home · ← back to all models
minimax

minimax/minimax-m2.5

Mean 0.923 · 28/40 perfect tests · $0.44 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 $0.0135 · 202s · in 12,447 / out 9,030 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:**
- *DS2+* = deals in stage DS2, DS3, DS4, or DS5
- *Substantive why_buys* = 15+ characters of real text, OR LONG(n) marker indicating truncated substantive text
- *DS1 timestamp* = a non-empty t_ds1 field
- *Regressed* = a deal where an earlier-stage timestamp is later than a later-stage timestamp (t_dsN > t_ds(N+1) for any N)

---

**Why-Buys Compliance (DS2+ Deals):**

| Owner | DS2+ Deals | Compliant | Non-Compliant | Non-Compliant % |
|-------|------------|-----------|---------------|-----------------|
| Bryce Harmon (119337721) | 36 | 3 | 33 | 91.7% |
| Alex Franklin (84342457) | 39 | 8 | 31 | 79.5% |
| Dana Mercer (83155923) | 13 | 2 | 11 | 84.6% |
| Cole Ingram (83155924) | 12 | 5 | 7 | 58.3% |
| **Overall** | **100** | **18** | **82** | **82.0%** |

*Note: owner 716654662 (Farid Osman) has 0 DS2+ deals.*

---

**Other Hygiene Metrics:**
- **Deals with no DS1 timestamp:** 0
- **Deals that regressed:** 0

---

**Three Process Fixes:**
1. **Mandatory why_buys entry at DS2 transition** — block stage advancement unless why_buys field contains 15+ characters or is flagged LONG; auto-notify rep on empty submission.
2. **Weekly why_buys audit report** — automate a dashboard highlighting DS2+ deals with missing/placeholder why_buys, bucketed by owner, to surface hygiene gaps before deals stall.
3. **Stage-timestamp validation rule** — enforce at-time-of-transition that t_ds(N+1) ≥ t_dsN; reject saves that create regressions and require correction before proceeding.
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 $0.0112 · 1823s · in 29,965 / out 1,475 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
⚠ Stream stalled mid tool-call (execute_code); the action was not executed. Ask me to retry if you want to continue.**RESULTS**

**Number of deals resolved:** 156

**Deals whose owner_id has no match in the owners table:** NONE — all 156 owner_ids matched to a row in the owners table.

**Owner_ids that map to archived/deactivated owners:** NONE — all resolved owners (119337721, 83155923, 84342457, 83155924, 716654662, 701163055) have `archived=false` in the owners table. The two archived owners (Gavin Porter ID 1520255671, Hugo Lindqvist ID 77260721) have no deals in this extract.

**Total pipeline amount per resolved owner:**

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

**Combined pipeline total:** $2,314,044.43
gong-hubspot-join-integrity0.75

? The Question

Check the integrity of the call-to-deal mapping below. The gong table lists calls in the last 90 days keyed to HubSpot deal ids; deals_open lists the current open default-pipeline deals. Report: (a) count of gong rows whose deal id has NO match in deals_open (orphans; list a few sample aliases), (b) duplicate conversation keys (rows where calls_90d exceeds distinct_conversation_keys), (c) among the open deals that ARE at stage DS3 or later, the share that have at least one logged call. Give sample aliases for the orphan finding, all taken from the data. Note: gong_owner_id is a Gong user id, not a HubSpot owner id.
cost $0.0654 · 1890s · in 144,333 / out 20,856 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
**Count: 30** (gong rows whose deal ID has NO match in deals_open)

Sample aliases:
- Deal-B038F0
- Deal-9A43B4
- Deal-D84A2D
- Deal-51EA1A
- Deal-36EA09
- Deal-76821A
- Deal-344163
- Deal-3B6668
- Deal-5592CC
- Deal-8FA85D

*These are deals that exist in Gong (have recorded calls in last 90 days) but are NOT in the current open default-pipeline. Likely closed-won, closed-lost, or moved to another pipeline.*

---

### (b) Duplicate Conversation Keys
**Count: 0**

No rows have `calls_90d > distinct_conversation_keys`. The data is consistent — every call maps to a unique conversation key.

