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anthropic

anthropic/claude-opus-4.5

Mean 0.957 · 32/40 perfect tests · $9.83 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 $0.6543 · 218s · in 10 / out 18,329 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
## Stage Hygiene Audit

**Definitions**
- **Non-compliant why_buys (DS2+)**: why_buys is empty, a placeholder (tbd/n/a/see notes), or <15 chars. LONG(n) = substantive, treated as compliant.
- **Missing DS1 timestamp**: t_ds1 field is empty.
- **Regressed**: t_ds(n) > t_ds(n+1) for any consecutive stage pair.

---

**Why_Buys Non-Compliance (DS2+ Deals)**

| Metric | Value |
|--------|-------|
| Total DS2+ deals | 124 |
| Non-compliant | 101 |
| **Overall rate** | **81.5%** (101/124) |

**By Owner:**
| Owner | Non-compliant | Total DS2+ | Rate |
|-------|---------------|------------|------|
| Cole Ingram | 19 | 22 | 86.4% |
| Bryce Harmon | 15 | 18 | 83.3% |
| Dana Mercer | 18 | 22 | 81.8% |
| Alex Franklin | 48 | 61 | 78.7% |
| Farid Osman | 1 | 1 | 100% |

---

**Other Hygiene Issues**
- Deals with no DS1 timestamp: **0**
- Deals that regressed (timestamp inversion): **0**

---

**Three Process Fixes**

1. **CRM validation gate**: Block stage advancement to DS2+ unless why_buys ≥ 15 characters (enforce in HubSpot workflow).

2. **Weekly hygiene digest**: Automated email to each owner listing their DS2+ deals with missing/placeholder why_buys — include deal link and days-in-stage.

3. **Manager visibility**: Add why_buys completion rate as a column in pipeline review dashboards and track in 1:1 scorecards; tie to forecast credibility.
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 $0.4021 · 146s · in 10 / out 12,413 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
## Owner Resolution Report

**Deals Resolved:** 156 of 156 (100%)

Every owner_id in the deals extract matches an owner_id in the owners table.

---

**Deals with Unmatched owner_id:** 0

No deals reference an owner_id absent from the owners table.

---

**Deals Mapped to Archived/Deactivated Owners:** 0

The owners table lists two archived owners:
- Gavin Porter (owner_id 1520255671, archived=true)
- Hugo Lindqvist (owner_id 77260721, archived=true)

Neither owner_id appears in the deals extract, so no open pipeline is assigned to a deactivated owner.

---

**Total Pipeline by Resolved Owner:**

| Owner Name | Owner ID | Deals | Pipeline Amount |
|------------|----------|-------|-----------------|
| Bryce Harmon | 119337721 | 35 | $1,054,144.00 |
| Alex Franklin | 84342457 | 67 | $624,310.00 |
| Dana Mercer | 83155923 | 24 | $341,195.00 |
| Cole Ingram | 83155924 | 22 | $288,161.43 |
| Farid Osman | 716654662 | 7 | $4,134.00 |
| Elena Sinclair | 701163055 | 1 | $2,100.00 |
| **TOTAL** | | **156** | **$2,314,044.43** |

---

**Arithmetic (spot-checks):**

Bryce Harmon (35 deals): 24000 + 19656 + 13500 + 7000 + 2520 + 240000 + 99000 + 72000 + 70000 + 63600 + 45000 + 1 + 21000 + 23400 + 13680 + 5502 + 8160 + 1 + 11400 + 1 + 36000 + 31500 + 6000 + 10800 + 30275 + 17400 + 12600 + 18000 + 37440 + 18828 + 2880 + 36000 + 20880 + 10920 + 25200 = $1,054,144

Cole Ingram (22 deals, includes decimals): 2760 + 1330 + 7225.40 + 3360 + 5616 + 2700 + 7781.20 + 6947.50 + 40000 + 12168 + 2480.40 + 11116 + 11193 + 1875 + 31750 + 32175 + 4779.88 + 3334.80 + 58529.25 + 4140 + 18000 + 18900 = $288,161.43
gong-hubspot-join-integrityPASS 1.00

? 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 $0.5101 · 185s · in 10 / out 15,944 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
## Call-to-Deal Mapping Integrity Report

### (a) Orphan Gong Rows (deal_id not in deals_open)

**Count: 30 orphan rows** out of 67 total gong rows

Sample aliases from orphans:
- Deal-8FA85D (46 calls, last_call 2026-11-05)
- Deal-8FC3F9 (24 calls)
- Deal-3B7945 (21 calls)
- Deal-42B265 (21 calls)
- Deal-9CCC42 (17 calls)
- Deal-3F86A0 (6 calls)
- Deal-B038F0 (5 calls)

These are likely closed/won/lost deals or deals in a different pipeline.

---

### (b) Duplicate Conversation Keys

**Count: 0 rows**

Every gong row has `calls_90d == distinct_conversation_keys`. No duplicates detected.

---

### (c) DS3+ Deals with Logged Calls

Total open deals at DS3 or later:
- DS3: 61 deals
- DS4: 13 deals
- DS5: 10 deals
- **Total: 84 deals**

DS3+ deals with at least one call in gong: **25 deals**

**Share: 25 / 84 = 29.8%**

Notable DS3+ deals WITH calls: Deal-C6FE92 (11 calls), Deal-F9A3C1 (9 calls), Deal-5408B0 (8 calls), Deal-D348E1 (8 calls), Deal-B7EBD1 (8 calls)

Notable DS3+ deals WITHOUT calls: Deal-9AAE5F (DS4, $11,250), Deal-6787C2 (DS4, $7,000), Deal-62D607 (DS4, $4,800), Deal-7BBDFA (DS3, $37,440), Deal-FC22A3 (DS3, $10,800)

---

**Summary**
| Check | Result |
|-------|--------|
| Orphan rows | 30 / 67 (45%) |
| Duplicate conv keys | 0 |
| DS3+ with calls | 25 / 84 (30%) |
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 $0.1674 · 93s · in 10 / out 5,940 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

show the model's full answer
```sql
SELECT
    company_id,
    M1_USERS         AS unique_givers,
    M1_GIVES         AS recognition_count,
    M1_REDEMPTIONS   AS successful_redemption_count

-- PRODUCTION.PLG.COMPANY_COHORT_SUMMARY: Canonical pre-aggregated cohort table.
-- One row per self-serve company; M1_* columns = first calendar month metrics.
-- Historical giving counts preserve deleted givers (not excluded per business rule).
-- M1_REDEMPTIONS reflects STATE='succeeded' redemptions per business rule.
-- Avoided: PRODUCTION.HUBSPOT.DEALS (stale since 2023-03), V2_LIVE (unpopulated).
FROM PRODUCTION.PLG.COMPANY_COHORT_SUMMARY
```
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 $0.1815 · 71s · in 10 / out 6,069 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
## CRM Completeness Audit

### Field Completeness by Object

**COMPANIES (34 rows)**
| Field | Populated | Total | Completeness |
|-------|-----------|-------|--------------|
| company_alias | 34 | 34 | 100.0% |
| domain | 34 | 34 | 100.0% |
| industry | 34 | 34 | 100.0% |
| employee_count | 26 | 34 | 76.5% |
| hq_country | 29 | 34 | 85.3% |

Missing employee_count (8): C-EC3025, C-96039F, C-44EA29, C-D04904, C-7BBDFA, C-50D386, C-93C8BF, C-EE9FFB
Missing hq_country (5): C-2D1F1B, C-D73B89, C-44EA29, C-2C60E5, C-EE9FFB

**CONTACTS (52 rows)**
| Field | Populated | Total | Completeness |
|-------|-----------|-------|--------------|
| contact_key | 52 | 52 | 100.0% |
| company_alias | 52 | 52 | 100.0% |
| email (valid) | 47 | 52 | 90.4% |
| title | 42 | 52 | 80.8% |
| persona | 37 | 52 | 71.2% |

**DEALS: Not provided.** Cannot assess owner, stage, amount, close_date, why_buys fields.

---

### Duplicate Company Clusters (shared domain)

| Cluster | Domain | Members | Employee Discrepancy | Survivor | Rationale |
|---------|--------|---------|---------------------|----------|-----------|
| 1 | acme-corp.com | C-0A092931 (500), C-0A092932 (510) | 10 | **C-0A092931** | Cleaner industry="Technology", round headcount |
| 2 | globex.io | C-0A092933, C-0A092934 | 0 | **C-0A092934** | Industry="Technology" aligns to standard taxonomy; C-0A092933 uses non-standard "SaaS" |

Action: Merge contacts/deals from duplicates into survivor, then archive duplicates.

---

### Invalid Emails (4)

| Contact | Raw Email | Issue |
|---------|-----------|-------|
| CT-0010 | user0@ | Missing domain portion |
| CT-0080 | user0@ | Missing domain portion |
| CT-0081 | user1@ | Missing domain portion |
| CT-0192 | user2@ | Missing domain portion |

---

### Domain Mismatches (1)

| Contact | Email | Company Domain | Issue |
|---------|-------|----------------|-------|
| CT-0011 | user1@other-domain.com | 66d1fc.com | Email domain ≠ company domain |

Could be personal email, consultant, or data-entry error. Flag for sales rep review.

---

### Enrichment Fills (ZI has data where CRM is blank)

| Company | Field | CRM Value | ZI Value | Action |
|---------|-------|-----------|----------|--------|
| C-EC3025 | employee_count | (blank) | 400 | Fill 400 |
| C-96039F | employee_count | (blank) | 400 | Fill 400 |
| C-44EA29 | employee_count | (blank) | 400 | Fill 400 |
| C-D04904 | employee_count | (blank) | 400 | Fill 400 |
| C-7BBDFA | employee_count | (blank) | 400 | Fill 400 |
| C-50D386 | employee_count | (blank) | 400 | Fill 400 |
| C-B23205 | employee_count | (blank) | 400 | Fill 400 |

**Cannot fill (no ZI match):**
- C-93C8BF (93c8bf.com) — employee_count blank, no ZI row
- C-EE9FFB (ee9ffb.com) — employee_count + hq_country blank, no ZI row
- C-332637, C-C9BB20, C-BA969B — no ZI rows exist

**Cannot fill (ZI also blank):**
- C-2D1F1B: hq_country blank in both
- C-D73B89: hq_country blank in both
- C-44EA29: hq_country blank in both
- C-2C60E5: hq_country blank in both

---

### CRM vs ZI Disagreements

**Industry terminology (semantic match, format differs):**
| Company | CRM | ZI | Recommendation |
|---------|-----|-----|----------------|
| C-66D1FC | tech | Computer Software | Use ZI (standard taxonomy) |
| C-44EA29 | tech | Computer Software | Use ZI |
| C-60C75F | tech | Computer Software | Use ZI |
| C-425E2A | Tech  (trailing space) | Computer Software | Use ZI |
| C-BA969B | Tech  (trailing space) | (no ZI row) | Normalize to "Technology" |
| C-93C8BF | Tech  (trailing space) | (no ZI row) | Normalize to "Technology" |
| C-C9BB20 | Tech  (trailing space) | (no ZI row) | Normalize to "Technology" |

All "Technology" vs "Computer Software" differences are terminology, not factual disagreements. Recommend adopting ZI's "Computer Software" or standardizing CRM to "Technology" (pick one).

**Country format (semantic match):**
US / USA / United States all appear. Recommend standardizing to "United States" per ZI convention.

**No factual disagreements** on employee_count or hq_country where both sources have values.

---

### Top 10 Fixes by Pipeline Amount at Stake

**CANNOT RANK — deals.csv not provided.**

Without deal records (amount field), I cannot calculate pipeline at risk per company. To complete this section, provide deals.csv with columns: deal_id, company_alias, owner, stage, amount, close_date, why_buys.

---

### Summary Fix Counts

| Category | Count |
|----------|-------|
| Duplicate company clusters to merge | 2 (4 records → 2) |
| Invalid emails to repair | 4 |
| Domain-mismatch contacts to review | 1 |
| Company employee_count fillable from ZI | 7 |
| Company fields unfillable (no ZI data) | 5 records across 2 fields |
| Industry values to standardize | 7 |
| Country formats to standardize | ~12 |
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 $0.3752 · 167s · in 10 / out 13,336 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
## Classification of 90 Closed-Lost Deals (Last 6 Months)

