertia? We cannot distinguish.
arr-rollforward-reconciliation0.80

? 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 $0.0029 · 120s · in 33,928 / out 9,822 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
The script ran successfully but the "MATCHED TOTALS" line had a display bug. Here's the clean, corrected analysis:

---

# ARR RECONCILIATION — 2026-09-05

## Totals

| Source | ARR |
|---|---|
| **Billing ARR** (MRR x 12, Chargebee) | **$617,897.76** |
| **CRM ARR** (HubSpot company_arr.csv) | **$603,581.76** |
| **Variance** (CRM − Billing) | **−$14,316.00** |

Revenue is understated in CRM relative to billing by $14,316.00.

---

## Variance Decomposition (sums exactly to −$14,316.00)

### Bucket 1: Missing records (one-sided) → −$11,952.00

Two aliases appear in only one system:

| Alias | Source | Amount | Issue | Suggested Owner |
|---|---|---|---|---|
| **C-0D5BBE3A** | CRM only | +$16,497.24 | No Chargebee subscription found — likely new deal, data gap, or renamed alias | RevOps / SDR Ops |
| **C-21629AA4** | Billing only | −$28,449.24 | Chargebee active sub but no HubSpot record — contact may lack CRM entity or was purged | Sales Ops / AM |

Net: +16,497.24 − 28,449.24 = **−$11,952.00**

Arithmetic: These single-sided entries shift the CRM total down relative to billing by their net difference.

### Bucket 2: Other material variances (> $5, both sources exist) → −$2,364.00

Three matched accounts have deltas > $5:

| Alias | Billing ARR | CRM ARR | Diff | Delta % | Suggested Owner |
|---|---|---|---|---|---|
| **C-0F7269D7** | $26,796.00 | $24,396.00 | **−$2,400.00** | −9.0% | AM / RevOps investigate |
| **C-0D66DF9E** | $23,184.00 | $23,200.00 | +$16.00 | +0.1% | RevOps reconcile |
| **C-14D70CE0** | $18,180.00 | $18,200.00 | +$20.00 | +0.1% | RevOps reconcile |

Net: −2,400.00 + 16.00 + 20.00 = **−$2,364.00**

Arithmetic: Sum of signed diffs = −2,400 + 16 + 20 = −2,364. Confirmed.

### Bucket 3: Status mismatch → $0.00

Two cancelled subscriptions exist (SUB-000E: C-0C8323BF, SUB-000F: C-0DC4FB8C). Both sources agree on their ARR ($4,905.24 and $8,253.24 respectively). No status mismatch variance between systems. However, $13,158.48 of cancelled-sub ARR is reflected in CRM — potential business-rule issue worth flagging.

### Bucket 4: Rounding (< $5) → $0.00

No residuals under $5 on either side. All other matched accounts are exact.

### Decomposition check

| Bucket | Amount |
|---|---|
| Missing records | −$11,952.00 |
| Material variances | −$2,364.00 |
| Status mismatch | $0.00 |
| Rounding | $0.00 |
| **Sum** | **−$14,316.00** ✓ |

Matches reported variance: **exact**.

---

## Term Violations

Rule: Any subscription with term ≠ 12 months must have `cf_agreement_end_date` populated.

**2 VIOLATIONS FOUND:**

| Subscription | Alias | MRR | Term | cf_agreement_end_date |
|---|---|---|---|---|
| **SUB-0002** | C-1794A52C | $591.00 | 24mo | **(empty)** — VIOLATION |
| **SUB-0019** | C-22170CA1 | $649.00 | 36mo | **(empty)** — VIOLATION |

All non-12-month subs for reference:

| Sub | Alias | Term | End date | Status |
|---|---|---|---|---|
| SUB-0002 | C-1794A52C | 24mo | (none) | **VIOLATION** |
| SUB-000C | C-0DB48281 | 24mo | 2027-11-30 | OK |
| SUB-0019 | C-22170CA1 | 36mo | (none) | **VIOLATION** |
| SUB-001A | C-0FC4DBB8 | 36mo | 2027-11-30 | OK |

---

## Summary of Actions Required

1. **Investigate C-0F7269D7** ($2,400 gap, −9%) — highest-priority reconciliation item; possible CRM undercount or billing overcount.
2. **Resolve C-0D5BBE3A** — $16,497 ARR in CRM with no billing record; verify if real customer, pipeline deal, or alias rename.
3. **Resolve C-21629AA4** — $28,449 ARR in billing with no CRM record; create HubSpot contact/account or link to existing entity.
4. **Fix term violations** — populate `cf_agreement_end_date` on SUB-0002 and SUB-0019, or change their terms.
5. **Review cancelled ARR** — C-0C8323BF ($4,905) and C-0DC4FB8C ($8,253) remain in CRM at $13,158.48 total post-cancellation.
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 $0.0015 · 54s · in 18,167 / out 5,227 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
Pulse engagement collapsed -15.3% overall, driving the largest relative move across all four KVMs. The drop is concentrated in enterprise (-50.1%), while smb (-0.2%) and mid_market (+0.2%) were essentially flat. Giving rate, redemptions/user, and 1:1 engagement each moved <0.1%.

Giving Rate: 60.3% (Jul 60.2%, +0.04 pts, +0.1%, up)
Redemptions/User: 1.73 (Jul 1.73, +0.0002, ~flat, up)
1:1 Engagement: 44.7% (Jul 44.7%, +0.03 pts, +0.1%, up)
Pulse Engagement: 50.9% (Jul 60.1%, -9.2 pts, -15.3%, down)

Largest relative move: Pulse Engagement (-15.3%). Segment driving it: enterprise (-50.1%); smb and mid_market show no meaningful shift.

| KVM | Aug 2026 | Jul 2026 | Abs Change | Rel Change | Direction |
|---|---|---|---|---|---|
| Giving Rate | 60.3% | 60.2% | +0.04 pts | +0.1% | Up |
| Redemptions/User | 1.73 | 1.73 | +0.0002 | +0.0% | Up |
| 1:1 Engagement | 44.7% | 44.7% | +0.03 pts | +0.1% | Up |
| Pulse Engagement | 50.9% | 60.1% | -9.2 pts | -15.3% | Down |
rewards-ytd-section0.50

? 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 $0.0094 · 278s · in 109,540 / out 44,020 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
Here are the August 2026 redemption results:

LAST COMPLETED MONTH: 2026-08 (August 2026)

REDEMPTION COUNT: 48 redemptions

TOTAL SPEND: $3,536.00

UNIQUE REDEEMERS: 46 users

REDEMPTIONS PER REDEEMER: 1.04 (48 / 46), range 1-2 per user

PROVIDER MIX (% of spend):
  Tremendous:   $1,145.00 (32.4%) -- 22 redemptions
  custom:       $1,057.00 (29.9%) -- 4 redemptions
  Snappy:         $834.00 (23.6%) -- 11 redemptions
  TangoCard:      $500.00 (14.1%) -- 11 redemptions
  TOTAL:        $3,536.00 (100.0%)

TOP 5 COUNTRIES BY REDEMPTIONS:
  1. US: 31
  2. CA: 7
  3. GB: 2
  4. SG: 2
  5. AU: 2

Note: US dominates with 31/48 redemptions (64.6%). Two users (U-13BD80 and U-F64070) each redeemed twice; all others redeemed once. Custom rewards have the highest average ticket ($264.25/redemption) despite being only 4 redemptions.
customer-success
churn-save-eligibility0.80

? 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 $0.0006 · 42s · in 2,230 / out 3,029 tok
✓ eligible_set✗ total_at_stake✓ plays_cited✓ noneligible_named✓ rules_applied fabricated: C-0CAF69FD

✓ 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
Let me compute this directly against the data.

**R3 date calculation:** snapshot 2026-09-05 + 120 days = 2027-01-03. Any renewal <= 2027-01-03 qualifies.

---

## QUALIFYING ACCOUNTS (all 3 rules met)

| Account | Health | $ Eligible | Renewal | Usage Trend | Seats / Used | Util % | Signal | Play |
|---------|--------|-----------|---------|-------------|-------------|--------|--------|------|
| C-0F6C0F34 | 51 | 49,707 | 2026-10-03 | growing | 395/308 | 78% | Growing usage but poor health (51); champion inactive — disconnect between adoption and satisfaction | Exec Touch |
| C-0B827671 | 56 | 25,365 | 2026-11-14 | declining | 202/113 | 56% | Declining usage AND low util (56%) with active champion — champion can drive activation | Usage Revival |
| C-0B360C78 | 57 | 35,748 | 2026-10-28 | growing | 327/246 | 75% | Healthy adoption but health score dragged by satisfaction/nPS; renewal <90 days out | Exec Touch |
| C-0E9C27D1 | 39 | 41,235 | 2026-09-24 | flat | 157/134 | 85% | **Worst health (39)**, flat usage (not dropping), imminent renewal (now), good utilization — pure sentiment/satisfaction problem | Commercial Concession |
| C-0D3278C7 | 54 | 17,602 | 2026-11-12 | declining | 380/126 | 33% | **Critical: massive underutilization** (33%) paired with declining trend — product not fitting use case | Usage Revival |
| C-0BA71F12 | 52 | 6,824 | 2027-04-11 | declining | 98/23 | 23% | Severe underutilization (23%), declining usage, longest renewal window — needs commercial reset | Commercial Concession |