---

### (c) DS3+ Open Deals with Logged Calls
- **Total DS3+ deals (DS3, DS4, DS5):** 85
- **With at least one logged call:** 25
- **Share:** **29.4%**

*Among deals advanced to stage DS3 or later in the current open pipeline, fewer than a third have any Gong-recorded calls in the past 90 days.*
snowflake-sql-trapped-catalog0.67

? 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 $0.0018 · 20s · in 1,038 / out 996 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
Looking at the catalog, I can identify:

1. **Customer companies**: From `OBJECTS_DEALS` (HubSpot deals → companies via associations)
2. **First calendar month as a customer**: From `PROPERTY_CLOSEDATE` on deals
3. **Recognition count**: Requires a recognitions/giving table — **not present in this catalog**
4. **Successful redemption count**: From `PRODUCTION.DEPRECATED_RECOGNITION.REDEMPTION_RECORDS_V2` with `STATE = 'succeeded'`

The catalog references "deleted givers" but provides **no recognitions or givers table** in this excerpt. I cannot compute "unique givers" or "recognition count" without that data source.

**The catalog is insufficient** for the requested query. I need one of:

- A recognitions/giving events table (the "deleted givers" filter implies one exists)
- Or clarification on which table provides giver/recognition data

Would you like me to:
1. **Query using only available tables** (deals → redemption records, producing company-level redemption metrics per first month, but skipping givers/recognition)?
2. **Check for other tables** in the warehouse that might contain recognitions data?
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 $0.0419 · 1434s · in 59,663 / out 9,885 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
⚠ Stream stalled mid tool-call (execute_code); the action was not executed. Ask me to retry if you want to continue.============================================================
CRM EXTRACT AUDIT REPORT
============================================================

### COMPLETENESS PERCENTAGE

**COMPANIES (34 records):**
- industry: 34/34 = 100.0%
- employee_count: 25/34 = 73.5%
- hq_country: 28/34 = 82.4%

**CONTACTS (52 records):**
- email: 52/52 = 100.0%
- title: 40/52 = 76.9%
- persona: 37/52 = 71.2%

**DEALS (156 records):**
- owner_id: 156/156 = 100.0%
- stage: 156/156 = 100.0%
- amount: 156/156 = 100.0%
- close_date: 156/156 = 100.0%
- why_buys: 23/156 = 14.7%

---

### DUPLICATE COMPANY CLUSTERS

**Cluster 1: acme-corp.com**
- C-0A092931: industry='Technology', employees='500', country='US'
- C-0A092932: industry='tech', employees='510', country='USA'
- **SURVIVOR: C-0A092931** (most complete)

**Cluster 2: globex.io**
- C-0A092933: industry='SaaS', employees='200', country='US'
- C-0A092934: industry='Technology', employees='200', country='US'
- **SURVIVOR: C-0A092933** (first entry)

---

### INVALID EMAILS

| Contact Key | Email | Company |
|-------------|-------|---------|
| CT-0010 | user0@ (incomplete) | C-66D1FC |
| CT-0080 | user0@ (incomplete) | C-92D97D |
| CT-0081 | user1@ (incomplete) | C-92D97D |
| CT-0192 | user2@ (incomplete) | C-425E2A |

---

### DOMAIN MISMATCHES

| Contact Key | Email | Listed Domain | Issue |
|-------------|-------|---------------|-------|
| CT-0011 | user1@other-domain.com | 66d1fc.com | **MISMATCH** - email domain does not match company domain |

---

### CRM vs ENRICHMENT DISCREPANCIES (21 total)

| Company | Field | CRM Value | Enrichment Value | Recommendation |
|---------|-------|-----------|------------------|----------------|
| C-66D1FC | industry | tech | Computer Software | Use enrichment |
| C-66D1FC | hq_country | US | United States | Use enrichment |
| C-B25F40 | employee_count | 50 | 120 | Use enrichment (conflicts) |
| C-B25F40 | industry | Tech | Computer Software | Use enrichment |
| C-425E2A | industry | Tech | Computer Software | Use enrichment |

**General recommendation:**
- Fill blank employee_count/hq_country from enrichment where available
- Standardize hq_country to full names ("United States" not "US"/"USA")
- Use enrichment "Computer Software" over vague "Technology"/"Tech"

---

### TOP 10 FIXES BY PIPELINE AT STAKE

| # | Object | Field | Identifier | Pipeline |
|---|--------|-------|------------|----------|
| 1 | COMPANY | hq_country | C-2D1F1B | $240,000 |
| 2 | CONTACT | title | CT-0000 at C-2D1F1B | $240,000 |
| 3 | CONTACT | persona | CT-0000 at C-2D1F1B | $240,000 |
| 4 | DEAL | why_buys | Deal-2D1F1B | $240,000 |
| 5 | DEAL | why_buys | Deal-66D1FC | $99,000 |
| 6 | CONTACT | title | CT-0022 at C-C6FE92 | $72,000 |
| 7 | CONTACT | persona | CT-0022 at C-C6FE92 | $72,000 |
| 8 | DEAL | why_buys | Deal-C6FE92 | $72,000 |
| 9 | DEAL | why_buys | Deal-950043 | $70,000 |
| 10 | COMPANY | hq_country | C-D73B89 | $63,600 |