### Deal-by-Deal Classification

| Deal | Category | Side | Notes |
|------|----------|------|-------|
| Deal-DB0AAC | timing | buyer | "putting this on pause" |
| Deal-F7F635 | competitor | buyer | "go in another direction" |
| Deal-AC944F | no decision | buyer | MIA |
| Deal-214060 | no decision | buyer | MIA |
| Deal-91A056 | timing | buyer | "reconnect early in 2027" |
| Deal-29326C | timing | buyer | |
| Deal-5DB9B0 | other | unknown | Spam/not ICP |
| Deal-831B7B | timing | buyer | "new year" |
| Deal-F97C37 | competitor | Bonusly | "more diversified offerings" |
| Deal-13E9CF | no decision | buyer | **TAG DISAGREES** - tag=Cost, text="Not a budget issue" |
| Deal-39E25C | timing | buyer | |
| Deal-7ED004 | pricing | buyer | "Did not get budget approval" |
| Deal-21B045 | no decision | buyer | MIA |
| Deal-B3ABED | timing | buyer | "Q2 next year" |
| Deal-422BA6 | competitor | buyer | ADP partnership preference |
| Deal-ED9AE7 | timing | buyer | text lists timing/budget/authority |
| Deal-988493 | no decision | buyer | MIA |
| Deal-381C8C | competitor | unknown | no detail |
| Deal-F308CA | no decision | buyer | MIA since April |
| Deal-F1E8A6 | competitor | unknown | no detail |
| Deal-B6AC09 | timing | buyer | "revisiting in 2027" |
| Deal-70F704 | no decision | buyer | MIA, narrow scope |
| Deal-E6E80A | timing | buyer | "pushed into early 2027" |
| Deal-B038F0 | timing | buyer | "pushed back into early 2027" |
| Deal-4664E1 | no decision | buyer | MIA |
| Deal-175756 | timing | buyer | "other priorities" → 2027 |
| Deal-E74A73 | no decision | buyer | testing manually first |
| Deal-DDAB52 | competitor | Bonusly | Rippl "offers a lot more" |
| Deal-ACE061 | competitor | buyer | likely HeyTaco |
| Deal-BB78F3 | timing | buyer | other action items first |
| Deal-D48E0B | no decision | buyer | MIA |
| Deal-15DA99 | timing | buyer | "early 2027" |
| Deal-F4AF5D | timing | buyer | "early next year" |
| Deal-79B7A1 | timing | buyer | |
| Deal-583ADB | no decision | buyer | MIA |
| Deal-8E27DA | product gap | buyer | wanted swag only, not R&R |
| Deal-2D2F8D | competitor | unknown | no detail |
| Deal-E0441F | no decision | unknown | inherited stale deal |
| Deal-7CB44D | no decision | buyer | MIA since demo |
| Deal-0F96AA | competitor | buyer | RFP loss |
| Deal-1BCA50 | competitor | buyer | stakeholder down path w/ another vendor |
| Deal-7CC678 | competitor | unknown | "Nothing specific provided" |
| Deal-FAC17C | other | buyer | IT Director approval block |
| Deal-242273 | competitor | Bonusly | needed on-site point redemption feature |
| Deal-50E5D8 | no decision | buyer | deprioritized |
| Deal-A2C349 | competitor | buyer | Awardco + surveys |
| Deal-9F176A | timing | buyer | "end of the year" |
| Deal-7B2236 | pricing | buyer | "budget" + "cheaper" |
| Deal-AFA56C | no decision | buyer | MIA |
| Deal-C7156E | competitor | buyer | |
| Deal-C33D91 | pricing | buyer | budget cuts |
| Deal-9048EB | product gap | Bonusly | **TAG DISAGREES** - tag=MIA, text="multiple feature gaps" |
| Deal-5E64CE | timing | buyer | **TAG DISAGREES** - tag=Cost, text=Nectar contract lock-in to Oct 2027 |
| Deal-8A0992 | competitor | buyer | Canadian provider preference |
| Deal-D0C698 | competitor | buyer | past Kudos user returning |
| Deal-69CF3D | timing | buyer | on hold |
| Deal-ECBF89 | timing | buyer | on hold |
| Deal-3618CC | product gap | Bonusly | **TAG DISAGREES** - tag="Lost DM", text="Wanted Surveys" |
| Deal-EECC02 | competitor | unknown | no detail |
| Deal-5AD03E | competitor | unknown | vague "budget access" |
| Deal-D1A623 | timing | buyer | |
| Deal-413C56 | no decision | buyer | CEO not ready |
| Deal-47F1A1 | competitor | buyer | staying with WorkTango |
| Deal-BF2A98 | competitor | buyer | deployed HiThrive |
| Deal-2A292B | no decision | buyer | building internally |
| Deal-D1AABF | no decision | buyer | MIA |
| Deal-FEDBCB | no decision | buyer | not engaged |
| Deal-1E7DA9 | competitor | buyer | |
| Deal-2BBA21 | no decision | buyer | MIA |
| Deal-286F9C | competitor | buyer | "not a good fit" |
| Deal-7FBAC6 | no decision | buyer | leadership paused |
| Deal-369281 | competitor | buyer | Paylocity incumbent |
| Deal-386F6E | no decision | buyer | MIA |
| Deal-9FCD0D | competitor | buyer | Canadian company preference |
| Deal-55867E | timing | buyer | |
| Deal-DAFB82 | pricing | buyer | budget needed elsewhere until 2028 |
| Deal-2FEDDB | no decision | buyer | unsure on timing |
| Deal-64B19A | competitor | buyer | Motivosity |
| Deal-3F86A0 | no decision | buyer | MIA |
| Deal-096750 | no decision | buyer | MIA |
| Deal-F325A5 | champion left | buyer | "Layoffs and Change in Leadership" |
| Deal-ABD14C | no decision | buyer | not interested |
| Deal-79E61A | no decision | buyer | MIA |
| Deal-8A119B | pricing | buyer | no approval |
| Deal-AE7C4E | no decision | buyer | MIA |
| Deal-DAB4F1 | no decision | buyer | MIA |
| Deal-B4B50F | no decision | buyer | MIA |
| Deal-981AD4 | product gap | Bonusly | "not UK focused" |
| Deal-DC77FE | competitor | Bonusly | wanted point-labeling customization |
| Deal-5885B9 | no decision | buyer | MIA |

---

## Summary

### Category Counts (90 deals)
```
no decision     31  (34%)
competitor      26  (29%)
timing          21  (23%)
pricing          5   (6%)
product gap      4   (4%)
other            2   (2%)
champion left    1   (1%)
```

### Side Split
```
buyer       75  (83%)
unknown      8   (9%)
Bonusly      7   (8%)
```

### Tag/Text Disagreements: 4 deals

| Deal | Tag | Text Contradiction |
|------|-----|-------------------|
| Deal-13E9CF | "Doing nothing/Not a priority/Cost" | Text says "Not a budget issue" |
| Deal-9048EB | "MIA" | Text says "multiple feature gaps" |
| Deal-3618CC | "Lost DM" | Text says "Wanted Surveys" |
| Deal-5E64CE | "Doing nothing/Not a priority/Cost" | Text describes Nectar contract lock-in (timing) |

---

## Two Patterns Most Worth Acting On

**1. MIA/No Decision Epidemic (31 deals, 34%, $217K ARR)**

The single largest loss category. Deals going silent mid-funnel signals either weak qualification or engagement gaps. Many of these (Deal-F308CA, Deal-4664E1, Deal-7CB44D, Deal-2BBA21) explicitly note "ignored multiple pieces of outreach" — buyers are disengaging after intro/demo with no objection surfaced.

Action: Tighten qualification criteria, implement structured re-engagement sequences, and require reps to surface a clear blocker or "no" before closing as MIA.

**2. Product Gaps Hiding Under Wrong Tags (4+ deals, underreported)**

Deal-9048EB (MIA), Deal-3618CC (Lost DM), and Deal-5E64CE (Cost) all describe product shortcomings in the free-text but are tagged incorrectly. The actual product gaps identified:
- Surveys (Deal-3618CC)
- UK/international focus (Deal-981AD4)
- On-site point redemption (Deal-242273)
- Point-labeling customization (Deal-DC77FE)

This intelligence is being lost in the data. When competitors win on features ("more diversified offerings," "offers a lot more"), it's logged as Competitor, not captured as product feedback.

Action: Add a required "competitor feature cited" field when Competitor is selected; audit MIA/Lost DM tags to recover buried product feedback.
pipeline-tiering0.80

? 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 $0.5158 · 218s · in 10 / out 15,725 tok
✓ json_returned✓ tier_vocabulary✗ tier_counts_sum✓ risky_deals_listed✓ no_lock_violation_claimed

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
```json
{
  "tier_counts": {
    "LOCK": 6,
    "ACTION": 20,
    "BUILD": 40,
    "REVIVE": 15,
    "WATCH": 50,
    "RISKY": 10
  },
  "tier_examples": {
    "LOCK": ["Deal-C26D20", "Deal-D348E1", "Deal-403845"],
    "ACTION": ["Deal-C6FE92", "Deal-D73B89", "Deal-93C8BF"],
    "BUILD": ["Deal-66D1FC", "Deal-036E80", "Deal-CC08D1"],
    "REVIVE": ["Deal-2D1F1B", "Deal-7BBDFA", "Deal-950043"],
    "WATCH": ["Deal-332637", "Deal-3795AD", "Deal-E25A09"],
    "RISKY": ["Deal-547B2B", "Deal-B7EBD1", "Deal-9AAE5F"]
  },
  "risky_deals": [
    "Deal-547B2B",
    "Deal-B7EBD1",
    "Deal-A2B47C",
    "Deal-2465CE",
    "Deal-584EE5",
    "Deal-FD9F4E",
    "Deal-A5E80A",
    "Deal-E53952",
    "Deal-9AAE5F",
    "Deal-C61CF7"
  ],
  "lock_violations": 0,
  "pipeline_shape": "Top-heavy funnel: 141 deals concentrated in DS1-DS3 (mostly PIPELINE forecast) with only 6 LOCKs (~$91K combined) at DS4/DS5 showing genuine meeting velocity. Ten RISKY deals flag forecast inflation—six COMMIT deals at DS5 have zero meetings_30d, one DS1+COMMIT jumps the gun, and three BEST_CASE deals (Deal-E53952, Deal-9AAE5F, Deal-C61CF7) have meetings stale >60 days. Fifteen deals dormant >21 days need REVIVE outreach before they slip. The shape suggests strong inbound generation but weak conversion mechanics: deals accumulate at early stages rather than advancing, and reps are over-forecasting without recent buyer engagement to support it."
}
```
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 $0.1005 · 36s · in 10 / out 3,056 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
## CRM Write-Back Extractions

### TX-001 / Deal-CFE7F4
```json
{
  "deal_alias": "Deal-CFE7F4",
  "why_buys": [
    "The big win for us would be automating anniversary and birthday awards — our HR team of three cannot keep up with it manually."
  ],
  "pain_points": [
    "HR team of 3 cannot keep up with manual anniversary/birthday awards",
    "Track everything in spreadsheet, people slip through the cracks",
    "Need SSO and audit logs for IT sign-off"
  ],
  "stakeholders": ["Prospect (VP People)", "Prospect (HR Admin)"],
  "budget_signal": "$40k earmarked for engagement tools this fiscal year",
  "timeline_signal": "before open enrollment in November",
  "competitor_mentioned": "Achievers",
  "next_step": "Security review on September 12",
  "objections": ["Need SSO and audit logs for IT sign-off"],
  "confidence": "high"
}
```

### TX-002 / Deal-70BB30
```json
{
  "deal_alias": "Deal-70BB30",
  "why_buys": [
    "We want to tie recognition to retention for our hourly workforce — regretted turnover there is over 30%."
  ],
  "pain_points": [
    "Regretted turnover over 30% for hourly workforce",
    "Workday integration must be rock solid"
  ],
  "stakeholders": ["Prospect (Head of Total Rewards)", "Prospect (CFO)"],
  "budget_signal": "$25k pilot budget for this quarter",
  "timeline_signal": "Decision by end of September",
  "competitor_mentioned": null,
  "next_step": "Send pilot agreement; prospect will route to legal this week",
  "objections": ["Workday integration is a hard requirement"],
  "confidence": "high"
}
```

### TX-003 / Deal-530B50
```json
{
  "deal_alias": "Deal-530B50",
  "why_buys": [
    "We need to make recognition visible across our 12 retail locations."
  ],
  "pain_points": [
    "Store managers have zero budget autonomy for on-the-spot recognition"
  ],
  "stakeholders": ["Prospect (People Ops Manager)"],
  "budget_signal": null,
  "timeline_signal": "No rush until Q1",
  "competitor_mentioned": "Bucketlist",
  "next_step": "Schedule call with CEO — prospect will send two times",
  "objections": [
    "No urgency until Q1",
    "CEO has to be sold first — she decides anything people-related"
  ],
  "confidence": "medium"
}
```

### TX-004 / Deal-180D02
```json
{
  "deal_alias": "Deal-180D02",
  "why_buys": [
    "We want to consolidate three separate recognition tools into one."
  ],
  "pain_points": [
    "Paying for three tools and none talk to HRIS",
    "Procurement cycle runs 6-8 weeks minimum",
    "Last vendor security review took 3 months"
  ],
  "stakeholders": ["Prospect (VP People)", "Prospect (IT Security Lead)"],
  "budget_signal": "Under $15k annually can be approved without board",
  "timeline_signal": "6-8 week procurement cycle minimum",
  "competitor_mentioned": null,
  "next_step": null,
  "objections": [
    "Procurement cycle 6-8 weeks",
    "Security review hesitation — last one took 3 months"
  ],
  "confidence": "low"
}
```
*Note: "Maybe — I need to check her calendar, no promises" is not an agreed next step.*

### TX-005 / Deal-F8767A
```json
{
  "deal_alias": "Deal-F8767A",
  "why_buys": [
    "Automate service milestones",
    "Give us analytics on recognition equity across departments"
  ],
  "pain_points": [
    "Night-shift teams feel invisible — engagement scores 20 points lower",
    "Exec team skeptical after failed rollout two years ago"
  ],
  "stakeholders": ["Prospect (HR Director)", "Prospect (People Ops Coordinator)"],
  "budget_signal": "$12k approved under engagement line",
  "timeline_signal": "Before January all-hands",
  "competitor_mentioned": "Nectar",
  "next_step": "Present to exec team on October 2",
  "objections": [
    "Mid-pilot with Nectar — need to beat that experience",
    "Exec team skeptical after failed rollout"
  ],
  "confidence": "medium"
}
```

### TX-006 / Deal-EE195F
```json
{
  "deal_alias": "Deal-EE195F",
  "why_buys": [
    "Cut the admin time on service awards."
  ],
  "pain_points": [
    "5 hours/month ordering and shipping plaques"
  ],
  "stakeholders": ["Prospect (HR Manager)"],
  "budget_signal": "Budget isn't the issue — time is",
  "timeline_signal": "Fiscal year resets February; Q1 start realistic",
  "competitor_mentioned": null,
  "next_step": "Send one-pager; prospect will forward to COO this week",
  "objections": [
    "COO usually prefers building things in-house"
  ],
  "confidence": "medium"
}
```
*Note: Comparing against internal build, not a named competitor.*

---

**Summary table:**

| Deal | Budget | Timeline | Competitor | Next Step Agreed | Confidence |
|------|--------|----------|------------|------------------|------------|
| Deal-CFE7F4 | $40k | Nov (open enrollment) | Achievers | Yes (Sep 12 security) | high |
| Deal-70BB30 | $25k pilot | End of Sep | — | Yes (pilot agreement) | high |
| Deal-530B50 | — | Q1 | Bucketlist | Yes (CEO call) | medium |
| Deal-180D02 | <$15k | 6-8 wk procurement | — | No | low |
| Deal-F8767A | $12k | Jan all-hands | Nectar | Yes (Oct 2 exec pres) | medium |
| Deal-EE195F | not constrained | Q1 / Feb FY reset | — | Yes (one-pager to COO) | medium |
next-to-closePASS 1.00

? The Question

Which 3 deals are closest to signature right now? Use the CRM data and the Slack thread; Slack often has the real status before the CRM does. For each: deal, amount, why it is close, what is left. Under 250 words.
cost $0.0458 · 21s · in 10 / out 1,054 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
**Top 3 Deals Closest to Signature**

1. **Deal-547B2B** | $11,200 | Alex Franklin
   - **Why close:** Slack (09-04) says redlines came back clean, signing page is out, VP People confirmed signing "tomorrow." Alex explicitly called it "signature-imminent."
   - **What's left:** Waiting on signature — no blockers identified.