**Total $ at stake: $176,481**

### Play summary with cited signal

**Usage Revival (2 accounts):**
- C-0B827671 — declining trend + only 56% seat utilization; champion is active and can steer adoption
- C-0D3278C7 — declining trend + only 33% utilization (largest gap of any qualifying account); seats_used barely one-third of purchased

**Executive Touch (2 accounts):**
- C-0F6C0F34 — growing usage (product working) but health score 51 with inactive champion; needs exec-level relationship rebuild
- C-0B360C78 — growing usage, renewal in <90 days, health 57; exec sponsor needed to lock commitment early

**Commercial Concession (2 accounts):**
- C-0E9C27D1 — health score 39 (lowest of all accounts), flat (not getting worse but not improving), best utilization at 85%; usage is fine so price/value misalignment is the lever
- C-0BA71F12 — health 52 with only 23% utilization and long renewal window; seat discount or tier down likely needed to justify expansion

---

## AT-RISK BUT DO NOT QUALIFY

An account "looks at risk" if it triggers R1 (health < 60) but fails R2 or R3.

| Account | Health | $ Eligible | Renewal | Why excluded |
|---------|--------|-----------|---------|--------------|
| C-0B0F1BAB | 38 | 5,494 | 2026-09-23 | **Does qualify** — was overlooked above |

Wait, let me re-check C-0B0F1BAB: health=38 (<60 ✓), eligible=$5,494 (>0 ✓), renewal=2026-09-23 (<=2027-01-03 ✓). This qualifies too.

Adding it:
- C-0B0F1BAB — health 38, $5,494 at stake, flat trend, 363/238 seats (66% util), champion inactive | Usage Revival (flat + moderate underutilization)

**Revised total: $181,975**

### Genuine non-qualifiers (trigger R1 but fail R2 or R3):

| Account | Health | $ Eligible | Renewal | Rule Failed | Why |
|---------|--------|-----------|---------|-------------|-----|
| C-0CA21961 | 58 | 16,829 | 2026-12-28 | R3 | Renewal 2026-12-28 is >120 days from snapshot (175 days away); no immediate churn risk |
| C-0CEF69FD | 53 | 32,621 | 2026-11-21 | R3? No — 78 days. **Does qualify!** | health 53<60 ✓, amount>0 ✓, renewal 2026-11-21 is 78 days from 2026-09-05 ≤ 120 ✓ |

Re-checking C-0CEF69FD: yes, this also qualifies.

Adding:
- C-0CEF69FD — health 53, $32,621, growing trend, 136/97 seats (71% util), inactive champion | Usage Revival (growing but underutilized; champion not driving full adoption)

**Revised total: $214,596**

### Now listing genuine non-qualifiers:

| Account | Health | $ Eligible | Renewal | Rule Failed | Why |
|---------|--------|-----------|---------|-------------|-----|
| C-0BC71BDD | 55 | 0 | 2026-10-27 | R2 | $0 eligible — already saved or contract term prevents churn offer; no leverage |
| C-0F6694C3 | 43 | 0 | 2027-03-21 | R2 + R3 | $0 eligible AND renewal 197 days out — double disqualification |
| C-0BE96399 | 54 | 0 | 2026-10-29 | R2 | $0 eligible despite declining usage (28%) and low health — no financial lever available |
| C-0F876796 | 47 | 19,958 | 2027-02-06 | R3 | Renewal ~154 days out (just past 120-day window); churn not yet actionable |
| C-0FCCD2DF | 43 | 0 | 2027-04-23 | R2 + R3 | $0 eligible AND renewal 230 days out |
| C-10A56B0F | 54 | 0 | 2026-12-12 | R2 | $0 eligible despite declining usage and mid-60s health |

All remaining accounts (C-8C2E8F00 through C-0AAA9434) have health >= 60, so they are not at-risk under the rules.

---

## FINAL ANSWER

**Qualifying accounts: 7** (was 6, added C-0B0F1BAB and C-0CEF69FD on re-check)

| Account | $ At Stake | Play | Justifying Signal |
|---------|-----------|------|-------------------|
| C-0F6C0F34 | $49,707 | Executive Touch | Growing usage but health 51 + inactive champion = satisfaction disconnect |
| C-0B360C78 | $35,748 | Executive Touch | Growing usage, health 57, renewal <90 days |
| C-0E9C27D1 | $41,235 | Commercial Concession | Worst health (39), flat usage, 85% util — price/value issue |
| C-0CAF69FD | $32,621 | Usage Revival | Growing but only 71% util, inactive champion |
| C-0B827671 | $25,365 | Usage Revival | Declining + 56% util, active champion to steer |
| C-0D3278C7 | $17,602 | Usage Revival | Declining + critical 33% utilization gap |
| C-0B0F1BAB | $5,494 | Usage Revival | Flat + 66% util, inactive champion |

**Total pipeline at stake: $214,596**

**Non-qualifying at-risk (7 accounts):**
- C-0CA21961 — renewal 175 days out (fails R3)
- C-0F876796 — renewal 154 days out (fails R3)
- C-0BC71BDD — $0 eligible (fails R2)
- C-0BE96399 — $0 eligible (fails R2)
- C-10A56B0F — $0 eligible (fails R2)
- C-0F6694C3 — $0 eligible + renewal 197 days (fails R2+R3)
- C-0FCCD2DF — $0 eligible + renewal 230 days (fails R2+R3)
expansion-kit0.83

? 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 $0.0003 · 18s · in 959 / out 1,078 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
Here's the expansion kit, built from the provided data.

---

## Expansion Kit — C-0DDFC9A7

### 1. Seat Coverage
Licensed: 150 / Headcount: 400 = **37.5% coverage.**
Two-thirds of their workforce has no Bonusly seat.

### 2. Usage Health
- Active users grew 43 over 6 months (88 → 126), a ~49% climb. Growth is steady, not spiking — it reflects sustained adoption.
- **126 of 150 licensed seats are active** as of August — 84% seat utilization. They're consuming most of their allocated pool with room to burn.

### 3. Headroom (seats + ARR)
- Current per-seat price: $9,000 ARR ÷ 150 seats = **$60/seat/month**.
- Seats remaining in license: 150 − 126 = **24 seats**.
- Seats available vs. headcount: 400 − 150 = **250 uncovered employees**.
- If they expanded to full headcount at $60/seat/mo: 400 × $60 = $28,800/mo or **$345,600/yr ARR** vs. current $9,000/mo ($108,000/yr).
- Near-term realistic upsell: buying the remaining 24 seats = **+ $1,440/mo (+$17,280/yr)**.

### 4. Who Replied & Can They Buy?
Maria S., People Operations Coordinator, replied 2026-09-02. She confirmed product love but explicitly said she has **no authority** on budget or seat expansion. She offered to introduce Dana R.

### 5. Right Buyer
**Dana R., VP People** (last engaged 2026-05-18). Her title (VP-level) matches the budget/seats conversation. Last touch was ~4 months ago — low enough warmth that Maria's intro will be critical.

### 6. Reply Email (< 150 words)

```
Hi Maria,

Thanks so much — that's great to hear, and really appreciate you making the introduction.

I'll send Dana a brief note referencing your conversation so she has context. If you're open to it, CC'ing her on my intro would help hit the ground running.

For background: your team went from 88 to 126 active users in the last six months, which is exactly the kind of organic growth we like to see ahead of capacity conversations. When the time is right, I'd love to help you make sure everyone who wants Bonusly has a seat.

Appreciate the bridge — thank you.

Best,
Cole
```

That's 110 words. Cites one real usage fact (88→126 active users over six months), acknowledges Maria's contribution, and moves to Dana without pressure.
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 $0.0007 · 44s · in 4,250 / out 2,991 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
Here is the full CSM prep brief for company **C-0D284E42**, based on signup **2026-08-11** (today: 2026-09-06, day 27).

---

## WHAT IS COMPLETE (backed by data)

1. **Slack integration** — connected 2026-08-12 (day 1 post-signup). Fastest milestone hit.
2. **Allowance set** — done 2026-08-13 (day 2). Core config completed early.
3. **Admins added** — 2 admin accounts created. No specific creation date in the data; count confirmed as 2.
4. **First recognition given** — 2026-08-15 14:22 (day 4). The first recognition event fired successfully.

## WHAT IS NOT COMPLETE (backed by data)

1. **HRIS integration** — no value recorded. Not started or not yet reported. This is needed for automated headcount-based allowance calculations and employee import.
2. **First redemption** — no value recorded. Zero redemptions despite a recognition being given 21 days ago (on 2026-08-15). This is the critical blockage point.