**Total pipeline at stake in top 10 fixes: $1,168,600**
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 $0.0245 · 1822s · in 27,368 / out 12,614 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
## Lost Deal Classification Summary (90 deals, Aug-Jul 2026)

### Category Counts

| Category | Count | % of Total |
|----------|-------|-------------|
| no decision | 29 | 32% |
| competitor | 23 | 26% |
| timing | 19 | 21% |
| product gap | 8 | 9% |
| champion left | 6 | 7% |
| pricing | 4 | 4% |
| other | 1 | 1% |

**Arithmetic:**
- 29 + 23 + 19 + 8 + 6 + 4 + 1 = **90 deals**

### Side Split

| Side | Count | % of Total |
|------|-------|-------------|
| buyer | 46 | 51% |
| unknown | 44 | 49% |

**Arithmetic:**
- 46 + 44 = **90 deals**
- 46/90 = 51.1%

### Tag vs Free-Text Disagreements: 10

These are deals where the `closed_lost_tag` clearly disagrees with the structured `closed_lost_reason_text`:

| Deal Alias | Issue |
|------------|-------|
| Deal-DB0AAC | Tag "Timing" but text = "putting this on pause" = no specific timeline |
| Deal-831B7B | Tag "Timing" but text = "circle back then" = vague |
| Deal-DDAB52 | Tag "Competitor" but text = "offers a lot more at same cost" = pricing argument |
| Deal-BB78F3 | Tag "Timing" but text mentions specific survey action items |
| Deal-1BCA50 | Tag "Competitor" but text = "mostly about budget" = pricing |
| Deal-9F176A | Tag "Timing" but text = "pause" = no timeline given |
| Deal-69CF3D | Tag "Timing" but text = "On Hold" = no timeline |
| Deal-ECBF89 | Tag "Timing" but text = "On Hold for now" = no timeline |
| Deal-5AD03E | Tag "Competitor" but text = "Wanted more defined budget access" = budget |
| Deal-55867E | Tag "Timing" but text = "not be moving forward" = no decision |

### Two Patterns Most Worth Acting On

**1. MIA/Unresponsive Deals (22 of 90 = 24%)**
- These are classified as "no decision" where prospect went dark
- Revenue at risk: ~$395K
- **Action:** Implement earlier intervention protocols, automated multi-touch sequences, and SDR handoff before deals go cold

**2. Competitor Losses with Specific Vendors (23 deals)**
- Specific competitors named: ADP (PEO partnership), Awardco, HeyTaco, HiThrive, Kudos, Motivosity, Rippl, WorkTango, Paylocity
- Revenue at risk: ~$406K
- **Action:** Build competitive battle cards addressing:
  - ADP TotalSource partnershipdifferentiation
  - Budget-friendly pricing vs Rippl's "same cost but no exchange rate hassle"
  - Points currency digitization (multiple deals cited this)
pipeline-tieringPASS 1.00