2. **Deal-403845** | $9,000 | Alex Franklin
   - **Why close:** CRM shows DS5/COMMIT with 09-11 close. Dana confirmed in Slack "moving fine — the order form is with their finance team."
   - **What's left:** Finance approval and signature.

3. **Deal-B7EBD1** | $9,000 | Dana Mercer
   - **Why close:** DS5/COMMIT with the earliest close date (09-10). No negative signals in Slack.
   - **What's left:** Unknown — no Slack update, but CRM status and date suggest final stages.

---

**Excluded despite CRM data:**
- **Deal-2465CE** ($5,400): CRM still shows DS5/COMMIT for 09-10, but Slack reveals champion left and procurement froze vendors. Dana says "blocked" and "realistically a Q4 deal now." CRM is stale.

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

? The Question

Surface true product gaps raised by prospects in active deals. For each candidate: quote the prospect line, classify as real gap, plan gate, or rollout/enablement issue using the product docs excerpt, and name the deal and amount. Only real gaps go in the summary. Rep language does not count as prospect voice.
cost $0.0468 · 21s · in 10 / out 1,204 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
## Product Gap Analysis — Prospect Voice Only

| Transcript | Deal | Prospect Quote | Classification | Rationale |
|------------|------|----------------|----------------|-----------|
| TG-001 | Deal-EC3025 | "We need SCIM user provisioning — our security team will not approve any tool that requires manual account management." | **REAL GAP** | Product docs: "SCIM user provisioning... NOT currently listed as supported capabilities." No tier offers it. |
| TG-002 | Deal-D0D6B5 | "Our payroll runs on ADP Workforce Now and I don't see ADP anywhere in your integration list — that's a dealbreaker for us." | **REAL GAP** | Product docs: "ADP Workforce Now integrations are NOT currently listed as supported capabilities." HRIS list is Workday, BambooHR, Gusto, Rippling only. |
| TG-003 | Deal-CFE7F4 | "I tried to build a custom report combining recognition frequency with turnover and couldn't — is that possible on our current plan?" | Plan gate | Custom report builder exists on Enterprise tier per docs. Not a product gap — upgrade path available. |
| TG-004 | Deal-84DBA6 | "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." | Rollout/enablement | Slack integration works on all plans. Issue is adoption and manager training, not missing functionality. |
| TG-005 | Deal-36C33F | *(No prospect-voiced gap)* | Excluded | Mobile app gap raised by rep (Alex Franklin), not prospect. Prospect response: "The web version should be fine for our office staff for now." |

---

## Summary: True Product Gaps

| Gap | Deal | Prospect Role | Amount |
|-----|------|---------------|--------|
| SCIM user provisioning | Deal-EC3025 | IT Security Lead | *not provided in data* |
| ADP Workforce Now integration | Deal-D0D6B5 | HRIS Manager | *not provided in data* |

**Note:** Deal amounts are missing from transcripts_gaps.csv — cannot report dollar exposure without that field.
rep-performance
stale-pipeline-by-rep0.67

? 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 $0.9391 · 362s · in 10 / out 32,670 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
Snapshot: 2026-09-05. Stale = last contact (email/call/meeting) before 2026-08-29 (>7 days ago).
Dates beyond 2026-09-05 treated as scheduled (future), not logged — excluded from recency.

---

## BRYCE HARMON (119337721)

| Deal Alias | Stage | Amount | Days Since Last Contact |
|------------|-------|--------|-------------------------|
| Deal-2D1F1B | DS1 | $240,000 | 81 |
| Deal-66D1FC | DS1 | $99,000 | 16 |
| Deal-950043 | DS1 | $70,000 | 19 |
| Deal-B23205 | DS1 | $45,000 | 16 |
| Deal-7BBDFA | DS3 | $37,440 | 46 |
| Deal-332637 | DS2 | $36,000 | 9 |
| Deal-1BEEBF | DS1 | $31,500 | 19 |
| Deal-A414F6 | DS1 | $25,200 | 19 |
| Deal-C5658B | DS1 | $23,400 | 16 |
| Deal-40522D | DS3 | $21,000 | 19 |
| Deal-C1FA6D | DS1 | $18,000 | 16 |
| Deal-01E193 | DS1 | $12,600 | 8 |
| Deal-F0EBBB | DS3 | $11,400 | 24 |
| Deal-927338 | DS1 | $10,920 | 18 |
| Deal-E25A09 | DS1 | $6,000 | 9 |
| Deal-C9C286 | DS2 | $5,502 | 9 |
| Deal-012CB1 | DS1 | $1 | 23 |
| Deal-3795AD | DS2 | $1 | 8 |

**Bryce Harmon: 18 stale deals, $692,964 total**

Arithmetic: 240000+99000+70000+45000+37440+36000+31500+25200+23400+21000+18000+12600+11400+10920+6000+5502+1+1 = $692,964

---

## DANA MERCER (83155923)

| Deal Alias | Stage | Amount | Days Since Last Contact |
|------------|-------|--------|-------------------------|
| Deal-44EA29 | DS2 | $60,000 | 10 |
| Deal-E51FB7 | DS2 | $43,875 | 12 |
| Deal-B42F46 | DS1 | $27,000 | 19 |
| Deal-BA3DDC | DS3 | $23,400 | 15 |
| Deal-9DDE86 | DS2 | $20,000 | 15 |
| Deal-215CCA | DS3 | $18,900 | 17 |
| Deal-5EED42 | DS3 | $16,250 | 11 |
| Deal-57887A | DS2 | $15,000 | 8 |
| Deal-944310 | DS4 | $10,500 | 33 |
| Deal-B7EBD1 | DS5 | $9,000 | 16 |
| Deal-3974EB | DS4 | $9,000 | 8 |
| Deal-F40F04 | DS2 | $8,100 | 15 |
| Deal-7599B8 | DS3 | $7,350 | 18 |
| Deal-87DDD1 | DS1 | $5,000 | 19 |
| Deal-F336B6 | DS3 | $4,200 | 15 |
| Deal-0660B4 | DS4 | $1,920 | 16 |

**Dana Mercer: 16 stale deals, $279,495 total**

Arithmetic: 60000+43875+27000+23400+20000+18900+16250+15000+10500+9000+9000+8100+7350+5000+4200+1920 = $279,495

---

## COLE INGRAM (83155924)

| Deal Alias | Stage | Amount | Days Since Last Contact |
|------------|-------|--------|-------------------------|
| Deal-D04904 | DS2 | $58,529.25 | 11 |
| Deal-B25F40 | DS3 | $40,000 | 8 |
| Deal-813836 | DS2 | $32,175 | 11 |
| Deal-1BA595 | DS2 | $31,750 | 11 |
| Deal-CFE1E8 | DS3 | $18,000 | 11 |
| Deal-CD47A6 | DS2 | $12,168 | 11 |
| Deal-627646 | DS3 | $11,193 | 11 |
| Deal-FF809F | DS2 | $7,781.20 | 11 |
| Deal-AF932D | DS2 | $7,225.40 | 11 |
| Deal-A71728 | DS2 | $6,947.50 | 11 |
| Deal-8BC9F5 | DS2 | $5,616 | 10 |
| Deal-175395 | DS3 | $4,779.88 | 11 |
| Deal-481E24 | DS3 | $4,140 | 10 |
| Deal-C7F9BF | DS2 | $3,360 | 11 |
| Deal-2F3A66 | DS3 | $3,334.80 | 11 |
| Deal-342E96 | DS2 | $2,700 | 24 |
| Deal-E568D5 | DS3 | $1,875 | 11 |
| Deal-FD9F4E | DS5 | $1,330 | 10 |

**Cole Ingram: 18 stale deals, $252,905.03 total**

Arithmetic: 58529.25+40000+32175+31750+18000+12168+11193+7781.2+7225.4+6947.5+5616+4779.88+4140+3360+3334.8+2700+1875+1330 = $252,905.03

---

## ALEX FRANKLIN (84342457)

| Deal Alias | Stage | Amount | Days Since Last Contact |
|------------|-------|--------|-------------------------|
| Deal-CC08D1 | DS1 | $24,000 | 16 |
| Deal-E73427 | DS3 | $18,000 | 10 |
| Deal-885F45 | DS2 | $9,300 | 12 |
| Deal-C2FF3C | DS1 | $8,316 | 10 |
| Deal-3EED2C | DS2 | $7,200 | no engagement data |
| Deal-0D2F7A | DS3 | $5,100 | 12 |
| Deal-6C60D4 | DS3 | $4,800 | 12 |
| Deal-13FEBD | DS2 | $4,680 | 12 |
| Deal-819506 | DS1 | $4,400 | 8 |
| Deal-9D0060 | DS3 | $3,840 | 12 |
| Deal-690476 | DS2 | $3,600 | 18 |
| Deal-C6D97A | DS4 | $3,240 | 8 |
| Deal-EE195F | DS3 | $3,120 | 8 |
| Deal-278DEC | DS3 | $2,700 | 8 |
| Deal-635B8E | DS3 | $2,600 | 18 |
| Deal-6883F3 | DS1 | $2,400 | 16 |
| Deal-4A13AD | DS3 | $2,160 | 26 |
| Deal-F67D31 | DS2 | $1,800 | 8 |
| Deal-5FDCE4 | DS3 | $1,600 | 12 |
| Deal-BA571A | DS4 | $1,080 | 18 |

**Alex Franklin: 20 stale deals, $113,936 total**

Arithmetic: 24000+18000+9300+8316+7200+5100+4800+4680+4400+3840+3600+3240+3120+2700+2600+2400+2160+1800+1600+1080 = $113,936

---

## FARID OSMAN (716654662)

| Deal Alias | Stage | Amount | Days Since Last Contact |
|------------|-------|--------|-------------------------|
| Deal-8BA24E | DS1 | $2,880 | 8 |
| Deal-8FDCD2 | DS1 | $1 | 15 |

**Farid Osman: 2 stale deals, $2,881 total**

---

## ELENA SINCLAIR (701163055)

| Deal Alias | Stage | Amount | Days Since Last Contact |
|------------|-------|--------|-------------------------|
| Deal-57FF13 | DS1 | $2,100 | no engagement data |

**Elena Sinclair: 1 stale deal, $2,100 total**

---

## SUMMARY BY OWNER

| Owner | Stale Deals | Stale Amount |
|-------|-------------|--------------|
| Bryce Harmon | 18 | $692,964 |
| Dana Mercer | 16 | $279,495 |
| Cole Ingram | 18 | $252,905.03 |
| Alex Franklin | 20 | $113,936 |
| Farid Osman | 2 | $2,881 |
| Elena Sinclair | 1 | $2,100 |
| **TOTAL** | **75** | **$1,344,281.03** |
activity-mix-vs-outcomePASS 1.00

? The Question

The snapshot date is 2026-09-05. For each sales rep (owner), compute the last-30-day totals of emails, calls, and meetings from the engagements table, and the number of deals that entered DS2 in the last 30 days (t_ds2 within 30 days of the snapshot). Report per rep: the activity mix as percentages (emails/calls/meetings share of that rep's total activities), total activities per DS2 entry, and rank the reps by that efficiency ratio (lowest activities per DS2 entry = most efficient). Name the most efficient rep and the highest-volume rep, and note if they differ. Do not attribute cause.
cost $0.6926 · 238s · in 10 / out 22,812 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
**Rep Activity & DS2 Efficiency Analysis (30-day window: 2026-08-06 to 2026-09-05)**

---

**Per-Rep Summary:**

| Rep | Emails | Calls | Meetings | Total | DS2 Entries | Activities/DS2 |
|-----|--------|-------|----------|-------|-------------|----------------|
| Alex Franklin | 307 | 36 | 41 | 384 | 18 | 21.3 |
| Bryce Harmon | 162 | 0 | 43 | 205 | 4 | 51.3 |
| Cole Ingram | 96 | 14 | 1 | 111 | 2 | 55.5 |
| Farid Osman | 38 | 0 | 34 | 72 | 1 | 72.0 |
| Dana Mercer | 84 | 18 | 11 | 113 | 1 | 113.0 |
| Elena Sinclair | 0 | 0 | 0 | 0 | 0 | N/A |

---

**Activity Mix (% of rep's total):**

| Rep | Emails % | Calls % | Meetings % |
|-----|----------|---------|------------|
| Alex Franklin | 79.9% | 9.4% | 10.7% |
| Bryce Harmon | 79.0% | 0.0% | 21.0% |
| Cole Ingram | 86.5% | 12.6% | 0.9% |
| Farid Osman | 52.8% | 0.0% | 47.2% |
| Dana Mercer | 74.3% | 15.9% | 9.7% |

---

**Efficiency Ranking (lowest activities per DS2 entry = most efficient):**

1. Alex Franklin — 21.3 (384 activities ÷ 18 DS2 entries)
2. Bryce Harmon — 51.3 (205 ÷ 4)
3. Cole Ingram — 55.5 (111 ÷ 2)
4. Farid Osman — 72.0 (72 ÷ 1)
5. Dana Mercer — 113.0 (113 ÷ 1)

Elena Sinclair excluded (0 DS2 entries).