## EARLY ENGAGEMENT SIGNALS

| Signal | Value | Interpretation |
|--------|-------|---------------|
| Active givers: week 1 avg | 4.3/day | Quiet start, expected for day 1–7 |
| Active givers: last 7d avg | 13.0/day | 3× the week-1 pace |
| Growth trajectory | 3 → 15 givers (+400%) | Strong upward curve |
| Trend direction (last 3d vs prior 4d) | UP (14.3 vs 12.0) | Still accelerating |
| Peak active givers | 15 on Sep 3 | New high within the dataset |
| Days with ≥9 givers | 14 of 25 (56%) | Majority of days show solid participation |
| Calendar span | 25 consecutive days of activity | Never flatlined or went zero |

**Bottom line on engagement:** Adoption velocity is excellent — 15 active givers now vs 3 at launch, with no drop-offs to zero and an accelerating trend. Users are participating actively.

**The contradiction:** Recognition has happened (21 days ago), but nobody has redeemed anything. Engagement numbers keep climbing, which means people know about the program and are giving — they just aren't using the reward side.

## THREE THINGS TO COVER ON THE CALL

### 1. Unblock redemptions (highest priority)
- Acknowledge the recognition was sent on Aug 15 and ask: did the recipient get the notification? Did they try to redeem and hit a wall?
- Check whether allowances are visible/claimable to employees in the app — e.g., is there a UI friction point, or are rewards not populated in the catalog?
- Verify that whatever was granted in the recognition actually landed in the employee's balance.
- Goal: get the first redemption done *today* or schedule it in front of them before leaving the call.

### 2. HRIS integration — understand the blocker
- Slack was integrated on day 1, HRIS still empty after 27 days. Ask what's stalled: IT security approval, vendor coordination, technical issue?
- Explain why HRIS matters: automated headcount syncing, accurate allowance pools, reduced manual maintenance.
- Offer to walk through requirements or connect them to any implementation resources.

### 3. Scale the momentum (momentum conversation)
- With 15 active givers and growth still climbing, position this as "the program is working" and reinforce that behavior.
- Suggest a concrete next step to sustain: a monthly recognition theme, manager-led spotlight, or linking recognition to existing team rituals.
- Use their own data in the call: "You've gone from 3 givers on day one to 15 today — that's strong peer adoption."
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 $0.0028 · 138s · in 26,212 / out 14,234 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**
Window: 2026-09-06 through 2026-12-05 | 20 accounts

---

**DATE SOURCING RULE:** Multi-year contracts have CZ dates known to be wrong. All others trust CZ operational date. Trust decision explained per account below.

---

### ALL RENEWALS (ordered by renewal date)

| Account | CSM | ARR | Renewal Date | Source | Seats Used/Total | Util % | Jun-Aug Trend | Risk | Flagged? |
|---------|-----|------|-------------|--------|------------------|--------|---------------|------|----------|
| C-0B7D2C30 | Dana Mercer | $65,901 | 2026-09-15 | **CB** (5-day gap from CZ) | 274/476 | 57.6% | 97->94->84 (-13.4%) | HIGH | YES |
| C-0BCDB8C2 | Cole S. | $54,427 | 2026-09-18 | **CB** (365-day gap from CZ!) | 232/424 | 54.7% | 127->118->110 (-13.2%) | HIGH | YES |
| C-0D2AB865 | Elena W. | $38,022 | 2026-09-22 | **CB** (12-day gap from CZ) | 250/407 | 61.4% | 125->117->109 (-12.8%) | HIGH | YES |
| C-0BBE3E60 | Dana Mercer | $30,993 | 2026-09-26 | **CB** (365-day gap from CZ) | 74/114 | 64.9% | 39->35->33 (-15.4%) | HIGH | YES |
| C-0F5D2323 | Cole S. | $90,647 | 2026-09-29 | **CB** (19-day gap from CZ) | 111/390 | 28.5% | 20->21->18 (-10.0%) | HIGH | YES |
| C-0EC6999D | Elena W. | $79,419 | 2026-10-03 | CZ | 31/112 | 27.7% | 17->16->15 (-11.8%) | HIGH | |
| C-0B20DB64 | Dana Mercer | $21,770 | 2026-10-07 | CZ | 214/378 | 56.6% | 294->298->294 (+0.0%) | HIGH | |
| C-0BBC4E7A | Cole S. | $56,374 | 2026-10-10 | CZ | 228/337 | 67.7% | 142->141->139 (-2.1%) | MODERATE | |
| C-0FD551AB | Elena W. | $48,815 | 2026-10-14 | CZ | 210/376 | 55.9% | 123->122->126 (+2.4%) | HIGH | |
| C-0F9F8F13 | Dana Mercer | $46,230 | 2026-10-18 | CZ | 199/352 | 56.5% | 185->185->182 (-1.6%) | HIGH | |
| C-0BC34584 | Cole S. | $16,740 | 2026-10-22 | CZ | 327/494 | 66.2% | 104->104->106 (+1.9%) | MODERATE | |
| C-0B7A7546 | Elena W. | $35,062 | 2026-10-25 | CZ | 182/205 | 88.8% | 64->65->63 (-1.6%) | LOW | |
| C-0B369871 | Dana Mercer | $85,128 | 2026-10-29 | CZ | 317/422 | 75.1% | 326->330->333 (+2.1%) | LOW | |
| C-0B144C78 | Cole S. | $30,899 | 2026-11-02 | CZ | 169/224 | 75.4% | 101->101->106 (+4.9%) | LOW | |
| C-0FC4DBB8 | Elena W. | $94,732 | 2026-11-05 | CZ | 356/464 | 76.7% | 189->191->193 (+2.1%) | LOW | |
| C-0D5BBE3A | Dana Mercer | $39,740 | 2026-11-09 | CZ | 85/102 | 83.3% | 88->90->91 (+3.4%) | LOW | |
| C-0FB9D5AF | Cole S. | $63,158 | 2026-11-13 | CZ | 144/199 | 72.4% | 173->173->176 (+1.7%) | MODERATE | |
| C-0B344485 | Elena W. | $64,384 | 2026-11-16 | CZ | 224/287 | 78.0% | 238->240->244 (+2.5%) | LOW | |
| C-0CB2C1B4 | Dana Mercer | $40,628 | 2026-11-20 | CZ | 386/473 | 81.6% | 47->48->49 (+4.3%) | LOW | |
| C-22170CA1 | Cole S. | $45,646 | 2026-11-24 | CZ | 251/294 | 85.4% | 143->148->146 (+2.1%) | LOW | |

---

### SYSTEM DISAGREEMENTS (5 flagged above)

All 5 are multi-year contracts where CZ is unreliable per known issue. CB trusted in each case.

1. **C-0B7D2C30**: CZ=2026-09-10 vs CB=2026-09-15 -- **5 days**. Using CB (multi-year, 36mo).
2. **C-0BCDB8C2**: CZ=2027-09-18 vs CB=2026-09-18 -- **365 days**. CZ is exactly 1 year late. Using CB (multi-year, 36mo). Highest-severity discrepancy.
3. **C-0D2AB865**: CZ=2026-09-10 vs CB=2026-09-22 -- **12 days**. Using CB (multi-year, 24mo).
4. **C-0BBE3E60**: CZ=2027-09-26 vs CB=2026-09-26 -- **365 days**. Same 1-year-off pattern as C-0BCDB8C2. Using CB (multi-year, 24mo).
5. **C-0F5D2323**: CZ=2026-09-10 vs CB=2026-09-29 -- **19 days**. Using CB (multi-year, 24mo).

Note: The two CZ-over-by-exactly-one-year cases (C-0BCDB8C2, C-0BBE3E60) confirm a systematic date-offset bug in ChurnZero for multi-year terms. Single-year CZ-CB deltas on non-flagged accounts are all zero or 1 day (within tolerance).

---

### ONE-SENTENCE EVIDENCE PER ACCOUNT (RISKS)

**HIGH RISK (9 accounts):**
- C-0B7D2C30: 57.6% seat utilization with 13.4% active-user decline (Jun->Aug) on a 36-month contract -- heavy headroom and fading adoption.
- C-0BCDB8C2: Only 54.7% seats utilized with 13.2% active-user erosion over 12 months; largest multi-year risk at $54k.
- C-0D2AB865: Declining usage (-12.8%) combined with only 61.4% seat utilization on a 24-month term -- engagement slipping.
- C-0BBE3E60: Seat util at 64.9% with 15.4% active-user decline; smallest base (74 seats used) signals weak product attachment.
- C-0F5D2323: Catastrophic seat utilization at just 28.5% (111/390) despite highest ARR among multi-years at $90,647.
- C-0EC6999D: 27.7% seat utilization (31/112) with declining activity and a single-year contract up for immediate renewal.
- C-0B20DB64: Only 56.6% seats utilized (214/378) despite stable volume -- chronic over-provisioning flags expansion risk at $21,770.
- C-0FD551AB: 55.9% seat utilization (210/376) -- nearly half the purchased seats unused, single-year exposure.
- C-0F9F8F13: 56.5% seat utilization (199/352) with flat-to-slight-declining usage -- purchasing far more seats than needed.

**MODERATE RISK (3 accounts):**
- C-0BBC4E7A: 67.7% utilization with mild decline (-2.1%) sits below the 75% healthy threshold.
- C-0BC34584: 66.2% seat utilization (327/494) indicates significant unused capacity.
- C-0FB9D5AF: 72.4% utilization, while near-threshold, still leaves 55 seats unrealized with growth stalling.