? The Question

Score the open pipeline below on these signals: forecast category, stage, meetings_30d, contact count, and engagement recency. Assign exactly one tier per deal: LOCK, ACTION, BUILD, REVIVE, WATCH, or RISKY (RISKY = the forecast category disagrees with the engagement evidence). Never tier a deal LOCK if it has zero meetings_30d. Do NOT list every deal. Return ONLY a compact JSON object with exactly these keys: - "tier_counts": object mapping each tier to the number of deals in it (must sum to the total deal count), - "tier_examples": object mapping each tier to up to 3 example deal aliases from the data, - "risky_deals": array of the deal aliases you tiered RISKY, - "lock_violations": number of deals tiered LOCK that have zero meetings_30d (should be 0), - "pipeline_shape": one short paragraph on the overall pipeline shape. Note: inbound_emails_30d is 0 for every row due to a data defect, so treat meetings_30d as the inbound signal.
cost $0.0474 · 1823s · in 89,290 / out 13,187 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
{
  "tier_counts": {
    "LOCK": 2,
    "ACTION": 7,
    "BUILD": 11,
    "REVIVE": 1,
    "WATCH": 129,
    "RISKY": 6
  },
  "tier_examples": {
    "LOCK": ["Deal-C26D20", "Deal-403845"],
    "ACTION": ["Deal-25F752", "Deal-D348E1", "Deal-944310"],
    "BUILD": ["Deal-E53952", "Deal-5408B0", "Deal-9AAE5F"],
    "REVIVE": ["Deal-2D1F1B"],
    "WATCH": ["Deal-66D1FC", "Deal-C6FE92", "Deal-950043"],
    "RISKY": ["Deal-547B2B", "Deal-B7EBD1", "Deal-A2B47C"]
  },
  "risky_deals": ["Deal-547B2B", "Deal-B7EBD1", "Deal-A2B47C", "Deal-2465CE", "Deal-584EE5", "Deal-FD9F4E"],
  "lock_violations": 0,
  "pipeline_shape": "156 deals total: 24 late-stage (DS4/DS5) vs 132 early-stage; forecast split: 11 COMMIT, 40 BEST_CASE, 105 PIPELINE. Top-tier LOCK/ACTION accounts for $85,459, with 6 RISKY deals flagged where COMMIT forecast lacks meeting evidence."
}
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 $0.0032 · 59s · in 1,846 / out 2,092 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
```json
[
  {
    "transcript_id": "TX-001",
    "deal_alias": "Deal-CFE7F4",
    "why_buys": [
      "automating anniversary and birthday awards — our HR team of three cannot keep up with it manually"
    ],
    "pain_points": [
      "track everything in a spreadsheet, and people slip through the cracks",
      "HR team cannot keep up manually with awards"
    ],
    "stakeholders": ["VP People", "HR Admin"],
    "budget_signal": "$40k earmarked for engagement tools this fiscal year",
    "timeline_signal": "live before open enrollment in November",
    "competitor_mentioned": "Achievers",
    "next_step": "security review on September 12",
    "objections": [
      "need SSO and audit logs for IT to sign off"
    ],
    "confidence": "high"
  },
  {
    "transcript_id": "TX-002",
    "deal_alias": "Deal-70BB30",
    "why_buys": [
      "tie recognition to retention for our hourly workforce"
    ],
    "pain_points": [
      "regretted turnover over 30%"
    ],
    "stakeholders": ["Head of Total Rewards", "CFO"],
    "budget_signal": "$25k pilot budget for this quarter",
    "timeline_signal": "decision by end of September",
    "competitor_mentioned": null,
    "next_step": "pilot agreement to be routed to legal this week",
    "objections": [
      "Integration with Workday has to be rock solid — that's my one condition"
    ],
    "confidence": "high"
  },
  {
    "transcript_id": "TX-003",
    "deal_alias": "Deal-530B50",
    "why_buys": [
      "make recognition visible across our 12 retail locations"
    ],
    "pain_points": [
      "Store managers have zero budget autonomy for on-the-spot recognition today"
    ],
    "stakeholders": ["People Ops Manager"],
    "budget_signal": null,
    "timeline_signal": "no rush until Q1",
    "competitor_mentioned": "Bucketlist",
    "next_step": "schedule a call with CEO",
    "objections": [],
    "confidence": "medium"
  },
  {
    "transcript_id": "TX-004",
    "deal_alias": "Deal-180D02",
    "why_buys": [
      "consolidate three separate recognition tools into one",
      "paying for three tools and none of them talk to our HRIS"
    ],
    "pain_points": [
      "paying for three tools that don't integrate with HRIS"
    ],
    "stakeholders": ["VP People", "IT Security Lead"],
    "budget_signal": "under $15k annually",
    "timeline_signal": "procurement cycle runs six to eight weeks minimum",
    "competitor_mentioned": null,
    "next_step": null,
    "objections": [
      "security review took three months for last vendor"
    ],
    "confidence": "medium"
  },
  {
    "transcript_id": "TX-005",
    "deal_alias": "Deal-F8767A",
    "why_buys": [
      "automate service milestones",
      "give us analytics on recognition equity across departments",
      "Our night-shift teams feel invisible — their engagement scores run 20 points lower"
    ],
    "pain_points": [
      "night-shift teams feel invisible",
      "engagement scores run 20 points lower for night-shift"
    ],
    "stakeholders": ["HR Director", "People Ops Coordinator"],
    "budget_signal": "$12k approved under our engagement line",
    "timeline_signal": "running before January all-hands",
    "competitor_mentioned": "Nectar",
    "next_step": "present to exec team on October 2",
    "objections": [
      "exec team is skeptical after a failed rollout two years ago"
    ],
    "confidence": "high"
  },
  {
    "transcript_id": "TX-006",
    "deal_alias": "Deal-EE195F",
    "why_buys": [
      "cut the admin time on service awards",
      "I personally spend five hours a month ordering and shipping plaques"
    ],
    "pain_points": [
      "five hours a month ordering and shipping plaques manually"
    ],
    "stakeholders": ["HR Manager"],
    "budget_signal": null,
    "timeline_signal": "Q1 start (fiscal year resets in February)",
    "competitor_mentioned": "internal build",
    "next_step": "send one-page overview to forward to COO this week",
    "objections": [
      "COO usually prefers building things in-house"
    ],
    "confidence": "medium"
  }
]
```
next-to-close0.75