---

**Findings:**

- Most efficient rep: **Alex Franklin** (21.3 activities per DS2 entry)
- Highest-volume rep: **Alex Franklin** (384 total activities)

They are the **same rep**. No causal attribution implied.
ae-qtd-scorecardPASS 1.00

? The Question

Build a QTD scorecard for the named AE as of 2026-09-05: bookings vs quota with attainment percent, new vs expansion split, active pipeline by stage with amount, rolling 90-day DS2-to-won rate, win and loss counts with top loss reason, activity volume by type in the last 30 days. Exclude any closed-won deal dated before the quarter. Close with three coaching observations grounded in the numbers.
cost $0.3842 · 170s · in 10 / out 12,608 tok
✓ bookings✓ quota✓ attainment✓ new_expansion_split✓ top_loss_reason✓ coaching_obs

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
# QTD Scorecard: Alex Franklin
As of 2026-09-05 | Q3 2026 (Jul 1 – Sep 30)

---

## BOOKINGS VS QUOTA

Q3 Closed-Won (close_date >= 2026-07-01):
  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

QTD Bookings:   $150,000
Q3 Quota:       $200,000
Attainment:     75.0%  ($150,000 / $200,000)
Gap to Quota:   $50,000

---

## NEW VS EXPANSION SPLIT

New Business:   $113,500  (75.7%)
  40,000 + 35,000 + 21,000 + 11,000 + 6,500 = $113,500

Expansion:       $36,500  (24.3%)
  20,000 + 12,000 + 4,500 = $36,500

---

## ACTIVE PIPELINE BY STAGE

Stage  | Count | Amount
-------|-------|----------
DS1    |  20   | $284,621
DS2    |  28   | $353,760
DS3    |  67   | $552,705
DS4    |   4   |  $21,090
DS5    |   5   |  $45,730
-------|-------|----------
TOTAL  | 124   | $1,257,906

Late-stage (DS4+DS5): $66,820 (5.3% of pipeline)

---

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

Window: 2026-06-08 to 2026-09-05 (deals with entered_ds2 in range)

Resolved deals that entered DS2 in window:
  Won:   8
  Lost: 27
  -----
  Total: 35

DS2-to-Won Rate: 22.9%  (8 / 35)

---

## WIN/LOSS RECORD (QTD)

Wins:   8
Losses: 27

Loss Reasons:
  Lost- Timing (1 year or more)     13  (48.1%)
  MIA                                5  (18.5%)
  Competitor                         5  (18.5%)
  Lost DM                            2  ( 7.4%)
  Feature Request                    1  ( 3.7%)
  Lost- Does not fit ICP             1  ( 3.7%)

Top Loss Reason: "Lost- Timing (1 year or more)" — 13 of 27 losses

---

## ACTIVITY VOLUME (LAST 30 DAYS)

Activity Type | Volume
--------------|--------
Emails        |    740
Calls         |    112
Meetings      |    114
Notes         |     50
--------------|--------
Total         |  1,016

Avg per open deal: 6.0 emails, 0.9 calls, 0.9 meetings

---

## COACHING OBSERVATIONS

1. Timing losses dominate the funnel. 48% of all Q3 losses (13 of 27) went to "Lost- Timing (1 year or more)." Deals are entering pipeline before buyers have active budget or decision authority. Tighten discovery to confirm fiscal timing, budget allocation status, and decision timeline before advancing to DS2. Each premature qualification burns activity on deals that were never closable this quarter.

2. 22.9% DS2-to-won rate is a conversion leak. 3 out of 4 deals that reach qualification stage are lost. Combined with the $1.26M pipeline, the problem isn't volume—it's yield. Examine what happens between DS2 and DS4: are demos compelling? Is multi-threading happening? Are proposals landing with economic buyers or just champions?

3. Quota math requires late-stage urgency. $50K needed with 25 days left, but only $66,820 sits in DS4/DS5. The $1.19M in DS1-DS3 won't close in Q3. Prioritize the 9 late-stage deals (Deal-403845, Deal-547B2B, Deal-A2B47C, Deal-C61CF7, Deal-D348E1 in DS5; Deal-1FC049, Deal-C6D97A, Deal-BA571A, Deal-5408B0 in DS4) over top-of-funnel work for the rest of the quarter.
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 $0.1156 · 43s · in 10 / out 3,681 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
## Threading Analysis

**Cutoff for "active":** engaged on/after July 10, 2026 (60 days before Sep 8) AND is_former = false

**Data gap:** deal_contacts.csv contains no `amount` or `stage` columns. I cannot provide those values or stage-specific persona recommendations.

---

### Flagged Deals

| Deal | Company | Active Contacts | Personas Present | Personas Missing | Flag Reason | Unengaged Contact On File |
|------|---------|-----------------|------------------|------------------|-------------|---------------------------|
| **Deal-EC3025** | C-FDD0C7 | 1 | champion | economic buyer, HR admin, IT security, finance | Single-threaded | CT-6827DB (economic buyer) |
| **Deal-92D97D** | C-E23238 | 1 | HR admin | economic buyer, champion, IT security, finance | Single-threaded | none on file |
| **Deal-36C33F** | C-077A0E | 1 | IT security | economic buyer, champion, HR admin, finance | Single-threaded | CT-1DB73E (economic buyer) |
| **Deal-FCBE5B** | C-737030 | 1 | champion | economic buyer, HR admin, IT security, finance | Single-threaded | none on file |
| **Deal-F9A08A** | C-0D15DF | 1 | champion | economic buyer, HR admin, IT security, finance | Single-threaded | CT-697541 (economic buyer) |
| **Deal-50D386** | C-EB10E4 | 2 | champion, HR admin | economic buyer, IT security, finance | Under-threaded (<3) | CT-A1C4B3 (economic buyer) |
| **Deal-D0D6B5** | C-32918E | 3 | champion (×3) | economic buyer, HR admin, IT security, finance | All one persona | CT-1FA4DB (economic buyer) |
| **Deal-5BFE3B** | C-535D36 | 2 | champion (×2) | economic buyer, HR admin, IT security, finance | Under-threaded + one persona | none on file |
| **Deal-885F45** | C-5E8EFB | 2 | economic buyer, champion | HR admin, IT security, finance | Under-threaded (<3) | CT-B3F25D (IT security) |
| **Deal-5408B0** | C-2AE3AA | 2 | champion, HR admin | economic buyer, IT security, finance | Under-threaded (<3) | CT-07FA76 (economic buyer) |
| **Deal-C6D97A** | C-5A8FC2 | 3 | champion (×3) | economic buyer, HR admin, IT security, finance | All one persona | none on file |

---

### Arithmetic Detail

- **Deal-EC3025:** 2 contacts total. CT-F2C1AE is_former=true → excluded. 1 active.
- **Deal-92D97D:** 2 contacts. CT-A902AE last engaged Jun 1 (>60 days) → excluded. 1 active.
- **Deal-36C33F:** 3 contacts. CT-405B45 and CT-86B22F both is_former=true → excluded. 1 active.
- **Deal-F9A08A:** 2 contacts. CT-913581 last engaged Jun 20 (>60 days) → excluded. 1 active.
- **Deal-D0D6B5:** 3 contacts, all active, all persona=champion. Fails diversity test.
- **Deal-C6D97A:** 3 contacts, all active, all persona=champion. Fails diversity test.

---

### Most Valuable Persona to Add (general guidance, stage unknown)

Without stage data, the priority is:

1. **Economic buyer** — required for deals missing one. Matches available unengaged contact at 6 companies.
2. **Champion** — foundational; Deal-36C33F has IT security only, needs champion but none on file.
3. **IT security / finance** — typically late-stage; Deal-885F45 has IT security contact available (CT-B3F25D).

**Summary:** 11 deals flagged. 6 have an actionable unengaged contact on file (5 economic buyers, 1 IT security). 5 have no matching unengaged contact in the provided data.
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 $0.0848 · 36s · in 10 / out 2,188 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
## Rep Analysis: Alex Franklin — Last 10 Calls

### Opening (first 5 minutes)
**8 of 10 calls** open with the same social-proof line:
> "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 exceptions: TT-004 (agenda-led) and TT-009 (pricing follow-up).

---

### Three Most Common Objections & Handling

| Objection | Count | Handling |
|-----------|-------|----------|
| Budget locked | 4 | Reframes to turnover savings: "Most teams fund this out of turnover savings — that retailer saved about $210k in avoided backfills, which is how their finance team signed off." |
| Revisit next quarter | 3 | Proposes pilot: "What if we scope a 90-day pilot with one department so you have internal data before next quarter's planning?" |
| Spreadsheet status quo | 3 | Automation + analytics: "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." |

---

### Next Step Agreed — Rate
**7 / 10 = 70%**

Agreed (7): TT-001, TT-002, TT-003, TT-005, TT-006, TT-008, TT-009
Not agreed (3): TT-004, TT-007, TT-010

---

### Competitors Raised by Prospects

| Competitor | Call | Deal Alias | Quote |
|------------|------|------------|-------|
| Awardco | TT-003 | Deal-547B2B | "We're also in late talks with Awardco — their rewards catalog looks bigger than yours." |
| Kudos | TT-007 | Deal-EDC141 | "How are you different from Kudos? Our CEO used them at her last company." |

(Workhuman mentioned in TT-005 but by Alex, not prospect — excluded.)

---

### Coaching Notes

1. **Soft closes leak deals.** In the 3 calls with no next step (TT-004, TT-007, TT-010), Alex accepted stalls ("I'll leave it with you," "Fair enough," "Understood, thanks for the candor") without proposing even a calendar placeholder. Teach: always offer a fallback step — e.g., "Can I send a one-pager and follow up in two weeks?"

2. **Vary the opener when a competitor is already in play.** The 400-person retailer case lands well, but in TT-003 the prospect raised Awardco before minute 5. When the prospect signals they're evaluating, lead with differentiation ("where we win is automation and the analytics") earlier instead of the standard case study.
reporting-analytics
q3-weighted-forecastPASS 1.00

? The Question

Below are the open deals in the default sales pipeline with a close date between 2026-07-01 and 2026-10-15, pulled from the CRM warehouse on 2026-09-05. Stages: DS1 (discovery) through DS5 (contract). Build the Q3 2026 forecast (quarter = 2026-07-01 to 2026-09-30). Weighting rule: 100 percent of COMMIT plus 35 percent of BEST_CASE. PIPELINE deals count zero. Only deals with a close date inside the quarter count. Report: COMMIT total, BEST_CASE total, weighted forecast, count of deals in each category inside the quarter, the deals you excluded for being outside the quarter (count and total amount), and the top 5 BEST_CASE deals by amount inside the quarter. Show arithmetic. Then, in a final '## Data quality' section of no more than four sentences, name the data quality problems in this extract that would make the forecast unreliable if it were run unattended.
cost $0.4668 · 139s · in 10 / out 13,326 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 (2026-07-01 to 2026-09-30)

### Category Totals (deals with close_date in Q3)

**COMMIT** — 7 deals, $44,729
```
Deal-547B2B    11,200   (09-11)
Deal-B7EBD1     9,000   (09-10)
Deal-403845     9,000   (09-11)
Deal-A2B47C     6,360   (09-11)
Deal-2465CE     5,400   (09-10)
Deal-A5E80A     2,520   (09-11)
Deal-499BF6     1,249   (09-30)
───────────────────────
              $44,729
```

**BEST_CASE** — 24 deals, $203,565
```
Sum of 24 deals: 38,935 + 24,000 + 19,656 + 16,250 + 11,116 + 10,800 + 
10,500 + 9,890 + 9,720 + 9,000 + 7,200 + 3,840 + 3,780 + 3,600 + 3,240 + 
3,150 + 3,120 + 3,060 + 2,916 + 2,760 + 2,484 + 2,100 + 1,920 + 528 = $203,565
```

**PIPELINE** — 23 deals, $201,637.40 (counts $0 toward forecast)

### Weighted Forecast Calculation

```
COMMIT contribution:    100% × $44,729    =  $44,729.00
BEST_CASE contribution:  35% × $203,565   =  $71,247.75
PIPELINE contribution:    0% × $201,637   =       $0.00
─────────────────────────────────────────────────────────
WEIGHTED FORECAST                          $115,976.75
```

### Deals Excluded (close_date outside Q3)

32 deals excluded, total $227,575

All have close_date in October (10-01 through 10-15). Breakdown:
- 1 COMMIT (Deal-D348E1, $13,770)
- 9 BEST_CASE ($28,240)
- 22 PIPELINE ($185,565)

### Top 5 BEST_CASE Deals in Q3 by Amount

| Rank | Deal Alias   | Amount   | Close Date |
|------|--------------|----------|------------|
| 1    | Deal-2D7423  | $38,935  | 09-30      |
| 2    | Deal-25F752  | $24,000  | 09-25      |
| 3    | Deal-E53952  | $19,656  | 09-30      |
| 4    | Deal-5EED42  | $16,250  | 09-30      |
| 5    | Deal-FA32A0  | $11,116  | 09-25      |

---

## Data quality

The `owner` field is blank on 85 of 86 deals, making rep-level forecasting and quota roll-up impossible. Two COMMIT deals (Deal-A5E80A at DS1, Deal-499BF6 at DS2) sit at early-stage discovery, suggesting forecast_category is not validated against stage progression. The `why_buys_chars` field is zero on 78 deals, indicating incomplete discovery documentation that would flag weak qualification. Only anonymized deal aliases exist—no company or account names—so external validation against the source system is impossible.
aha-moment-2x20.83

? 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 $0.5904 · 232s · in 10 / out 20,562 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
## Activation Hypothesis Test: 2x2 Retention Matrix

**Definitions:**
- Givers signal: m1_users >= 5
- Redemption signal: m1_redemptions >= 1
- Retained: current_status = 'active'

### Full 2x2 Results

| Cohort | n | Retained | 24-Mo Retention |
|--------|---|----------|-----------------|
| **Both signals** (≥5 givers AND ≥1 redemption) | 47 | 31 | **66.0%** |
| **Givers-only** (≥5 givers, 0 redemptions) | 49 | 23 | 46.9% |
| **Redemption-only** (<5 givers, ≥1 redemption) | 29 | 9 | 31.0% |
| **Neither** (<5 givers, 0 redemptions) | 95 | 38 | 40.0% |
| **Total** | 220 | 101 | 45.9% |

**Arithmetic check:** 47 + 49 + 29 + 95 = 220 companies. 31 + 23 + 9 + 38 = 101 retained.

### Exclusions

**0 companies excluded.** All 220 rows have defined values for m1_users, m1_redemptions, and current_status.

### Single Signal with Largest Retention Lift

**Givers signal (m1_users >= 5)** provides the only positive single-signal lift:
- Givers-only vs Neither: 46.9% - 40.0% = **+6.9 pp**
- Redemption-only vs Neither: 31.0% - 40.0% = **-9.0 pp** (worse than baseline)

### What This Does and Does Not Prove

**DOES prove:**
- Strong correlation between Month-1 dual activation and 24-month retention in this 2023 cohort
- Broad user adoption (≥5 givers) is the more predictive single signal
- The combination (both signals) outperforms either signal alone by 19+ pp

**DOES NOT prove:**
- Causation — healthier/better-fit companies may naturally activate faster (selection effect)
- Generalizability beyond 2023 signup cohort
- Independence from confounders — tier, ARR, industry, integrations (has_slack, has_hris) not controlled
- That forcing redemptions early will improve retention — the redemption-only cohort (31.0%) actually underperforms "neither" (40.0%), suggesting redemptions without broad adoption may indicate problematic usage patterns (e.g., a handful of users treating it as personal rewards)
arr-rollforward-reconciliationPASS 1.00

? The Question

Reconcile ARR between CRM company records and billing subscriptions as of 2026-09-05. Billing ARR is MRR x 12. Report both totals, the variance, and a decomposition into buckets (status mismatch, rounding, missing records, other) that sums exactly to the variance. List each mismatched account with a suggested owner. Business rule: any subscription with a term other than 12 months must have cf_agreement_end_date populated; list violations.
cost $0.2284 · 81s · in 10 / out 8,259 tok
✓ billing_total✓ crm_total✓ variance✓ buckets_sum✓ violations_listed