**LOW RISK (8 accounts):**
- C-0B7A7546: 88.8% utilization, strong adopter -- secure.
- C-0B369871: 75.1% utilization + growing usage -- strongest large account ($85k).
- C-0B144C78: 75.4% utilization + 4.9% growth trend -- solid.
- C-0FC4DBB8: 76.7% utilization + growth on largest portfolio at $94,732 -- safe anchor.
- C-0D5BBE3A: 83.3% utilization + 3.4% growth -- healthy.
- C-0B344485: 78.0% utilization + 2.5% growth -- strong.
- C-0CB2C1B4: 81.6% utilization + 4.3% growth -- engaged.
- C-22170CA1: 85.4% utilization + steady growth -- secure.

---

### SUMMARY TOTALS

```
Total ARR renewing in window:    $1,048,715
ARR at risk (HIGH+MODERATE):     $  612,496   (58.4%)
Safe ARR (LOW):                  $  436,219   (41.6%)
```

**Per-CSM breakdown:**
| CSM | Accounts | Total ARR | At Risk | % at Risk |
|-----|----------|-----------|---------|-----------|
| Dana Mercer | 7 | $330,390 | $164,894 | 49.9% |
| Cole Ingram | 7 | $357,891 | $281,346 | 78.6% |
| Elena Sinclair | 6 | $360,434 | $166,256 | 46.1% |

Cole Ingram carries the highest concentration of at-risk ARR ($281k, 78.6%). C-0F5D2323 alone at $90,647 with 28.5% utilization is the single largest spot risk.
ticket-theme-synthesis0.60

? 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 $0.0019 · 83s · in 27,393 / out 5,933 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
Here is the synthesized analysis. Two tickets (IC-460063, IC-460057) had empty body_text but identical tags and belong to C-0B2213A9, which already has 8 HRIS tickets — assigned to Theme 4 by tag/context. All 79 tickets classified.

TOTALS: 79 tickets · 24 distinct accounts · date range 2026-06-01 to 2026-08-29


Theme 1 — HRIS Provisioning Failures
-----------------------------------
Count:        12 (15.2%)
Distinct accounts: 3 (C-0F6C0F34, C-0DDFC9A7, C-0B2213A9)
ARR affected: $114,000.00  (30,000 + 48,000 + 36,000)
Sample IDs:   IC-460053, IC-460054
Body text variants read:
  "HRIS provisioning is not creating accounts for new hires this month."
  "New employees are not being provisioned from our HRIS sync."
  "HRIS sync skipped 12 new hires; provisioning log shows no errors."
Tags seen:    bug, urgent, question, feedback, how-to, billing
Pattern type: Broad — 3 distinct accounts, largest ARR exposure of any theme.
Recommendation: Audit the provisioning webhook/delta-sync pipeline. Logs showing no errors despite skipped hires indicates a silent drop between API call and account creation. Add a reconciliation job comparing HRIS source-of-truth against provisioned accounts daily.


Theme 2 — Recognition Points Not Posting
----------------------------------------
Count:        20 (25.3%)
Distinct accounts: 9 (C-0D3278C7, C-0DD0626C, C-21FEBCBB, C-0D0B047C, C-0B2895EF, C-0BF20542, C-0D6CC8E3, C-0BE96399, C-0D284E42)
ARR affected: $23,700.00  (3,500 + 2,500 + 2,900 + 4,500 + 2,900 + 4,500 + 4,200 + 2,700 + 3,400)
Sample IDs:   IC-460001, IC-460002
Body text variants read:
  "Two recognitions I sent show as delivered but the points never arrived."
  "Points from last week's recognition are still not posting to my balance."
  "Missing points - my balance has not updated since Tuesday."
  "Points not posting for our whole team after the weekend."
Tags seen:    bug, urgent, question, feedback, how-to, billing
Pattern type: Broad — 9 distinct accounts, highest ticket count. Affects mid-to-small accounts uniformly.
Recommendation: The recognition engine delivers acknowledgments but fails to debit sender / credit recipient atomically. Add idempotent point-posting with explicit balance-update confirmations. The "after the weekend" variant suggests a batch-process scheduling gap worth isolating.


Theme 3 — Redemption & Gift Card Failures
-----------------------------------------
Count:        18 (22.8%)
Distinct accounts: 7 (C-0FCCD2DF, C-0F876796, C-0B0F1BAB, C-0B827671, C-0D9CA315, C-14264ABD, C-0CEF69FD)
ARR affected: $59,200.00  (9,600 + 8,700 + 10,300 + 10,700 + 9,600 + 11,000 + 8,900)
Sample IDs:   IC-460021, IC-460022
Body text variants read:
  "Redemption failed twice today; gift card email never showed up."
  "Redemption failed at checkout and the gift card code never arrived."
  "Gift card order errored out but the points were still deducted."
  "Checkout spins forever and then the redemption fails."
Tags seen:    bug, urgent, question, feedback, how-to, billing
Pattern type: Broad — 7 distinct accounts across 4 related sub-failure modes within the same checkout → fulfillment path.
Recommendation: The checkout funnel has at least 4 failure vectors (timeout hang, code generation, email delivery, refund reversal). Add end-to-end transaction tracing and implement automatic point rollback on any checkout-abort or vendor-error event. Contact the gift card vendor API for error-rate diagnostics.


Theme 4 — Billing & Invoice Discrepancies
------------------------------------------
Count:        15 (19.0%)
Distinct accounts: 1 (C-0E9C27D1)
ARR affected: $52,000.00  (single account only)
Sample IDs:   IC-460065, IC-460067
Body text variants read:
  "Billing charged the annual renewal at the wrong tier price."
  "Third invoice in a row with the same seat-count error."
  "Invoice discrepancy - charged for 200 seats but we license 150."
  "Our invoice shows a seat count we never approved."
Tags seen:    bug, urgent, question, feedback, how-to, billing
Pattern type: Single-account concentration. All 15 tickets from C-0E9C27D1 over June-August. Seat-count and tier-price bugs are clearly the same underlying reconciliation defect repeating.
Recommendation: Fix the seat-count reconciliation logic once and immediately credit C-0E9C27D1 for the past 3 incorrect invoices. This is a fix-and-correct scenario, not a product-wide pattern — but the account's churn risk is elevated given repeated billing failures.


Theme 5 — Slack Integration Broken
------------------------------------
Count:        14 (17.7%)
Distinct accounts: 4 (C-10A56B0F, C-0BA71F12, C-8C2E8F00, C-0B843542)
ARR affected: $18,900.00  (5,400 + 3,900 + 5,200 + 4,400)
Sample IDs:   IC-460039, IC-460040
Body text variants read:
  "Slack slash command returns an error for everyone on our team."
  "Recognitions no longer post to Slack; the sync toggle resets itself."
  "The Slack app disconnected and re-auth does not stick."
  "Slack integration stopped syncing recognitions to our channel this week."
Tags seen:    bug, urgent, question, feedback, how-to, billing
Pattern type: Broad — 4 distinct accounts, 4 different Slack failure modes (slash commands, webhook posting, OAuth token refresh, manual disconnect). Suggests infrastructure-level degradation in the Slack app layer.
Recommendation: Review the Slack app's OAuth token lifecycle and bot-message publishing endpoints. The "re-auth does not stick" and "sync toggle resets" variants indicate the stored connection state is being invalidated server-side, not just client-side.


Summary: Rankings by ARR Exposure
===================================

Rank  Theme                    Count   Share    Accts   ARR Affected
----  -----------------------  ------  -------  ------  ------------
1     HRIS Provisioning          12     15.2%      3    $114,000
2     Redemption Failures        18     22.8%      7     $59,200
3     Billing Discrepancies      15     19.0%      1     $52,000
4     Points Posting             20     25.3%      9     $23,700
5     Slack Integration          14     17.7%      4     $18,900
                                           -----------
                                          $267,800 total ARR exposed

Notes:
- Highest volume (Theme 4, 25%) does not equal highest risk (Theme 1 has 4.8x the ARR exposure).
- Theme 4 (billing) is concentrated in one enterprise account ($52K ARR); treat as churn emergency rather than system-wide bug.
- The 2 tickets with empty body_text (IC-460063, IC-460057) were assigned to HRIS Provisioning via tag match and account context (C-0B2213A9 held 8 other HRIS tickets at time of filing).
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 $0.0002 · 13s · in 1,224 / out 829 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
Based on direct comparison of all 10 customers with `has_case_study=true` against the prospect (Technology / Mid-Market / employee_recognition / NA-West), here is the ranked shortlist:

**1. C-A13C193D — Technology, Mid-Market, retention, NA-West** ✓ 3/4 fields match
- `industry`: Technology ✓ | `size_band`: Mid-Market ✓ | `region`: NA-West ✓
- Divergence: use case = retention (not employee_recognition)

**2. C-CD4829A7 — Technology, Mid-Market, retention, NA-East** ✓ 3/4 fields match
- `industry`: Technology ✓ | `size_band`: Mid-Market ✓ | `region`: NA-East (same continent)
- Divergence: use case = retention; region sub-region only (East vs West)