✓ Correct Answer (ground truth)

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

Model's actual answer

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

### Summary Totals

| Source | ARR |
|--------|-----|
| Billing (active subscriptions × 12) | $604,739.28 |
| HubSpot (company_arr.csv) | $603,581.76 |
| **Variance (HubSpot − Billing)** | **−$1,157.52** |

---

### Variance Decomposition

| Bucket | Amount | Accounts |
|--------|--------|----------|
| Status Mismatch | +$13,158.48 | 2 |
| Missing Records | −$11,952.00 | 2 |
| Rounding | +$36.00 | 2 |
| Other (value mismatch) | −$2,400.00 | 1 |
| **Total** | **−$1,157.52** | |

Arithmetic check: +13,158.48 − 11,952.00 + 36.00 − 2,400.00 = −1,157.52 ✓

---

### Mismatched Accounts Detail

**Status Mismatch (cancelled in billing, ARR remains in HubSpot):**
| Alias | Subscription | HubSpot ARR | Billing ARR | Delta | Suggested Owner |
|-------|--------------|-------------|-------------|-------|-----------------|
| C-0C8323BF | SUB-000E | $4,905.24 | $0 | +$4,905.24 | Sales Ops (zero out HubSpot) |
| C-0DC4FB8C | SUB-000F | $8,253.24 | $0 | +$8,253.24 | Sales Ops (zero out HubSpot) |

**Missing Records:**
| Alias | Subscription | HubSpot ARR | Billing ARR | Delta | Suggested Owner |
|-------|--------------|-------------|-------------|-------|-----------------|
| C-21629AA4 | SUB-0004 (active) | — | $28,449.24 | −$28,449.24 | Sales Ops (create company record) |
| C-0D5BBE3A | — | $16,497.24 | — | +$16,497.24 | Billing Ops (locate subscription or zero HubSpot) |

**Rounding:**
| Alias | Subscription | HubSpot ARR | Billing ARR (MRR×12) | Delta |
|-------|--------------|-------------|----------------------|-------|
| C-0D66DF9E | SUB-0005 | $23,200.00 | $23,184.00 | +$16.00 |
| C-14D70CE0 | SUB-0008 | $18,200.00 | $18,180.00 | +$20.00 |

Suggested owner: Finance (immaterial; accept or round billing MRR)

**Other (significant value mismatch):**
| Alias | Subscription | HubSpot ARR | Billing ARR (MRR×12) | Delta | Suggested Owner |
|-------|--------------|-------------|----------------------|-------|-----------------|
| C-0F7269D7 | SUB-0006 | $24,396.00 | $26,796.00 | −$2,400.00 | RevOps (investigate; $200/mo discrepancy) |

---

### Business Rule Violations

**Term ≠ 12 months with missing cf_agreement_end_date:**

| Subscription | Alias | Term | cf_agreement_end_date | Action |
|--------------|-------|------|----------------------|--------|
| SUB-0002 | C-1794A52C | 24 mo | EMPTY | Populate end date |
| SUB-0019 | C-22170CA1 | 36 mo | EMPTY | Populate end date |

---

### Arithmetic Backup

**Billing ARR (active only):**
Sum of active MRR = $50,394.94
$50,394.94 × 12 = $604,739.28

**HubSpot ARR:**
Sum of 39 company records = $603,581.76
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 $0.1926 · 68s · in 10 / out 6,595 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
## KVM Analysis: 2026-08 vs 2026-07

**Arithmetic (n=30 companies, simple averages):**

- Giving Rate: July sum = 18.0689, Aug sum = 18.0814 → avgs 0.6023, 0.6027
- Redemptions/User: July sum = 51.8995, Aug sum = 51.9049 → avgs 1.7300, 1.7302  
- 1:1 Engagement: July sum = 13.4066, Aug sum = 13.4153 → avgs 0.4469, 0.4472
- Pulse Engagement: July sum = 18.0176, Aug sum = 15.2583 → avgs 0.6006, 0.5086

| KVM | 2026-08 | 2026-07 | Δ Abs | Δ Rel | Dir |
|-----|---------|---------|-------|-------|-----|
| Giving Rate | 0.6027 | 0.6023 | +0.0004 | +0.07% | ↗ |
| Redemptions/User | 1.7302 | 1.7300 | +0.0002 | +0.01% | → |
| 1:1 Engagement | 0.4472 | 0.4469 | +0.0003 | +0.06% | ↗ |
| Pulse Engagement | 0.5086 | 0.6006 | −0.0920 | −15.3% | ↓ |

**Largest relative move:** Pulse Engagement (−15.3%)

**Segment driver:** size_band = enterprise. All 10 enterprise accounts dropped from avg 0.550 → 0.274 (−50.1%), while SMB (−0.2%) and mid_market (+0.2%) held flat. Plan_tier is uniformly tier_three across all accounts, so it cannot explain the variance.
rewards-ytd-sectionPASS 1.00

? The Question

Produce the redemption section of the weekly rewards report through the last completed month (2026-08): redemption count, spend, unique redeemers, redemptions per redeemer, provider mix as percent of spend, top 5 countries by redemptions. Say which month is the last completed month. Provider shares must sum to 100.
cost $0.8228 · 292s · in 11 / out 18,646 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
**REDEMPTION SECTION — Weekly Rewards Report**
Last completed month: August 2026

SUMMARY METRICS
  Redemption count:        378
  Total spend:             $27,846.00
  Unique redeemers:        235
  Redemptions per redeemer: 1.61

PROVIDER MIX (% of spend)
  custom       $10,873  (39.0%)
  Tremendous   $8,505   (30.5%)
  Snappy       $5,238   (18.8%)
  TangoCard    $3,230   (11.6%)
  TOTAL        $27,846  (100.0%)

TOP 5 COUNTRIES BY REDEMPTIONS
  1. US — 244
  2. CA — 24
  3. AU — 21
  4. GB — 17 (tie)
  5. NL — 17 (tie)

Arithmetic check:
  39.0% + 30.5% + 18.8% + 11.6% = 99.9% (rounds to 100.0%)
customer-success
churn-save-eligibilityPASS 1.00

? The Question

Which at-risk accounts qualify for a churn-save offer under the documented eligibility rules, what amount is at stake per account and in total, and which play fits each (usage revival, executive touch, commercial concession)? Cite the signal that justifies each play. List accounts that look at risk but do not qualify and why.
cost $0.1146 · 47s · in 10 / out 3,800 tok
✓ eligible_set✓ total_at_stake✓ plays_cited✓ noneligible_named✓ rules_applied

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
## Churn-Save Eligibility Analysis

**Snapshot date:** 2026-09-05  
**120-day window ends:** 2027-01-03

**Rules applied:** Account must satisfy ALL THREE:
- R1: health_score < 60
- R2: churn_save_eligible_amount > 0
- R3: renewal_date ≤ 2027-01-03

---

### Qualifying Accounts (8)

| Account | Health | Eligible Amt | Renewal | Play | Justifying Signal |
|---------|--------|-------------|---------|------|-------------------|
| C-0F6C0F34 | 51 | $49,707 | 2026-10-03 | Executive touch | No champion (champion_active=false) despite growing usage—need exec re-engagement |
| C-0E9C27D1 | 39 | $41,235 | 2026-09-24 | Commercial concession | 85% seat utilization (134/157), active champion, flat usage—engagement healthy, likely pricing objection |
| C-0B360C78 | 57 | $35,748 | 2026-10-28 | Commercial concession | Growing usage, active champion, 75% utilization (246/327)—value delivered but still at-risk score |
| C-0CEF69FD | 53 | $32,621 | 2026-11-21 | Executive touch | No champion (champion_active=false) despite growing usage |
| C-0B827671 | 56 | $25,365 | 2026-11-14 | Usage revival | Declining usage trend with active champion to drive re-adoption |
| C-0D3278C7 | 54 | $17,602 | 2026-11-12 | Usage revival | Declining usage trend, only 33% seats used (126/380), but champion active |
| C-0CA21961 | 58 | $16,829 | 2026-12-28 | Usage revival | Only 26% seat utilization (84/325), flat trend—champion active to push adoption |
| C-0B0F1BAB | 38 | $5,494 | 2026-09-23 | Executive touch | Lowest health score (38), no champion, flat usage—urgent exec outreach needed |

**Arithmetic:**
```
$49,707 + $41,235 + $35,748 + $32,621 + $25,365 + $17,602 + $16,829 + $5,494 = $224,601
```

**Total at stake: $224,601**

---

### Play Summary

| Play | Count | Total Eligible |
|------|-------|----------------|
| Executive touch | 3 | $87,822 |
| Commercial concession | 2 | $76,983 |
| Usage revival | 3 | $59,796 |

---

### At-Risk But NOT Qualifying (7)

| Account | Health | Why Disqualified |
|---------|--------|------------------|
| C-0BC71BDD | 55 | Fails R2: eligible_amount = $0 (declining usage, 30% seats) |
| C-0BE96399 | 54 | Fails R2: eligible_amount = $0 (declining usage) |
| C-10A56B0F | 54 | Fails R2: eligible_amount = $0 (declining usage) |
| C-0BA71F12 | 52 | Fails R3: renewal 2027-04-11 is 218 days out (>120) |
| C-0F876796 | 47 | Fails R3: renewal 2027-02-06 is 154 days out (>120) |
| C-0F6694C3 | 43 | Fails R2 & R3: eligible_amount = $0, renewal 197 days out |
| C-0FCCD2DF | 43 | Fails R2 & R3: eligible_amount = $0, renewal 230 days out |
expansion-kitPASS 1.00

? The Question

A customer replied to outreach (reply included). Build the expansion kit: seat coverage (licensed over headcount), two lines on usage health, headroom in seats and ARR at the current per-seat rate, who replied and whether they can buy, the right buyer from our contacts, and a reply email under 150 words that moves toward the right person without being pushy and cites one real usage fact.
cost $0.0404 · 23s · in 10 / out 1,049 tok
✓ coverage_pct✓ headroom_arr✓ right_buyer✓ replier_not_buyer✓ usage_fact✓ email_under_150

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
## Expansion Kit: C-0DDFC9A7

### Seat Coverage
- Licensed: 150 seats
- Headcount: 400
- Coverage: 150 / 400 = **37.5%**

### Usage Health (2 lines)
1. **Strong adoption trend:** MAU grew from 88 → 126 over 6 months (Mar–Aug), adding ~7-8 users/month consistently.
2. **Approaching ceiling:** August's 126 active users = 84% of 150 licensed seats (126/150).

### Headroom
- **Seat headroom:** 400 − 150 = **250 seats** to cover full headcount
- **Per-seat rate:** $9,000 / 150 = **$60/seat/year**
- **ARR headroom:** 250 × $60 = **$15,000** potential expansion

### Who Replied & Can They Buy?
- **Maria S., People Operations Coordinator** — cannot buy. She explicitly states: "I'm not the right person for purchasing decisions here. Budget and seat expansion sit with Dana R."

### Right Buyer
- **Dana R., VP People** — named by Maria as owning budget and seat expansion. Last engaged 2026-05-18 (4 months stale).

---

### Reply Email (143 words)

```
Subject: RE: Growing your team's recognition program

Hi Maria,

Thanks for the kind words — always great to hear the team loves it. And yes, I'd really appreciate an introduction to Dana. No rush on your end.

For context if it's helpful: your August active users hit 126, which puts you at 84% of your current 150-seat license. With the steady growth you've seen, Dana may want to get ahead of that before you bump into the ceiling.

Happy to send over a quick usage summary she can review beforehand, or just jump on a 15-minute call — whatever works best for her calendar.

Thanks again for connecting us.

Best,
Cole
```

---

**Usage fact cited:** 126 active users in August at 84% seat utilization (126/150).
onboarding-status0.75

? 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 $0.0422 · 22s · in 10 / out 1,122 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
# Mid-Onboarding Call Prep: C-0D284E42

**Signup:** 2026-08-11 (25 days ago)

---

## Milestone Status

| Milestone | Status | Evidence |
|-----------|--------|----------|
| Slack integration | ✅ Complete | integration_slack = 2026-08-12 |
| HRIS integration | ❌ Not complete | integration_hris = empty |
| Allowance set | ✅ Complete | allowance_set = 2026-08-13 |
| Admins added | ✅ Complete | admins_added = 2 |
| First recognition | ✅ Complete | first_recognition_at = 2026-08-15 14:22 |
| First redemption | ❌ Not complete | first_redemption_at = empty |

**Complete:** 4 of 6
**Not complete:** HRIS integration, first redemption

---

## Early Engagement Signals

Active givers trend (from onboarding_usage.csv):
- Week 1 avg: (3+3+4+4+5+4+7) ÷ 7 = **4.3 givers/day**
- Week 2 avg: (5+7+6+9+8+9+9) ÷ 7 = **7.6 givers/day**
- Week 3 avg: (9+11+10+10+11+13+11) ÷ 7 = **10.7 givers/day**
- Week 4 (4 days): (13+13+15+15) ÷ 4 = **14.0 givers/day**

**Growth:** 3 → 15 active givers = +400% over 25 days. Steady week-over-week increase with no drop-off. Strong adoption signal.

---

## Three Things to Cover on the Call

1. **HRIS integration** — Not connected. Blocks automated onboarding of new hires and accurate headcount for analytics. Ask what's blocking (IT approval? which HRIS system?).

2. **First redemption** — Zero redemptions after 25 days despite recognition activity. Confirm employees know how to redeem; consider an admin-led "first redemption" prompt or demo.

3. **Celebrate & expand giver base** — 15 active givers is healthy traction. Ask how many total employees; identify departments not yet participating to target next.
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 $0.2516 · 90s · in 10 / out 8,362 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 (Sep 8 – Dec 7, 2026)

## Date Source Decision Rule
ChurnZero is known to be wrong on multi-year contracts. For accounts where is_multi_year=true in Chargebee, I trust Chargebee. For 12-month terms, dates agree on all accounts.

---

## Renewals by Account

### SEPTEMBER (5 accounts, 5 disagreements)

**C-0B7D2C30** | Dana Mercer | $65,901 | 2026-09-15 (Chargebee)
- Seat utilization: 274/476 = 57.6%
- 3mo trend: 97 → 94 → 84 (-13 users, -13.4%)
- **RISK: HIGH** — Sustained 12-month decline (155→84) with below-average seat utilization.
- ⚠️ DATE DISAGREEMENT: CZ=2026-09-10, CB=2026-09-15. Trust CB (36mo multi-year).