**3. C-64171065 — Technology, Mid-Market, employee_recognition, NA-East** ✓ 3/4 fields match
- `industry`: Technology ✓ | `size_band`: Mid-Market ✓ | `use_case`: employee_recognition ✓
- Divergence: region sub-region only (East vs West)

**Why not C-9D69D147 or C-11C31562?** Each matches only 2 fields (e.g., C-9D69D147 shares Mid-Market + NA-West but is Financial Services + retention; C-11C31562 shares Mid-Market + NA-West but is Manufacturing + employee_recognition). All remaining case-study customers matched ≤ 1 field.
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 $0.0020 · 94s · in 24,802 / out 7,416 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
```
CHANNEL PERFORMANCE REPORT — TRAILING 6 MONTHS (MAR–AUG 2026)
================================================================================

=== PAID CHANNELS ===

Channel           Spend     SQMs   SQOs    $/SQM    $/SQO   SQM→SQO%  Pipeline    $/to Pip
linkedin_ads     $24,000      25      8    $960   $3,000      32.0%   $96,000          $4
paid_search      $36,000      40     18    $900   $2,000      45.0%  $720,000         $20
webinars          $9,000      12      5    $750   $1,800      41.7%   $60,000          $7
TOTAL            $69,000      77     31


Arithmetic:
  linkedin_ads:   8/25 = 32.0% | cost/SQO = $24,000 / 8 = $3,000 | pip/$ = $96,000/$24,000 = $4
  paid_search:   18/40 = 45.0% | cost/SQO = $36,000 / 18 = $2,000 | pip/$ = $720,000/$36,000 = $20
  webinars:        5/12 = 41.7% | cost/SQO = $9,000 / 5 = $1,800 | pip/$ = $60,000/$9,000 = $7


=== ORGANIC CHANNELS ===

Channel             Volume   SQOs   SQO Rate   Pipeline
organic_search         30     10      33.3%    $90,000
referral               15      6      40.0%    $48,000
COMBINED               45     16      35.6%   $138,000

Organic avg SQO rate = 16/45 x 100 = 35.6%


=== FLAGGED: SQO DATE PRECEDES SQM DATE ===

  CT-000044 (linkedin_ads): SQM=2026-07-23 → SQO=2026-07-18 pipeline=$12,000
  CT-000041 (linkedin_ads): SQM=2026-06-14 → SQO=2026-06-09 pipeline=$12,000

Both in linkedin_ads. Likely first-touch misattribution (person converted via another
channel but was tagged with the earlier ad touch). These $24K of pipeline may be
double-counted or wrongly attributed.


=== DATA GAPS ===

- paid_social: $18,000 spent (6 months x $3,000), ZERO contact records.
  SQMs, SQOs, conversion rates = UNDEFINED (not zero; there is no sample at all).
  The $18k spend appears on channel_spend.csv but nobody from paid_social ever
  entered contacts_first_touch.csv. Either tracking is broken, or those impressions
  never drove form fills. Cost per SQM and cost per SQO cannot be computed.

- organic_search, referral: valid volume but $0 spend tracked. No paid denominator
  means pipeline-per-dollar is undefined for these channels.


=== REALLOCATION RECOMMENDATION ===

Paid channels ranked by pipeline-per-dollar:

  paid_search       $20/pip/$   ████████████████
  webinars           $7/pip/$   █
  linkedin_ads       $4/pip/$   ██

Action 1 — Shift budget from linkedin_ads to paid_search:
  Every $1,000 moved generates ~$76K incremental pipeline.
  Example: move $4,000/month from linkedin_ads to paid_search:
    linkedin would produce: $4k x $4/$ = $16K pipeline
    paid_search would produce: $4k x $20/$ = $80K pipeline
    Net gain: ~$64K/month pipeline per $4k shift

Action 2 — Fix or suspend paid_social tracking immediately:
  $18,000 across 6 months ($3,000/month) is invisible in the CRM. Without attribution,
  you cannot evaluate it. Pause until UTM/contact-source tracking is fixed, then
  reallocate to paid_search where marginal ROI is proven.

Action 3 — Maintain webinar spend:
  At $7/pip/$ and 41.7% conv rate, webinars outperform linkedin_ads. Sample is small
  (n=5 SQOs) but directionally sound.


=== CONFIDENCE ASSESSMENT ===

  paid_search     : HIGH (n=18 SQOs) — robust sample
  organic_search  : MEDIUM (n=10 SQOs)
  linkedin_ads    : MEDIUM (n=8 SQOs)
  referral        : MEDIUM (n=6 SQOs)
  webinars        : MEDIUM (n=5 SQOs)
  paid_social     : UNDEFINED (zero SQOs, zero data)

The linkedin_ads recommendations carry moderate confidence — two reversed-date rows
inflate the SQM count without adding SQOs, suggesting first-touch noise. paid_search
results are highly reliable given 18 conversions. Paid social results are truly
undefined, not just low-confidence, because there is literally no contact-level data.
competitor-battlecard-update0.80

? 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 $0.0008 · 52s · in 6,741 / out 3,381 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
Updated battlecard written to: `/Users/amaniphipps/Rivally_battlecard_updated.md`

**Summary of what changed from the old card:**

| Old Claim | Verdict | New |
|---|---|---|
| "Rivally lacks a Slack integration." | **False.** S04 confirms Slack integration worked out of the box (2026-02-02). | Removed. |
| "Rivally was acquired by WorkHuman in 2025." | **Unverified.** No source supports this. S01 says Series C, not acquisition. | Removed. |
| "Pricing starts at $5/user/month" | **Outdated.** Price raised to $7 on 2026-08-12 [S17]. | Updated to $7, with version conflict documented ($5 vs $7 both pricing_page; newer wins). |
| No recent changes section | N/A | Added — 8 changes tracked chronologically through Sep 2026. |

**Win rate:** 12 wins / 8 losses = 60% across 20 deals over the past 12 months. April and May were rough months (0% win rate); June through August recovered to 100%.

**Excluded rep opinions:** Bryce Harmon's UI comment [S09] and Elena Sinclair's discounting claim [S21] — flagged as rep opinion per your rule.
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 $0.0016 · 30s · in 32,442 / out 1,890 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
Here is the analysis from the provided data.

**Key finding: audiences.csv has only 40 unique rows (a sample), with zero multi-sequence contacts. However, the sequences show hundreds to thousands of sends, confirming the real contact lists are larger than this file represents — actual overlap cannot be verified from this data.**

---

### 1. New Logo Nurture
- Sent: 1,386 | Open: 35.4% | Reply: 6.5% | Meet: 1.9%
- Step rates: S1=8.4% reply / S2=6.6% / S3=4.2%. Steep decay at each step.
- Weakest step: **Step 3** (4.2% reply, 1.4% meet). Drop-off accelerates — sent falls 72 from S1 to S3 (500→428) while reply rate halves.
- Failure mode is engagement fatigue, not cold outreach rejection.

### 2. Expansion Nurture
- Totals look okay: 64.6% open, 6.7% reply. But **tracking error: Step 2 shows 340 opened vs 300 sent** (113.3%). Impossible. Either the send count or open count for S2 is wrong. Fix before drawing conclusions.
- Excluding the error, weak signal: **meetings fall** from S1(1.7%) to S2(1.3%) to S3(1.1%). Existing customers aren't requesting meetings despite opening emails.

### 3. Cold Outbound - HR Leaders
- Sent: 1,785 | Open: 30.5% | Reply: **0.4%** | Meet: 0%
- All three steps under 1% reply. **Failure mode confirmed: message-market misfit.** Zero meetings across 1,785 sends. Opening rates decay (40% → 29% → 22%) showing diminishing subject-line effectiveness. This audience is either wrong, or the value prop doesn't land.
- Change: **rethink targeting or complete creative overhaul.** This is the priority fix.

### 4. Cold Outbound - People Ops
- Sent: 1,163 | Open: 29.2% | Reply: 2.5% | Meet: 0.5%
- Steps: S1=3.5% reply, S2=2.3%, S3=1.6%. Under 2% threshold at Step 3. Same fatigue pattern as New Logo but colder.
- Weakness: declining momentum across steps. Not enough early-stage proof/credibility to carry replies.

---

### Priorities
1. **Fix Expansion Nurture S2 tracking first** (340 opens / 300 sent). Until fixed, no valid decisions on this sequence.
2. **Cold Outbound - HR Leaders is the critical failure:** 0.4% reply, 0 meetings across 1,785 sends. Stop scaling. Audit list quality and message fit immediately.
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 $0.0005 · 32s · in 2,616 / out 2,071 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

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Here's the Q3-2026 marketing weekly update (66 days elapsed of 92 — 71.7% through quarter):

**Weekly Marketing Update — Q3-2026 (Day 66/92)**

| Metric | Actual | Target | Pace Required (×66/92) | Status |
|--------|--------|--------|-----------------------|--------|
| SQMs | 230 | 300 | 215 | **AHEAD** — on track to hit 321 if pace holds |
| SQOs | 84 | 120 | 86 | **BEHIND** — will only hit ~118 at current pace |
| DS2s | 40 | 75 | 54 | **BEHIND** — will only hit ~56 at current pace |
| CL MIA rate | 20.0% | ≤10.0% | — | **BEHIND** — 5 of 25 closed-losers were MIA, double target |
| Same-qtr closes | 10 | 20 | 14 | **BEHIND** — will only hit ~14 at pace |
| Active pipeline | $3.0M | $4.0M | $2.87M | **AHEAD** — above minimum but still short of full target (75%) |

Key conversion funnel: SQM→SQO = 36.5% (84/230), SQO→DS2 = 47.6% (40/84).

**What moved this week:** Cannot be assessed. Prior-week actuals are not in the provided data — only QTD totals exist. A W/W delta narrative requires separate weekly snapshots.

Written to `/Users/amaniphipps/marketing_weekly_update.txt` with full arithmetic shown above. If you can supply prior-week numbers or a weekly snapshot file, I'll add W/W deltas and the movement paragraph.
communication
ceo-slack-compressionPASS 1.00

? 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 $0.0002 · 11s · in 1,173 / out 475 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

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Forecast is $116K ($44,729 COMMIT + 35% of $203,565 BEST_CASE), though $13,770 in committed value closes post-quarter. Owner is blank on 85/86 deals and why-buys is missing on 71/86 (including all 7 COMMsits); 32 post-quarter close dates suggests date pushing not re-qualification. Rebuild with validated commitment evidence before sharing externally.
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 $0.0002 · 8s · in 941 / out 333 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

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Subject: Following up on the 150-seat pricing

Hi [Name],

I'm circling back on the email I sent on August 5 with the pricing recap from our July 28 demo — wanted to make sure it didn't get buried.

You mentioned at the time that recognition budget sits with you, and the team was particularly interested in automated milestone awards and the points catalog. Any movement on your end?

Happy to hop on a quick 15-minute call this week if that's easier. Otherwise, happy to send over any additional info you or the HR team need.

Thanks,
Alex
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 $0.0002 · 16s · in 770 / out 812 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

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Marketing delivered 46 SQMs against a target of 52 — falling short by 6 units. Webinar drove 18 of those, making up nearly 40% of the total. Cost per SQM on paid search held steady at $150. Target miss: 11.5%.

Sales converted 14 SQOs into 9 DS2 meetings, generating $310,000 in new pipeline. Same-quarter close count landed at 3. Conversion ratio on SQO-to-DS2 sits at 64% (9 out of 14). Pipeline throughput is tracking but meeting conversion from SQO remains an area to tighten.

CS renewed 2 accounts this week and Team NPS rose to 61. Three red-flag accounts are on deck heading into next week — watch list active. Renewal velocity and sentiment both moving in the right direction.

PLG recorded 412 new signups with activation at 31%. That means roughly 286 signups did not activate within the window. Thirty-eight companies hit the Aha moment of 10 recognition gives — representing about 9% of all signups. Activation-to-Aha efficiency needs sharpening; the majority of inbound traffic is landing but not reaching critical engagement thresholds.
partner-digest-section0.67

? 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 $0.0002 · 12s · in 991 / out 712 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

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# Partner Digest — 2026-08-24 to 2026-09-04

## Active Partners

**Apex Rewards Co**
Co-webinar locked for 09-15. Two sourced opps this period (both DS1), logged with UTM Source = Partner.
Pipeline: 2 deals · $275,000 (Deal-DDAAF2: $180,000; Deal-2CF33E: $95,000)

**HRCloud Partners**
Integration referral passed security review; opp moved to DS2. One sourced opp this period.
Pipeline: 1 deal · $140,000 (Deal-F1CDA5)

**CultureBridge**
Lunch-and-learn produced two sourced opps, both early stage.
Pipeline: 2 deals · $135,000 (Deal-096E1D: $60,000; Deal-067213: $75,000)

**WorkWell Group**
No sourced deals this period. Planning call booked for 09-09 to restart joint playbook in Q4.
Pipeline: 0 deals · $0

## Quiet Partners

Recogniq — no activity reported.
KudosWave — no activity reported.
PeopleFirst Advisors — no activity reported.
TotalPerk — no activity reported.
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 $0.0002 · 11s · in 962 / out 696 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
**Vendor Response — Security Questionnaire**

| Item | Answer | Excerpt |
|------|--------|---------|
| **Q1** | Yes. 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** | Bonusly maintains a SOC 2 Type II report, renewed annually. A copy is available to prospects under NDA. | [P2] |
| **Q4** | Customer data is retained for the duration of the contract plus 90 days, after which it is deleted from production systems. | [P3] |
| **Q5** | Subprocessor list is published 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 available on the Enterprise plan (along with SCIM user provisioning). | [P5] |
| **Q7** | Production databases are backed up daily. Backups are encrypted and retained for 35 days. | [P6] |
| **Q8** | Not addressed in the provided excerpts. This would require an ISO 27001 certification statement or audit report. | — |
| **Q9** | Not addressed in the provided excerpts. This would require a contractual SLA document. | — |
| **Q10** | Not addressed in the provided excerpts. This would require a HIPAA / BAA policy statement or DPA. | — |
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 $0.0034 · 76s · in 69,417 / out 9,469 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
FINDING 1: Overlapping ALWAYS-trigger phrases
Severity: WARNING
Action: REVIEW
Finding: `weekly-pipeline-report` and `pipeline-intelligence-report` share three identical trigger phrases: "pipeline update", "pipeline report", and "what's the pipeline look like". Additionally, `comms-drafter` and `email-drafter` share "help me reply", "draft a follow-up", "bump email", "contract nudge", and "what should I say". Identical triggers force the router to guess intent, causing misrouted analytical vs. drafting tasks.
Proposal: REVIEW — Rewire triggers to be mutually exclusive. Reserve "pipeline update", "pipeline report", and "pipeline look like" strictly for `weekly-pipeline-report`; assign `pipeline-intelligence-report` exclusively to "score", "tier", and "full pipeline". Narrow `email-drafter` to outbound/follow-up/prospecting emails; move `comms-drafter` entirely to support tickets, partner outreach, Intercom replies, and non-email formats. Drop all overlapping phrases from `comms-drafter`.

FINDING 2: Circular delegation chain
Severity: INFO
Action: REVIEW
Finding: `email-drafter` and `deal-strategy-coach` contain reciprocal handoff instructions without execution precedence. `email-drafter` says "point them to deal-strategy-coach"; `deal-strategy-coach` says "use the email-drafter skill" for manager email drafts. In a multi-agent loop this creates a routing ping-pong where neither agent commits to its primary domain before passing the buck.
Proposal: REVIEW — Establish hard precedence: `email-drafter` always produces the draft first, then calls `deal-strategy-coach` only if strategic framing or objection handling is requested. `deal-strategy-coach` forwards pure email generation to `email-drafter` only when explicitly toggled; otherwise it retains coaching scope.

FINDING 3: Dangling delegation targets
Severity: CRITICAL
Action: UPDATE_BODY
Finding: `analysis-validator` (§12.4) and multiple drafting skills reference 9 peer-skill delegation targets that do not exist in the provided manifest: `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`, and `prospect-research-multithreading`. Runtime `delegate_task` calls to these names will fail silently or throw unresolved-target errors.
Proposal: UPDATE_BODY — Immediately purge or replace all `delegate_task` references to these 9 skills in `analysis-validator`, `comms-drafter`, `deal-strategy-coach`, and `email-drafter` with `[LIVE QUERY]` tags or inline connector fallbacks. If the skills are still in development, add them to the manifest NOW rather than leaving broken delegation hooks in production bodies.

FINDING 4: Version conflict
Severity: WARNING
Action: UPDATE_BODY
Finding: `analysis-validator` changelog lists two distinct patches, v3.5 and v3.6, both dated May 9, 2026. The duplicate timestamp collides with the v3.6 master stamp used in validation trails, making it ambiguous which gate logic is authoritative if a rollback occurs.
Proposal: UPDATE_BODY — Merge the v3.5 (Engagement Coverage Check) and v3.6 (ID Resolution) changelog entries into a single line dated May 9, 2026. Mark v3.6 as the surviving version and retain all gated features from both increments under that unified entry.

FINDING 5: Descriptions exceeding 1,024 characters
Severity: INFO
Action: None
Finding: Zero (0) manifest descriptions exceed the 1,024-character threshold. The maximum observed length is 1,006 characters, shared by `pipeline-intelligence-report` and `signalforge-claim-compressor`. All other entries range from 656 to 996 characters.
Proposal: INFO — No trimming required. Current descriptors sit safely within the 1,024 char limit.

FINDING 6: Hardcoded page IDs, dates, and person names
Severity: WARNING
Action: UPDATE_BODY
Finding: Extensive static values baked into skill bodies across `analysis-validator`, `partner-digest`, `deal-strategy-coach`, `pipeline-intelligence-report`, `sales-forecast`, `weekly-pipeline-report`, and `signalforge-feedback`. Includes 20+ Confluence/Page IDs, 2 Google Sheet IDs, 1 Slack Channel ID, full AE/CSM rosters with HubSpot Owner IDs, historical 2026 pricing tables, and specific case-study company names. These values will rot as reps change, sheets are recreated, or Confluence spaces are migrated, causing silent data mismatches.
Proposal: UPDATE_BODY — Replace all static identifiers (Confluence IDs, Sheet IDs, Slack channel IDs, Owner IDs) with live connector calls or `[LIVE QUERY]` placeholders. Move historical pricing, past-period benchmarks, and archival case studies into dated reference blocks tagged `[ARCHIVED — DO NOT USE FOR LIVE FORECASTING]` and enforce `[LIVE QUERY]` replacement on any PR that modifies these sections.

FINDING 7: Manifest drift (both directions)
Severity: INFO
Action: None
Finding: Perfect alignment. The manifest declares 14 files; exactly 14 corresponding `.SKILL.md` files were provided. Every manifest row maps to a physical file, and every provided file has a manifest row. No orphaned rows or untracked files detected.
Proposal: INFO — No drift remediation needed. The manifest and filesystem are fully reconciled.
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 $0.0004 · 28s · in 1,205 / out 1,971 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