**C-0BCDB8C2** | Cole Ingram | $54,427 | 2026-09-18 (Chargebee)
- Seat utilization: 232/424 = 54.7%
- 3mo trend: 127 → 118 → 110 (-17 users, -13.4%)
- **RISK: HIGH** — Steep 12-month decline (200→110, -45%) with half of seats unused.
- ⚠️ DATE DISAGREEMENT: CZ=2027-09-18, CB=2026-09-18 (1 YEAR OFF). Trust CB (36mo multi-year).

**C-0D2AB865** | Elena Sinclair | $38,022 | 2026-09-22 (Chargebee)
- Seat utilization: 250/407 = 61.4%
- 3mo trend: 125 → 117 → 109 (-16 users, -12.8%)
- **RISK: HIGH** — Continuous 12-month decline (199→109, -45%) signals disengagement.
- ⚠️ DATE DISAGREEMENT: CZ=2026-09-10, CB=2026-09-22. Trust CB (24mo multi-year).

**C-0BBE3E60** | Dana Mercer | $30,993 | 2026-09-26 (Chargebee)
- Seat utilization: 74/114 = 64.9%
- 3mo trend: 39 → 35 → 33 (-6 users, -15.4%)
- **RISK: HIGH** — Sharp 15% quarterly decline; half of original users gone (63→33).
- ⚠️ DATE DISAGREEMENT: CZ=2027-09-26, CB=2026-09-26 (1 YEAR OFF). Trust CB (24mo multi-year).

**C-0F5D2323** | Cole Ingram | $90,647 | 2026-09-29 (Chargebee)
- Seat utilization: 111/390 = 28.5%
- 3mo trend: 20 → 21 → 18 (flat, ~19 avg)
- **RISK: HIGH** — Only 28% seat utilization on largest September ARR; chronic low adoption.
- ⚠️ DATE DISAGREEMENT: CZ=2026-09-10, CB=2026-09-29. Trust CB (24mo multi-year).

---

### OCTOBER (8 accounts, no disagreements)

**C-0EC6999D** | Elena Sinclair | $79,419 | 2026-10-03 (agreed)
- Seat utilization: 31/112 = 27.7%
- 3mo trend: 17 → 16 → 15 (-2 users, flat)
- **RISK: HIGH** — Only 28% seat utilization; 2nd-largest October ARR severely under-adopted.

**C-0B20DB64** | Dana Mercer | $21,770 | 2026-10-07 (agreed)
- Seat utilization: 214/378 = 56.6%
- 3mo trend: 294 → 298 → 294 (flat, ±4)
- **RISK: LOW** — Stable usage over 12 months, no warning signs.

**C-0BBC4E7A** | Cole Ingram | $56,374 | 2026-10-10 (agreed)
- Seat utilization: 228/337 = 67.7%
- 3mo trend: 142 → 141 → 139 (-3 users, -2.1%)
- **RISK: LOW** — Healthy utilization with minimal decline.

**C-0FD551AB** | Elena Sinclair | $48,815 | 2026-10-14 (agreed)
- Seat utilization: 210/376 = 55.9%
- 3mo trend: 123 → 122 → 126 (+3 users, +2.4%)
- **RISK: LOW** — Usage ticking up; moderate utilization.

**C-0F9F8F13** | Dana Mercer | $46,230 | 2026-10-18 (agreed)
- Seat utilization: 199/352 = 56.5%
- 3mo trend: 185 → 185 → 182 (-3 users, flat)
- **RISK: LOW** — Stable engagement over 12 months.

**C-0BC34584** | Cole Ingram | $16,740 | 2026-10-22 (agreed)
- Seat utilization: 327/494 = 66.2%
- 3mo trend: 104 → 104 → 106 (+2 users, flat)
- **RISK: LOW** — Stable, above-average utilization.

**C-0B7A7546** | Elena Sinclair | $35,062 | 2026-10-25 (agreed)
- Seat utilization: 182/205 = 88.8%
- 3mo trend: 64 → 65 → 63 (flat)
- **RISK: LOW** — Excellent 89% seat utilization; stable.

**C-0B369871** | Dana Mercer | $85,128 | 2026-10-29 (agreed)
- Seat utilization: 317/422 = 75.1%
- 3mo trend: 326 → 330 → 333 (+7 users, +2.1%)
- **RISK: LOW** — Growing usage, strong utilization. Expansion candidate.

---

### NOVEMBER (7 accounts, no disagreements)

**C-0B144C78** | Cole Ingram | $30,899 | 2026-11-02 (agreed)
- Seat utilization: 169/224 = 75.4%
- 3mo trend: 101 → 101 → 106 (+5 users, +5.0%)
- **RISK: LOW** — Growing, strong utilization.

**C-0FC4DBB8** | Elena Sinclair | $94,732 | 2026-11-05 (agreed)
- Seat utilization: 356/464 = 76.7%
- 3mo trend: 189 → 191 → 193 (+4 users, +2.1%)
- **RISK: LOW** — Largest renewal; healthy growth and utilization.

**C-0D5BBE3A** | Dana Mercer | $39,740 | 2026-11-09 (agreed)
- Seat utilization: 85/102 = 83.3%
- 3mo trend: 88 → 90 → 91 (+3 users, +3.4%)
- **RISK: LOW** — Excellent 83% utilization, growing.

**C-0FB9D5AF** | Cole Ingram | $63,158 | 2026-11-13 (agreed)
- Seat utilization: 144/199 = 72.4%
- 3mo trend: 173 → 173 → 176 (+3 users, +1.7%)
- **RISK: LOW** — Stable, good utilization.

**C-0B344485** | Elena Sinclair | $64,384 | 2026-11-16 (agreed)
- Seat utilization: 224/287 = 78.0%
- 3mo trend: 238 → 240 → 244 (+6 users, +2.5%)
- **RISK: LOW** — Growing, strong utilization.

**C-0CB2C1B4** | Dana Mercer | $40,628 | 2026-11-20 (agreed)
- Seat utilization: 386/473 = 81.6%
- 3mo trend: 47 → 48 → 49 (+2 users, +4.3%)
- **RISK: LOW** — Excellent 82% utilization, growing.

**C-22170CA1** | Cole Ingram | $45,646 | 2026-11-24 (agreed)
- Seat utilization: 251/294 = 85.4%
- 3mo trend: 143 → 148 → 146 (+3 users, +2.1%)
- **RISK: LOW** — Excellent 85% utilization, stable.

---

## Date Disagreement Summary

| Account | ChurnZero | Chargebee | Δ | Trusted | Reason |
|---------|-----------|-----------|---|---------|--------|
| C-0B7D2C30 | 2026-09-10 | 2026-09-15 | 5d | CB | 36mo multi-year |
| C-0BCDB8C2 | 2027-09-18 | 2026-09-18 | **1yr** | CB | 36mo multi-year |
| C-0D2AB865 | 2026-09-10 | 2026-09-22 | 12d | CB | 24mo multi-year |
| C-0BBE3E60 | 2027-09-26 | 2026-09-26 | **1yr** | CB | 24mo multi-year |
| C-0F5D2323 | 2026-09-10 | 2026-09-29 | 19d | CB | 24mo multi-year |

Two accounts (C-0BCDB8C2, C-0BBE3E60) show ChurnZero dates a full year into the future — classic multi-year contract bug.

---

## Summary

**Total ARR Renewing (90 days):** $1,048,715

| Risk Level | Accounts | ARR |
|------------|----------|-----|
| HIGH | 6 | $359,409 |
| LOW | 14 | $689,306 |

**ARR at HIGH Risk: $359,409 (34.3% of renewing ARR)**

High-risk accounts by ARR:
1. C-0F5D2323 — $90,647 (28% utilization)
2. C-0EC6999D — $79,419 (28% utilization)
3. C-0B7D2C30 — $65,901 (declining usage)
4. C-0BCDB8C2 — $54,427 (declining usage)
5. C-0D2AB865 — $38,022 (declining usage)
6. C-0BBE3E60 — $30,993 (declining usage)

All 6 high-risk accounts renew in September/early October. Cole owns 2 ($145K), Elena owns 2 ($117K), Dana owns 2 ($97K).
ticket-theme-synthesis0.80

? The Question

Synthesize the quarter's support tickets into 4 to 7 themes. Do not trust existing tags; read the text. Per theme: name, count, share, distinct accounts, ARR affected, two ticket ids, one-line recommendation. Separate broad patterns from single-account noise and rank by ARR exposure, not volume.
cost $0.2537 · 92s · in 10 / out 8,677 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
## Q2 2026 Support Ticket Themes (80 tickets analyzed)

### BROAD PATTERNS (ranked by ARR exposure)

| # | Theme | Count | Share | Accounts | ARR Affected | Sample IDs | Recommendation |
|---|-------|-------|-------|----------|--------------|------------|----------------|
| 1 | **HRIS Provisioning Failures** | 12 | 15.0% | 3 | $114,000 | IC-460059, IC-460060 | Audit HRIS webhook integration; add sync-failure alerting to surface silent drops. |
| 2 | **Redemption & Gift Card Failures** | 18 | 22.5% | 7 | $68,800 | IC-460025, IC-460035 | Add idempotency + rollback to redemption flow; auto-refund points on failed orders. |
| 3 | **Points Not Posting** | 20 | 25.0% | 9 | $31,100 | IC-460004, IC-460016 | Implement point-ledger reconciliation job; alert on recognition/credit mismatch within 24h. |
| 4 | **Slack Integration Failures** | 14 | 17.5% | 4 | $18,900 | IC-460041, IC-460046 | Add Slack connection health check + auto-reconnect; surface OAuth failures to admin. |

---

### SINGLE-ACCOUNT CONCENTRATION

| # | Theme | Count | Share | Account | ARR Affected | Sample IDs | Recommendation |
|---|-------|-------|-------|---------|--------------|------------|----------------|
| 5 | **Billing/Invoice Errors** | 16 | 20.0% | 1 (C-0E9C27D1) | $52,000 | IC-460071, IC-460069 | Escalate to finance for manual audit of C-0E9C27D1; root-cause seat-count sync failure before renewal. |

---

**Arithmetic:**
- Total tickets: 80
- Broad-pattern ARR: $114K + $68.8K + $31.1K + $18.9K = $232,800 across 23 distinct accounts
- Single-account ARR at risk: $52,000 (C-0E9C27D1 filed 16 tickets about seat-count and tier-pricing errors)

**Key insight:** HRIS provisioning is the #1 ARR exposure ($114K) despite only 12 tickets—driven by 3 enterprise accounts (C-0B2213A9 at $36K, C-0DDFC9A7 at $48K, C-0F6C0F34 at $30K). Points-not-posting has highest volume (20 tickets) but lowest ARR ($31.1K)—mostly SMB accounts under $5K.
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 $0.0599 · 24s · in 10 / out 1,750 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
## Social Proof Ranking for Prospect C-82AF3719

**Prospect profile:** Technology | Mid-Market | employee_recognition | NA-West

---

### Customers with Case Studies Ranked by Similarity

**#1 — C-64171065**
Matches 3 of 4 dimensions:
- ✓ Industry: Technology
- ✓ Size band: Mid-Market
- ✓ Use case: employee_recognition
- ✗ Region: NA-East (prospect is NA-West)

*Strongest match: identical industry + use case + size band.*

---

**#2 — C-A13C193D**
Matches 3 of 4 dimensions:
- ✓ Industry: Technology
- ✓ Size band: Mid-Market
- ✗ Use case: retention (prospect is employee_recognition)
- ✓ Region: NA-West

*Same industry and local region; different use case.*

---

**#3 — C-11C31562**
Matches 3 of 4 dimensions:
- ✗ Industry: Manufacturing (prospect is Technology)
- ✓ Size band: Mid-Market
- ✓ Use case: employee_recognition
- ✓ Region: NA-West

*Same use case and region; different industry.*

---

### Arithmetic
8 customers have `has_case_study=true`. Scored each on 4 binary dimensions vs. prospect. Three customers tied at 3/4 matches; ranked by signal strength (industry + use_case weighted highest for social proof relevance).
channel-attribution-math0.80

? 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 $0.2226 · 80s · in 10 / out 7,687 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 — Trailing 6 Months (Mar–Aug 2026)

### Paid Channels

```
Channel        Spend     SQMs  SQOs  Cost/SQM  Cost/SQO  SQM→SQO   Pipeline    Pipeline/$
─────────────────────────────────────────────────────────────────────────────────────────
paid_search    $36,000    40    18    $900      $2,000     45%     $720,000     $20.00
linkedin_ads   $24,000    25     8    $960      $3,000     32%      $96,000      $4.00
paid_social    $18,000     0     0    UNDEF     UNDEF     UNDEF         $0      UNDEF
webinars        $9,000    12     5    $750      $1,800     42%      $60,000      $6.67
```

Arithmetic:
- paid_search: $6K×6=$36K; 18÷40=45%; $36K÷40=$900; $36K÷18=$2K; $720K÷$36K=$20
- linkedin_ads: $4K×6=$24K; 8÷25=32%; $24K÷25=$960; $24K÷8=$3K; $96K÷$24K=$4
- webinars: $1.5K×6=$9K; 5÷12=42%; $9K÷12=$750; $9K÷5=$1.8K; $60K÷$9K=$6.67

### Organic Channels

```
Channel          Volume (SQMs)   SQOs   SQO Rate   Pipeline
────────────────────────────────────────────────────────────
referral               15          6       40%     $48,000
organic_search         30         10       33%     $90,000
```

Arithmetic:
- referral: 6÷15=40%; 6×$8K=$48K
- organic_search: 10÷30=33%; 10×$9K=$90K

### Flagged Rows (SQO date precedes SQM date)

```
Contact     Channel       SQM Date     SQO Date     Issue
─────────────────────────────────────────────────────────
CT-000044   linkedin_ads  2026-07-23   2026-07-18   SQO 5 days before SQM
CT-000041   linkedin_ads  2026-06-14   2026-06-09   SQO 5 days before SQM
```

Both anomalies are in linkedin_ads. Possible backfill error or stage-skip in CRM.

### Reallocation Recommendation

1. **paid_social: pause or fix tracking.** $18K spent, zero SQMs recorded. Either attribution is broken or channel delivers nothing. Do not reallocate until tracking is verified.

2. **Shift budget toward paid_search.** At $20 pipeline per dollar, it outperforms linkedin_ads by 5× ($4/dollar) and webinars by 3× ($6.67/dollar). If $12K moves from linkedin_ads → paid_search, projected incremental pipeline: $12K × $20 = $240K vs. the $48K it would have generated in linkedin_ads.

3. **Test doubling webinars.** Lowest cost/SQO ($1,800) and strong conversion (42%) but small sample. Add $4.5K/mo (from linkedin_ads or reallocated paid_social) to validate scalability.

### Confidence Assessment

| Channel        | Confidence | Rationale |
|----------------|------------|-----------|
| paid_search    | HIGH       | n=40 SQMs, n=18 SQOs, no data anomalies |
| webinars       | MEDIUM     | n=12 SQMs, n=5 SQOs—directionally reliable but small |
| linkedin_ads   | LOW        | n=25 SQMs, n=8 SQOs, 2 flagged rows (25% of SQOs) suggest data quality issues |
| paid_social    | NONE       | Zero tracked SQMs—cannot evaluate |

**Overall confidence: MEDIUM.** paid_search signal is strong; linkedin_ads and paid_social need data audits before major budget shifts.
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 $0.0912 · 42s · in 10 / out 2,811 tok
✓ competitor_named✓ pricing✓ win_loss✓ unverified_marked✗ citations

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
# Battlecard: Rivally (Updated 2026-09-08)

## One-Line Positioning
Points-based recognition platform targeting mid-market with strong EU/distributed team positioning. [S02, S04, S12]

## Pricing
**Current list: $7/user/month, annual billing required** [S17, 2026-08-12, pricing_page]

Conflict note: Old card stated $5/user/month as of 2026-01. S03 (2026-01-20) and S08 (2026-04-01) confirmed $5. S17 (2026-08-12) shows price increase to $7. Newer source wins.

Deal intel: Prospect reported $7/user/mo list with 15% discount offered for 3-year term [S18].

## Where Rivally Wins
- Fast implementation: setup under a week reported [S04]
- Slack integration works out of box [S04]
- EU data residency now GA [S15]
- Multi-language support praised for distributed EU teams [S12]
- Engaging recognition feed UX [S02, S16]
- Support response time under 4 hours [S22]
- Microsoft Teams app v2 in preview [S19]

## Where We Win
- Analytics depth: prospect chose Bonusly over Rivally citing this [S25]
- Reporting dashboards: Rivally's are "basic compared to enterprise tools" [S07]
- SCIM provisioning: Rivally lacks it, manual user management painful [S10]
- Admin tooling: lags peers [S16], lacks bulk recognition editing [S24]
- Data portability: migration off Rivally hard, exports CSV-only [S20]
- EMEA rewards catalog: thinner than US [S14]

## Objections & Responses
| Objection | Response |
|-----------|----------|
| "Rivally has EU data residency" | True as of 2026-07 [S15]. Confirm our EU offering; compete on analytics depth. |
| "Rivally is cheaper" | List is now $7/user/mo [S17], same ballpark. Emphasize ROI via analytics. |
| "Rivally integrates with Teams" | Their v2 is still preview [S19]. Confirm our Teams integration status. |

## Recent Changes (Last 12 Months)
- 2026-09-01: Rivally Pulse (engagement survey add-on) exits beta [S23]
- 2026-08-20: Microsoft Teams app v2 enters public preview [S19]
- 2026-08-12: Price increase from $5 to $7/user/month [S17]
- 2026-07-01: Dublin office opened; EU data residency GA [S15]
- 2026-05-09: Hired ex-Workday VP EMEA for European expansion [S11]
- 2026-03-05: Launched Rivally Pulse engagement survey add-on [S06]
- 2025-11-04: Series C funding, $40M led by Northgate Ventures [S01]

## 12-Month Win/Loss Record vs. Rivally (Sep 2025 – Aug 2026)

| Month | Wins | Losses | Deals |
|-------|------|--------|-------|
| 2025-09 | 1 (Deal-072E31) | 1 (Deal-7767F5) | 2 |
| 2025-10 | 2 (Deal-A9FD43, Deal-F65C8F) | 0 | 2 |
| 2025-11 | 1 (Deal-7AA785) | 1 (Deal-D263E0) | 2 |
| 2025-12 | 1 (Deal-44C524) | 1 (Deal-935746) | 2 |
| 2026-01 | 2 (Deal-0D0CD6, Deal-E46EAB) | 0 | 2 |
| 2026-02 | 2 (Deal-D5B790, Deal-1D2392) | 0 | 2 |
| 2026-03 | 1 (Deal-5C636E) | 1 (Deal-9066A6) | 2 |
| 2026-04 | 0 | 2 (Deal-5645A5, Deal-72A02F) | 2 |
| 2026-05 | 0 | 1 (Deal-C6FFAA) | 1 |
| 2026-06 | 1 (Deal-67BE14) | 0 | 1 |
| 2026-07 | 1 (Deal-1B6969) | 0 | 1 |
| 2026-08 | 1 (Deal-F03E7B) | 0 | 1 |

**Totals: 14 wins, 6 losses, 70% win rate (14/20)**

Trend: Strong Feb–Mar 2026 (5-1), soft Apr–May (0-3), rebounded Jun–Aug (3-0).

## Unverified / Removed Claims from Old Card
- ❌ "Rivally lacks a Slack integration" — CONTRADICTED by S04 (Slack worked out of box). Removed.
- ❌ "Rivally was acquired by WorkHuman in 2025" — NO SOURCE in provided data. Marked unverified; do not use until sourced.
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 $0.1736 · 49s · in 10 / out 3,445 tok
✓ tracking_error✓ overlap_pair✓ weakest_step✓ fix_priority

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
**SEQUENCE METRICS**

| Sequence | Sent | Open Rate | Reply Rate | Meeting Rate | Weakest Step |
|----------|------|-----------|------------|--------------|--------------|
| New Logo Nurture | 500 | 42% (210/500) | 18% (90/500) | 5.4% (27/500) | Step 3 (4.2% reply) |
| Expansion Nurture | 300 | 43% (130/300) | 19.7% (59/300) | 4% (12/300) | Step 3 (4.4% reply) |
| Cold Outbound - HR Leaders | 600 | 40% (240/600) | **1.3% (8/600)** | **0% (0/600)** | Step 1 (0.83% reply) |
| Cold Outbound - People Ops | 400 | 37.5% (150/400) | 7.25% (29/400) | 1.5% (6/400) | Step 3 (1.6% reply) |

**TRACKING ERROR**

Expansion Nurture Step 2: 340 opens on 300 sent. Impossible. Likely pixel-caching or forwarded-email double-counting. Audit tracking domain or switch to unique-open counting.

**AUDIENCE OVERLAP**

21 contacts in BOTH Cold Outbound - HR Leaders AND Cold Outbound - People Ops (CT-000849, CT-000884, CT-000890, CT-000908, CT-001033, CT-001097, CT-001101, CT-001103, CT-001105, CT-001130, CT-001153, CT-001159, CT-001217, CT-001227, CT-001236, CT-001255, CT-001258, CT-001277, CT-001285, CT-001311, CT-001345). Same contacts getting both sequences simultaneously—burning trust and inflating unsubscribes.

2 contacts in BOTH Expansion Nurture AND New Logo Nurture (CT-000301, CT-000624).

**FAILURE MODE: COLD OUTBOUND - HR LEADERS (<2% REPLY)**

Opens are fine (40%), but 1.3% reply and zero meetings = message-market mismatch. HR Leaders are opening out of curiosity but the value prop doesn't speak to their pain. Likely too generic or wrong seniority assumption. Step 1 sets the ceiling—subsequent steps can't recover from a miss at entry.

**ONE CHANGE PER WEAK SEQUENCE**

- **HR Leaders**: Rewrite step 1 body to lead with a pain-specific hook (e.g., turnover cost at their company size), not product features.
- **People Ops**: Compress step 3 to a 2-sentence breakup email—current copy is losing people who already said no twice.
- **Expansion Nurture**: Fix tracking before optimizing; data is untrustworthy.
- **New Logo Nurture**: Add social proof to step 3 (case study link).

**FIX FIRST**: Cold Outbound - HR Leaders. Largest volume (600), worst reply (1.3%), zero pipeline. Every day it runs is burned budget and list fatigue.
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 $0.0637 · 34s · in 10 / out 2,011 tok
✓ metrics_present✓ deltas✓ coverage_ratio✓ pace✓ paragraph

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
## Q3-2026 Marketing Goals Update
**Week ending Sep 8 · 66 of 92 days elapsed (71.7%)**

| Metric | QTD Actual | Target | Delta | Pace |
|--------|-----------|--------|-------|------|
| SQMs | 230 | 300 | -70 | **AHEAD** (expect 215 at 71.7%) |
| SQOs | 84 | 120 | -36 | BEHIND (expect 86 at 71.7%) |
| DS2s | 40 | 75 | -35 | **BEHIND** (expect 54 at 71.7%) |
| Closed-Lost MIA Rate | 20% | ≤10% | +10 pts | **BEHIND** (2× target rate) |
| Same-Quarter Closes | 10 | 20 | -10 | BEHIND (expect 14 at 71.7%) |
| Active Pipeline | $3.0M | $4.0M | -$1.0M | BEHIND (75% of coverage target) |

**Arithmetic:**
- MIA rate: 5 MIA ÷ 25 closed-lost = 20%
- Pace expectations: target × (66 ÷ 92)
- SQMs: 300 × 0.717 = 215 → 230 actual = +7% ahead
- SQOs: 120 × 0.717 = 86 → 84 actual = 2% behind
- DS2s: 75 × 0.717 = 54 → 40 actual = 26% behind
- Same-quarter closes: 20 × 0.717 = 14 → 10 actual = 30% behind

**What moved this week:** Data does not include week-over-week deltas, so I cannot isolate this week's contribution. Observable pattern: top-of-funnel (SQMs) is healthy at 107% of pace, but conversion falters downstream—SQM→SQO rate is 37% (84/230) and SQO→DS2 rate is 48% (40/84), leaving DS2s and same-quarter closes materially behind. The 20% MIA rate on closed-lost suggests deals are going dark rather than losing to competition; tightening follow-up cadence or earlier disqualification could help. Pipeline coverage gap ($1M) needs either more qualified opps entering or acceleration of existing mid-stage deals.
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 $0.0390 · 20s · in 10 / out 946 tok
✓ max_3_sentences✓ under_80_words✓ weighted_present✓ caveat_present✓ no_em_dash

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
Q3 weighted forecast is $116K ($44,729 COMMIT + 35% of $203,565 BEST_CASE). All 7 COMMIT deals have empty why-buys, so the $44,729 floor is undocumented. Fix why-buys on COMMIT before treating this number as reliable.
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 $0.0298 · 16s · in 10 / out 624 tok
✓ word_cap✓ references_touch✓ single_ask

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
**Subject:** Following up on the July 28 demo

Hi [VP People],

Wanted to circle back—I know things get busy. During our July 28 demo, your team had a strong reaction to the automated milestone awards and the points catalog, and you asked for pricing, which I sent over on August 5.

I haven't heard back, so I wanted to check: did the pricing land in the right range, or are there questions I can help answer?

Happy to jump on a quick call this week if that's easier.

Best,
Alex

---

**Word count:** 79

**Sources cited:**
- ENG-00002 (July 28 demo, strong reaction to automated milestone awards and points catalog, asked for pricing)
- ENG-00001 (August 5 pricing email, no reply)
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 $0.0300 · 18s · in 10 / out 669 tok
✓ four_paragraphs✓ word_cap✓ teams

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
**Marketing**
The team delivered 46 SQMs against a 52-week target—88% to goal, with room to close the gap. Webinars carried the week, contributing 18 SQMs (39% of total volume). Paid search efficiency held steady at $150 per SQM, keeping acquisition costs predictable as we head into September.

**Sales**
Strong week on the board: 14 SQOs converted, 9 DS2 meetings locked in, and $310K in new pipeline created. Three deals closed same-quarter, proving the team can move opportunities through the funnel fast when the fit is right. Pipeline health looks solid heading into the holiday-shortened stretch.

**CS**
The team saved 2 renewals this week—each one a relationship preserved and revenue protected. NPS landed at 61, a healthy signal that customers feel supported. Three red-flag accounts remain open going into next week; the team has eyes on them and plans in place.

**PLG**
Self-serve momentum continues: 412 new signups joined the platform, with 31% activating. Even better, 38 companies hit the aha moment of 10 recognition gives—the point where teams start feeling the Bonusly magic. That's 38 organizations one step closer to seeing how recognition builds culture.

---
*Word count: 197. All figures sourced from week_extracts.md.*
partner-digest-sectionPASS 1.00

? The Question

Write the partner digest for the period. Every partner in the roster appears once: active plays get status with numbers, quiet partners get one line. Partner-sourced pipeline count and amount must match the deal data. Nothing omitted, nothing added.
cost $0.0343 · 14s · in 10 / out 785 tok
✓ all_partners✓ pipeline_amount✓ quiet_one_line

✓ Correct Answer (ground truth)

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

Model's actual answer

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

## Summary
Partner-sourced pipeline: **5 deals, $550,000**

Arithmetic: $180,000 + $95,000 + $140,000 + $60,000 + $75,000 = $550,000

---

## Active Partners

**Apex Rewards Co**
2 sourced opps (Deal-DDAAF2, Deal-2CF33E), both DS1. Pipeline: $275,000. Co-webinar locked for 09-15.

**HRCloud Partners**
1 sourced opp (Deal-F1CDA5), moved to DS2 after security review. Pipeline: $140,000.

**CultureBridge**
2 sourced opps (Deal-096E1D, Deal-067213) from lunch-and-learn, early stage. Pipeline: $135,000.

**WorkWell Group**
0 sourced deals this period. Q4 joint playbook restart planned; call booked 09-09.

---

## Quiet Partners

**Recogniq** — No activity this period.
**KudosWave** — No activity this period.
**PeopleFirst Advisors** — No activity this period.
**TotalPerk** — No activity this period.
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 $0.0371 · 15s · in 10 / out 895 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
## Security Questionnaire Response

| Item | Answer | Source |
|------|--------|--------|
| Q1 | Yes. AES-256 encryption at rest. | P1 |
| Q2 | Yes. TLS 1.2 or higher. | P1 |
| Q3 | Yes, SOC 2 Type II, renewed annually. Available under NDA. | P2 |
| Q4 | Contract duration + 90 days, then deleted from production. | P3 |
| Q5 | Published at trust.bonusly.com. 30 days notice before new subprocessor. | P4 |
| Q6 | Yes. SAML 2.0 SSO available on Enterprise plan. | P5 |
| Q7 | Daily backups, encrypted, retained 35 days. | P6 |
| Q8 | **Unanswerable.** Excerpts do not address ISO 27001. Need: certifications page or compliance documentation. | — |
| Q9 | **Unanswerable.** Excerpts do not include SLA terms. Need: Master Service Agreement or SLA addendum. | — |
| Q10 | **Unanswerable.** Excerpts do not address HIPAA. Need: HIPAA compliance statement or legal policy. | — |
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 $0.6472 · 109s · in 10 / out 6,473 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
# Skill Manifest Reconciliation Report

## Summary

14 manifest rows, 14 skill files. No manifest drift. 0 descriptions exceed 1,024 chars (max 1,006).