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# Runbook: Reward-Queue Backlog — 2026-08-29

## Pre-runbook
1. **Acknowledge PagerDuty alert** — *Command/Action:* Acknowledge PD alert for reward-worker queue depth > 10k.
   - *Who:* Bryce Harmon [M01]
   - *Verification:* Alert acknowledged manually.
   - *Rollback:* N/A (ack is not a state change).

---

## Step 1: Diagnose queue depth
- *Command:* `bundle exec rake sidekiq:queue_depth`
- *Who:* Farid Osman [M02]
- *Result:* reward queue at 48,213 pending jobs (normal < 500).
- *Verification:* Rake output confirmed depth.
- *Rollback:* N/A (read-only diagnostic).

## Step 2: Inspect dead set
- *Command:* Checked Sidekiq dead set (exact CLI command not stated in thread).
- *Who:* Farid Osman [M03]
- *Result:* Dead set has 112 jobs, all Redis::TimeoutError originating around 13:58.
- *Verification:* Dead set inspection output.
- *Rollback:* N/A (read-only diagnostic).
- *⚠ NEEDS CONFIRMATION:* Exact Sidekiq CLI used to inspect the dead set is not recorded in the thread.

## Step 3: Stop new jobs from queuing
- *Command:* `bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'`
- *Who:* Farid Osman [M04]
- *Purpose:* Pause enqueue to stop the bleed.
- *Verification:* Not explicitly verified in the thread after running; assumed successful by default flag-toggle semantics.
- *Rollback:* `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'` [M04]
- *⚠ NEEDS CONFIRMATION:* No post-disable verification was recorded at 14:08. The next mention of the flag status appears at M09 when it was re-enabled. Confirm whether the team expected immediate cessation of new enqueues or if there was a propagation delay.

## Step 4: Clear the dead set
- *Command:* Cleared the dead set via Rails console (exact method/command not stated).
- *Who:* Elena Sinclair [M05]
- *Verification:* Elena reported clearing the dead set; no follow-up check recorded.
- *Rollback:* Not applicable (dead set items are discarded once cleared).
- *⚠ NEEDS CONFIRMATION:* Specific console command/method used is not provided. Also: confirm that clearing the dead set was intentional for all 112 jobs and that none needed to be replayed.

## Step 5: Scale up workers
- *Command:* `kubectl scale deployment/reward-worker --replicas=6`
- *Who:* Bryce Harmon [M06]
- *Previous state:* 3 replicas.
- *Verification:* Not verified immediately; next visibility point is M07 at 14:33 (~11 min later).
- *Rollback:* `kubectl scale deployment/reward-worker --replicas=3` [M06]

## Step 6: Monitor queue drain rate
- *Command:* Implicit monitoring of `sidekiq:queue_depth` / Datadog (exact query not stated).
- *Who:* Farid Osman [M07]
- *Result:* Queue depth down to 9,400, falling ~1,200/min.
- *Verification:* Depth measurement at 14:33 confirmed declining trend.
- *Rollback:* N/A (monitoring only).
- *⚠ NEEDS CONFIRMATION:* Exact monitoring tool or command used to produce the "~1,200/min" rate figure is not recorded.

## Step 7: Verify full clearance
- *Command:* `bundle exec rake sidekiq:queue_depth`
- *Who:* Cole Ingram [M08]
- *Result:* Queue depth returned to 0. Error rate in Datadog back to baseline.
- *Verification:* Two signals: (a) rake output = 0, (b) Datadog error rate at baseline.
- *Rollback:* N/A (no state change).

## Step 8: Re-enable enqueue
- *Command:* `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'`
- *Who:* Bryce Harmon [M09]
- *Verification:* Confirmed 40 new jobs processed cleanly over the next 3 minutes [M09].
- *Rollback:* (See Step 3 rollback.) Could disable again if new jobs start failing.

## Step 9: Scale workers back to nominal
- *Command:* `kubectl scale deployment/reward-worker --replicas=3`
- *Who:* Bryce Harmon [M10]
- *Previous state:* 6 replicas.
- *Verification:* Queue stable at 0 [M10].
- *Rollback:* If queue starts building again at normal traffic, scale back up to 6 while investigating root cause.

---

## Summary Table

| # | Action | Who | Verified By | Rollback |
|---|--------|-----|-------------|----------|
| 1 | Ack PD alert | Bryce Harmon | Self [M01] | N/A |
| 2 | Check queue depth | Farid Osman | Rake output [M02] | N/A |
| 3 | Inspect dead set | Farid Osman | Dead set output [M03] | N/A |
| 4 | Disable enqueue FF | Farid Osman | Not explicitly verified [M04] | `FeatureFlag.enable(...)` [M04] |
| 5 | Clear dead set | Elena Sinclair | Elena's own report [M05] | N/A |
| 6 | Scale workers → 6 | Bryce Harmon | Drain rate at 9,400 [M07] | Scale → 3 [M06] |
| 7 | Monitor drain | Farid Osman | Depth = 9,400 @ ~1,200/min [M07] | N/A |
| 8 | Verify queue = 0 | Cole Ingram | Rake output + Datadog [M08] | N/A |
| 9 | Re-enable enqueue FF | Bryce Harmon | 40 jobs processed cleanly [M09] | Disable [see step 4] |
| 10 | Scale workers → 3 | Bryce Harmon | Queue stable at 0 [M10] | Scale → 6 if needed |

---

## Root Cause (from thread data)
At approximately 13:58 on 2026-08-29, Redis began timing out, causing 112 Sidekiq jobs to fail with `Redis::TimeoutError` and land in the dead set. New jobs continued enqueuing normally, resulting in a queue depth of 48,213 by 14:04. The timeouts themselves are not investigated further in this thread.

## Unresolved / Items for Follow-Up
- **⚠ Why did Redis timeout?** The root trigger for `Redis::TimeoutError` at 13:58 is not addressed in the thread.
- **⚠ Was any dead-set job replayable?** All 112 were discarded during Step 4 with no record of retry analysis.
- **⚠ Dead-set inspection command** is not specified — may hinder repeatability of Step 2.
- **⚠ Monitoring command** for the drain-rate figure (~1,200/min) is not specified — may hinder repeatability of Step 6.
datadog-incident-timeline0.80

? 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 $0.0005 · 41s · in 2,590 / out 2,461 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
Here is the sequenced failure cascade, built strictly from the two provided files.

## Timeline (chronological order)

**Phase 1 — Root cause (Redis goes down)**

| Timestamp | Service | Message | Meaning |
|---|---|---|---|
| 14:01:12Z | reward-service | `Redis::TimeoutError: Connection to Redis at redis-primary:6379 timed out after 5s` | **First error.** reward-service could not reach Redis. |
| 14:01:46Z – 14:01:57Z | (none logged, inferred from jobs table) | six RewardGiveJob failures (J-00001 through J-00006) between 14:01:46 and 14:01:57 | These six jobs attempted to execute immediately after the initial timeout and each hit Redis::TimeoutError. They confirm that reward work was actively flowing when the outage started. |

**Phase 2 — reward-service can no longer enqueue or run jobs**

| Timestamp | Service | Message |
|---|---|---|
| 14:01:20Z – 14:01:40Z | reward-service | `Redis::TimeoutError: retry exhausted for RewardGiveJob` (3 messages at 14:01:20, 14:01:30, 14:01:40) |
| 14:01:40Z | sidekiq | `RewardGiveJob failed: Redis::TimeoutError; retrying in 60s` |
| 14:02:28Z | sidekiq | `RewardGiveJob failed: Redis::TimeoutError; retrying` |

reward-service is trying and failing to write to Redis. Sidekiq then picks up queued jobs and fails against the same unreachable Redis. The 5-second client timeout plus retry exhaustion means these jobs cannot be processed at all.

**Phase 3 — Queue backs up**

| Timestamp | Service | Message |
|---|---|---|
| 14:02:30Z | sidekiq | `Queue reward depth above 10,000` | Jobs are accumulating because they cannot be dequeued and executed. |

**Phase 4 — Downstream: 502 cascade (api-gateway → web-app)**

| Timestamp | Service | Message |
|---|---|---|
| 14:03:05Z | api-gateway | `502 upstream timeout calling reward-service /gives` |
| 14:03:30Z | web-app | `Give form submission failed: upstream 502 from api-gateway` |
| 14:03:48Z | api-gateway | `502 upstream timeout calling reward-service` |
| 14:04:13Z | api-gateway | `502 upstream timeout calling reward-service` |
| 14:04:45Z | web-app | `Give form submission failed: upstream 502` |
| 14:05:16Z | api-gateway | `502 upstream timeout calling reward-service` |
| 14:05:26Z | sidekiq | `RewardGiveJob failed: Redis::TimeoutError; retrying` |
| 14:05:42Z | web-app | `Give form submission failed: upstream 502` |
| 14:06:47Z | sidekiq | `RewardGiveJob failed: Redis::TimeoutError; retrying` |
| 14:06:49Z | web-app | `Give form submission failed: upstream 502` |
| 14:06:52Z | api-gateway | `502 upstream timeout calling reward-service` |

Because reward-service cannot talk to Redis, it is either crashing its worker threads or refusing new HTTP requests. api-gateway sees unresponsive backends and returns 502s. web-app users see "Give form submission failed." RecognitionDigestJob also begins failing (J-00013–J-00016 at 14:02:36 through 14:05:50), showing Redis downtime affects all background workers, not just reward service.

**Phase 5 — Recovery**

| Timestamp | Service | Message |
|---|---|---|
| 14:22:10Z | reward-service | `Redis connection restored; resuming job processing` |
| 14:24:45Z | sidekiq | `Queue reward depth below 500` | Queue draining after ~20 min of catch-up processing. Note there is **no explicit restart message from reward-service** between the failure peak at 14:06 and recovery at 14:22 — the service may have been restarted off-screen, or simply reconnected to Redis without needing a full redeploy.

## Service + Job involved

- **Service:** `reward-service` (primary) communicating with `redis-primary:6379`. Secondary impact on `sidekiq`, `api-gateway`, `web-app`, and `postgres` (unchanged).
- **Job class:** `RewardGiveJob` (failed J-00001–J-00012 = 12 failures logged in sidekiq_jobs.csv; plus `RecognitionDigestJob` J-00013–J-00016 = 4 failures). All errors are identical: `Redis::TimeoutError`.

## Datadog query to confirm the first error

To confirm that the very first event at 14:01:12Z was truly a Redis connectivity failure on `redis-primary:6379`:

```
@service:reward-service Redis::TimeoutError OR (redis.command:(connect|ping)) AND host:redis-primary
   .by(source)
.time_range(1693744872000000..1693747200000000)   // 14:00 – 14:30 UTC, Sep 3
```

Translated: look in reward-service tags for `Redis::TimeoutError` traces, cross-referenced with Redis APM for `connect`/`ping` calls on host `redis-primary` in the same window. If you see `DEADLINE_EXCEEDED` or a spike in Redis latency/packet loss alongside zero successful connections starting at ~14:01, the root cause is confirmed as Redis being unreachable.

Alternatively, if querying directly in the Redis monitor namespace:

```
redis.command:(connect OR ping)
   AND @hostname:"redis-primary"
   AND (@latency_ms > 5000 OR status:fail)
.time_range(1693744872000000..1693747200000000)
```

This isolates Redis-side evidence of the outage window regardless of what the clients reported.

## What the logs do NOT show

1. **Why Redis went down.** No OOM killer event, no disk-full, no network partition, no replica-failover log, no config change. The only datum is "connection to redis-primary:6379 timed out." It could be Redis down, a networking glitch, a firewall rule, DNS resolution failure, or resource exhaustion. Unknown.

2. **Whether any other services besides reward-service and sidekiq were affected.** Only five services appear in datadog_logs.csv. There is no data about nginx, payment-service, notification-service, auth-service, etc.

3. **How long the outage really lasted before 14:01:12Z.** The first logged line is a health check OK at 14:01:12. Something could have gone wrong earlier and simply wasn't captured in this log slice.

4. **Impact on end-users beyond "form submission failed."** No error-rate dashboards, no HTTP 5xx count over time, no customer-impacted session counts, no SLO breach calculations.

5. **Whether data was lost.** Did any reward transactions silently drop, partially complete, or get retried idempotently? There is no transaction-log or audit trail to answer that.

6. **Who restarted or recovered the service.** The 14:22:10Z "resuming job processing" message does not state whether someone manually restarted reward-service, whether it self-healed after Redis came back up, or whether Kubernetes rolled the pod. No change-management ticket or deployment record is present.

7. **Root cause of the RecognitionDigestJob failures** — while logically linked to the same Redis outage, datadog_logs.csv contains no RecognitionDigestJob lines; that information comes only from sidekiq_jobs.csv.

8. **postgres behavior beyond checkpoint messages.** Three postgres entries (14:10–14:18) are normal checkpoints with no errors, confirming postgres was not involved. But we have no CPU/memory/wait-event data from postgres that might help rule out shared-kernel issues (e.g., if Redis and postgres ran on the same host).
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 $0.0003 · 18s · in 979 / out 1,393 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
Here is the feature-flag summary based strictly on the two provided files.

---

## Feature Flag State Summary

### 1. recognition_streaks_v2
- **Code:** `app/models/recognition.rb` — gates `StreakTracker.record(give)`
- **State:** on
- **Targeting:** segment:beta_companies
- **Company count:** 42

### 2. points_budget_guardrails
- **Code:** `app/services/budget_service.rb` — gates `BudgetService.new(company).enforce!(giver, points)`
- **State:** on
- **Targeting:** all_companies
- **Company count:** 220

### 3. slack_dm_nudges
- **Code:** `app/jobs/nudge_job.rb` — gates `SlackDm.send_nudge(user)` via early return
- **State:** on
- **Targeting:** segment:region_na
- **Company count:** 87

### 4. redeem_flow_redesign
- **Code:** `app/controllers/redeem_controller.rb` — toggles between `RedeemV2Component` and `RedeemV1Component`
- **State:** off
- **Targeting:** targeted_list
- **Company count:** 12

### 5. analytics_dashboard_v3
- **Code:** `app/controllers/analytics_controller.rb` — instantiates `AnalyticsV3.new(company)`
- **State:** on
- **Targeting:** segment:tier_three
- **Company count:** 65

### 6. ms_teams_app_v2
- **Code:** `app/services/teams_installer.rb` — calls `TeamsAppV2.install(company)`
- **State:** off
- **Targeting:** targeted_list
- **Company count:** 9

### 7. legacy_give_modal
- **Code:** *no code reference found*
- **State:** off
- **Targeting:** segment:legacy_plan
- **Company count:** 14

### 8. survey_boosters_q3
- **Code:** *no code reference found*
- **State:** on
- **Targeting:** segment:legacy_plan
- **Company count:** 7

### 9. paused_offboard_cleanup
- **Code:** *no code reference found*
- **State:** off
- **Targeting:** *none specified* (empty string in export)
- **Company count:** 0

---

## Flags with gaps (no code reference)

| flag_key | state | targeting_rules | company_count |
|---|---|---|---|
| legacy_give_modal | off | segment:legacy_plan | 14 |
| survey_boosters_q3 | on | segment:legacy_plan | 7 |
| paused_offboard_cleanup | off | *(empty)* | 0 |

Three of the nine flags have no corresponding code usage in the provided excerpt (`flag_code.md`). This means they may be dead/config-only flags, still being wired up, or their implementation lives outside `app/` and `lib/`.

## Quick-state tally

| State | Count | Flags |
|---|---|---|
| on | 5 | recognition_streaks_v2, points_budget_guardrails, slack_dm_nudges, analytics_dashboard_v3, survey_boosters_q3 |
| off | 4 | redeem_flow_redesign, ms_teams_app_v2, legacy_give_modal, paused_offboard_cleanup |
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 $0.0002 · 16s · in 1,027 / out 813 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 — GREEN** — Standard mutual NDA with Delaware law, reasonable 2-year term, standard carve-outs, no restrictive covenants. Clause driving classification: *"No license, no obligation to proceed, no exclusivity."* The 3-year survival period is slightly above the 1–2 year norm but not actionable.

**NDA-2 — YELLOW** — Counsel review required for the non-solicit. Clause: *"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."* The contact-nexus limitation ("with whom it came into contact") narrows it appropriately, but 18 months post-term is worth counsel confirming this won't restrict hiring from a prospect/partner relationship where Bonusly people may have met the counterparty's team. Nothing else objectionable.

**NDA-3 — RED** — Full legal review required. Three independent dealbreakers:

1. **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.'"* One-way, indirect prohibition on providing services to competitors is dangerously broad — it could block Bonusly from advising clients in the same space as Party C.

2. **Zero carve-outs** — *"No confidentiality carve-outs are stated in this Agreement."* Without the standard public/already-known/independent-development carve-outs, any idea you independently develop or already knew is technically trapped as their confidential information forever (5-year term × potentially extended interpretation).

3. **Irish governing law** — *"'This Agreement shall be governed by and construed in accordance with the laws of the Republic of Ireland, and the parties submit to the exclusive jurisdiction of its courts.'"* Foreign jurisdiction for a U.S. company introduces unquantified litigation cost and unfamiliar IP/confidentiality doctrine.