---

## Findings

### 1. ALWAYS-trigger overlap: comms-drafter ↔ email-drafter
**Severity:** CRITICAL
**Action:** MERGE

Verbatim duplicate triggers between skills:
- "write me an email"
- "draft a follow-up"
- "what should I say"
- "bump email"
- "contract nudge"
- "help me reply" / "help me reply to this"

comms-drafter has broader scope (all teams: AEs, SDRs, CSMs, partnerships, rewards, ops, Intercom). email-drafter is AE/SDR/CSM only. Merge email-drafter content into comms-drafter and delete email-drafter.

---

### 2. ALWAYS-trigger overlap: weekly-pipeline-report ↔ pipeline-intelligence-report
**Severity:** WARNING
**Action:** TRIM_DESC

Both trigger on variants of "pipeline report", "pipeline update". Collision risk when user says "run the pipeline report."

Trim weekly-pipeline-report description to emphasize specificity: "weekly", "Ben Lavin", "demand generation", "mid-month". Pipeline-intelligence-report owns "full scored pipeline", "tiers", "10-tab".

---

### 3. Circular reference: deal-strategy-coach ↔ email-drafter
**Severity:** INFO
**Action:** REVIEW

- deal-strategy-coach delegates to email-drafter for manager-to-prospect emails
- email-drafter lane-marks back to deal-strategy-coach for strategy

Not a true infinite loop (email-drafter reference is a lane marker, not a delegation). Pattern is intentional but documentation could be clearer. One-way delegation from deal-strategy-coach → email-drafter only; email-drafter's reference is advisory.

---

### 4. Dangling delegation targets
**Severity:** WARNING
**Action:** REVIEW

Skills referenced but not in manifest:

| Target | Referenced by |
|--------|---------------|
| bonusly-brand | comms-drafter, email-drafter, sales-forecast, signalforge-claim-compressor |
| prospect-research-multithreading | comms-drafter, deal-strategy-coach, email-drafter |
| skill-orchestrator | signalforge-feedback |
| signalforge-reports (org skill) | pipeline-intelligence-report, weekly-pipeline-report, sales-forecast |
| 8 bonusly-* specialist skills | analysis-validator (§12.4) |

Verify these exist as org skills, plugin skills, or create them. If org skills, no action. If missing, either create or remove references.

---

### 5. Version conflict: analysis-validator
**Severity:** INFO
**Action:** None

v3.5 and v3.6 both dated May 9, 2026. v3.6 is current version in file header. Changelog shows v3.6 added G2-F after v3.5 added G1-L. v3.6 survives. Already resolved.

---

### 6. Descriptions exceeding 1,024 characters
**Severity:** INFO
**Action:** None

Count: **0**

Maximum: 1,006 chars (pipeline-intelligence-report, signalforge-claim-compressor)

---

### 7. Hardcoded values in skill bodies
**Severity:** WARNING
**Action:** UPDATE_BODY

| Category | Values | Skills affected |
|----------|--------|-----------------|
| Stage IDs | 150582536, 150582537, 150582538, 150582539, 1175632767 | analysis-validator, closed-lost-analysis, deal-strategy-coach, next-to-close, pipeline-intelligence-report, stale-pipeline-report |
| Owner IDs | 119337721, 77260721, 83155923, 84342457, 83155924, 1520255671 | analysis-validator (§12.3), deal-strategy-coach, pipeline-intelligence-report |
| Person names | Bryce Harmon, Dana Mercer, Cole Ingram, Alex Franklin, Gavin Porter, Alaina Loori, Ben Lavin, Amani Phipps, Manish, +15 others | analysis-validator, deal-strategy-coach, pipeline-intelligence-report, partner-digest, weekly-pipeline-report, sales-forecast |
| Confluence IDs | 73fe98de-a4a3-4869-9f8a-bb1eeed4cf7f (cloud), 2286616609, 2232811524, 2295136266, 2234417154, 2247295002, 2232582148, 2286321666 | partner-digest, sales-forecast, signalforge-feedback |
| Slack IDs | C0561C1JCPJ, U03QLMBL7AR | partner-digest, stale-pipeline-report |
| Spreadsheet IDs | 1CLZeOsElVDF_LF0ZG_t2nfwvhnZ6bpwqM_nX3WEYzcw, 1ENuaEcCuLjdKhMvp8FK3Ys1ek5Aw9ZuOZhsHJJFoB_k | weekly-pipeline-report |
| HubSpot org ID | 1973303 | next-to-close, pipeline-intelligence-report, stale-pipeline-report |
| Pricing tables | 2026 per-tier pricing | deal-strategy-coach |
| Q1 2026 actuals | $365,152 vs $475,000, $2,490,532 vs $3,288,000 | weekly-pipeline-report |
| Population anchors | ~452K provisioned, ~110K dormant | analysis-validator |

Recommendation: Centralize constants (stage IDs, owner roster, Confluence/Slack IDs) into a shared reference skill or config, then reference dynamically. Owner roster and pricing tables are highest drift risk.

---

### 8. Manifest drift
**Severity:** INFO
**Action:** None

- Files with no manifest row: 0
- Manifest rows with no file: 0

Exact 14:14 match. No drift.

---

## Action Summary

| # | Severity | Action | Target | Description |
|---|----------|--------|--------|-------------|
| 1 | CRITICAL | MERGE | email-drafter → comms-drafter | Consolidate overlapping triggers |
| 2 | WARNING | TRIM_DESC | weekly-pipeline-report | Narrow trigger phrases to avoid collision with pipeline-intelligence-report |
| 3 | INFO | REVIEW | deal-strategy-coach ↔ email-drafter | Clarify one-way delegation pattern |
| 4 | WARNING | REVIEW | 4 dangling targets | Verify org/plugin existence or remove references |
| 5 | INFO | None | analysis-validator | v3.6 survives, already current |
| 6 | INFO | None | descriptions | 0 exceed 1,024 chars |
| 7 | WARNING | UPDATE_BODY | 8+ skills | Centralize hardcoded IDs, names, pricing, dates |
| 8 | INFO | None | manifest drift | No drift detected |
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 $0.0501 · 24s · in 10 / out 1,373 tok
✓ ordered_steps✓ rollback✓ needs_confirmation✓ sourced

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
# Runbook: Reward Queue Backlog (Sidekiq)

**Source:** #incident-2026-08-29-reward-queue
**Trigger:** PagerDuty alert for reward-worker queue depth > 10k

---

## Step 1: Assess queue depth
| | |
|---|---|
| **Action** | `bundle exec rake sidekiq:queue_depth` |
| **Who** | Farid Osman |
| **Verification** | Output shows current pending job count (was 48,213; normal < 500) |
| **Rollback** | N/A — read-only |
| **Thread ref** | M02 |

---

## Step 2: Check dead set for root cause
| | |
|---|---|
| **Action** | Inspect Sidekiq dead set for error patterns |
| **Who** | Farid Osman |
| **Verification** | Found 112 jobs, all Redis::TimeoutError from ~13:58 |
| **Rollback** | N/A — read-only |
| **Thread ref** | M03 |

---

## Step 3: Pause enqueue to stop inflow
| | |
|---|---|
| **Action** | `bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'` |
| **Who** | Farid Osman |
| **Verification** | Thread does not specify how success was verified — **NEEDS CONFIRMATION** |
| **Rollback** | `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'` |
| **Thread ref** | M04 |

---

## Step 4: Clear dead set
| | |
|---|---|
| **Action** | Cleared dead set via Rails console — **exact command not provided, NEEDS CONFIRMATION** |
| **Who** | Elena Sinclair |
| **Verification** | Not specified in thread — **NEEDS CONFIRMATION** |
| **Rollback** | Not provided — dead jobs are not recoverable once cleared |
| **Thread ref** | M05 |

---

## Step 5: Scale up workers to drain backlog
| | |
|---|---|
| **Action** | `kubectl scale deployment/reward-worker --replicas=6` |
| **Who** | Bryce Harmon |
| **Verification** | Queue depth falling ~1,200/min (M07, confirmed by Farid) |
| **Rollback** | `kubectl scale deployment/reward-worker --replicas=3` |
| **Thread ref** | M06 |

---

## Step 6: Verify queue drained
| | |
|---|---|
| **Action** | `bundle exec rake sidekiq:queue_depth` + check Datadog error rate |
| **Who** | Cole Ingram |
| **Verification** | Queue depth = 0; Datadog error rate at baseline |
| **Rollback** | N/A — read-only |
| **Thread ref** | M08 |

---

## Step 7: Re-enable enqueue
| | |
|---|---|
| **Action** | `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'` |
| **Who** | Bryce Harmon |
| **Verification** | 40 new jobs processed cleanly within 3 minutes |
| **Rollback** | `bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'` |
| **Thread ref** | M09 |

---

## Step 8: Scale workers back to normal
| | |
|---|---|
| **Action** | `kubectl scale deployment/reward-worker --replicas=3` |
| **Who** | Bryce Harmon |
| **Verification** | Queue stable at 0 |
| **Rollback** | `kubectl scale deployment/reward-worker --replicas=6` |
| **Thread ref** | M10 |

---

## Items needing confirmation

1. **Step 4 command** — Elena stated "cleared out the dead set" but did not provide the exact console command.
2. **Step 3 verification** — No explicit confirmation that the feature flag disable took effect.
3. **Step 4 verification** — No explicit confirmation that the dead set was empty after clearing.
datadog-incident-timelinePASS 1.00

? The Question

From the log slice, sequence the failure: first error with timestamp, the cascade in order, the service and job involved, and the Datadog query that would confirm the first error. State what the logs do not show.
cost $0.0552 · 25s · in 10 / out 1,310 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
## Failure Sequence Analysis

### 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 (chronological order)

| Timestamp | Service | Event |
|-----------|---------|-------|
| 14:01:12Z | reward-service | Redis connection timeout (ROOT) |
| 14:01:20Z | reward-service | Retry exhausted for RewardGiveJob |
| 14:01:40Z | sidekiq | RewardGiveJob failed; queued retry |
| 14:02:30Z | sidekiq | Queue depth exceeds 10,000 (backpressure) |
| 14:03:05Z | api-gateway | 502 upstream timeout calling reward-service /gives |
| 14:03:30Z | web-app | Give form submission failed (user-facing impact) |

Pattern continues until 14:22:10Z when Redis connection restored; queue clears by 14:24:45Z.

---

### Service & Job Involved
- **Service:** reward-service
- **Primary Job:** RewardGiveJob (12 failures in sidekiq_jobs.csv)
- **Collateral Job:** RecognitionDigestJob (4 failures, first at 14:02:36Z)

---

### Datadog Query to Confirm First Error

```
service:reward-service status:error "Redis::TimeoutError" 
| sort timestamp asc 
| limit 1
```

Or using Datadog's syntax:
```
@service:reward-service @level:ERROR @message:*redis-primary:6379* 
```

---

### What the Logs Do NOT Show

1. **Redis-side logs** — no entries from redis-primary; root cause unknown (OOM, network partition, crash, slow commands)
2. **Why Redis timed out** — no memory/CPU metrics, no slow-log, no eviction warnings
3. **Recovery action** — logs show "connection restored" but not whether this was auto-reconnect or manual intervention
4. **True job failure count** — sidekiq_jobs.csv shows 16 jobs, but queue depth hit 10,000+; the CSV is a sample, not exhaustive
5. **User impact scope** — no request counts, affected customer IDs, or error rates
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 $0.0407 · 15s · in 10 / out 1,022 tok
✓ orphans✓ no_targets✓ counts_match✓ no_invented

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
## Feature Flag Summary

### Flags with Code References

| Flag | State | What It Controls | Targeting | Companies |
|------|-------|------------------|-----------|-----------|
| recognition_streaks_v2 | ON | Enables StreakTracker.record(give) when recognition is given | segment:beta_companies | 42 |
| points_budget_guardrails | ON | Enforces budget limits via BudgetService.enforce!(giver, points) | all_companies | 220 |
| slack_dm_nudges | ON | Sends Slack DM nudges to users (SlackDm.send_nudge) | segment:region_na | 87 |
| redeem_flow_redesign | OFF | Switches redemption UI from RedeemV1Component to RedeemV2Component | targeted_list | 12 |
| analytics_dashboard_v3 | ON | Loads new analytics dashboard (AnalyticsV3) | segment:tier_three | 65 |
| ms_teams_app_v2 | OFF | Installs Teams app via TeamsAppV2.install | targeted_list | 9 |

### Flags with NO Code Reference (3)
- **legacy_give_modal** — OFF, segment:legacy_plan, 14 companies. No usage found in code excerpt.
- **survey_boosters_q3** — ON, segment:legacy_plan, 7 companies. No usage found in code excerpt.
- **paused_offboard_cleanup** — OFF, no targeting rules, 0 companies. No usage found in code excerpt.

### Flags with NO Targets (1)
- **paused_offboard_cleanup** — OFF, empty targeting_rules, 0 companies. Appears dormant.

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**Company count arithmetic check:**
42 + 220 + 87 + 12 + 65 + 9 + 14 + 7 + 0 = 456 total flag-company assignments (not unique companies; some may overlap across segments).
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 $0.0371 · 18s · in 10 / out 878 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 approval)**
Clean mutual NDA: standard carve-outs present, Delaware law, no embedded covenants. No issues.

**NDA-2 — YELLOW (counsel review)**
Embedded non-solicit: "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." — 18-month tail restricts Bonusly hiring; counsel should confirm acceptable scope.

**NDA-3 — RED (full legal review)**
Three compounding issues:
1. Non-compete: "Recipient shall not, directly or indirectly, engage in or provide services to any business that competes with Discloser's business" for 3 years — could arguably bar Bonusly from operating its own business.
2. One-way confidentiality favoring [PARTY C]; Bonusly receives no protection.
3. Missing carve-outs: "No confidentiality carve-outs are stated in this Agreement" — no safe harbors for public info, prior knowledge, or legally compelled disclosure.
4. Governing law: "laws of the Republic of Ireland… exclusive jurisdiction of its courts" — unusual venue, litigation exposure abroad.