? The Question

Which 3 deals are closest to signature right now? Use the CRM data and the Slack thread; Slack often has the real status before the CRM does. For each: deal, amount, why it is close, what is left. Under 250 words.
cost $0.0016 · 12s · in 1,176 / out 768 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
## 3 Deals Closest to Signature

### 1. Deal-547B2B — $11,200
**Why close:** Slack says "signing page is out, VP People confirmed signing tomorrow" (Alex Franklin, 9/4). Redlines clean, ready to execute.
**What left:** Signature only. Could close as early as 9/7.

### 2. Deal-403845 — $9,000
**Why close:** CRM shows DS5/COMMIT, close date 9/11. Slack confirms "order form is with their finance team" (Dana Mercer, 9/4).
**What left:** Finance approval, then signature.

### 3. Deal-A2B47C — $6,360
**Why close:** DS5/COMMIT with close date 9/11. Slack notes "normal legal-review pace" but still warm.
**What left:** Legal sign-off, then signature.

**Note:** Deal-2465CE ($5,400) shows DS5/COMMIT in CRM but Slack says blocked — champion left, procurement froze. Not actually close despite CRM status.
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 $0.0042 · 1356s · in 1,833 / out 2,250 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

| Deal | Prospect Quote | Classification | Amount |
|------|----------------|-----------------|--------|
| **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 confirm: "SCIM user provisioning ... are NOT currently listed as supported capabilities." | — |
| **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 confirm: "ADP Workforce Now integrations are NOT currently listed as supported capabilities." | — |
| **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 but is Enterprise tier only. Not a product gap. | — |
| **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 ISSUE** — Slack integration supported on all plans. Gap is training, not product. | — |
| **TG-005 — Deal-36C33F** | Rep: "we don't have a native mobile app for hourly workers yet, though it's on the roadmap." | **REAL GAP** — No mobile app in capabilities table. Rep admitted gap. | — |

---

### Summary: Real Product Gaps

**1. Deal-EC3025** (amount unknown)
- **Gap:** No SCIM user provisioning
- **Prospect voice:** "We need SCIM user provisioning — our security team will not approve any tool that requires manual account management."

**2. Deal-D0D6B5** (amount unknown)
- **Gap:** No ADP Workforce Now integration
- **Prospect voice:** "Our payroll runs on ADP Workforce Now and I don't see ADP anywhere in your integration list — that's a dealbreaker for us."

**3. Deal-36C33F** (amount unknown)
- **Gap:** No native mobile app
- **Prospect voice:** (Rep self-identified: "we don't have a native mobile app for hourly workers yet, though it's on the roadmap.")

---

*Note: Deal amounts were not provided in the transcript data.*
rep-performance
stale-pipeline-by-repPASS 1.00

? The Question

The snapshot date is 2026-09-05. Using the open deals and the engagements-by-deal table, list every open deal with no logged email, call, or meeting in the last 7 days. Compute recency from the engagements table's last_email/last_call/last_meeting fields, NOT from any date field on the deal (the deal's last_contacted_field is unreliable). For each stale deal give: deal alias, owner name, stage, amount, days since last contact. Group by owner, order by amount descending within each owner. Then state, per owner, how many stale deals they carry and the total stale amount. (Do not write outreach text.)
cost $0.0161 · 128s · in 22,285 / out 5,512 tok
✓ stale_recall_ge_80✓ grouped_by_owner✓ jake_top_amount✓ kevin_stale_amt✓ jenn_stale_amt✓ stale_count

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
Here are the stale deals with no logged email, call, or meeting in the last 7 days (since 2026-08-29), computed from the engagements table's last_email/last_call/last_meeting fields.

**Arithmetic verification examples:**
- Deal-2D1F1B: max(last_email=2026-06-11, last_meeting=2026-06-16) = 2026-06-16 → 81 days since last contact
- Deal-66D1FC: last_email=2026-08-20 → 16 days since last contact  
- Deal-44EA29: last_email=2026-08-26 → 10 days since last contact

---

### Bryce Harmon (13 stale deals, $626,243.00)
| Deal Alias | Stage | Amount | Days Since Last Contact |
|------------|-------|--------|--------------------------|
| Deal-2D1F1B | DS1 | $240,000.00 | 81 |
| Deal-66D1FC | DS1 | $99,000.00 | 16 |
| Deal-950043 | DS1 | $70,000.00 | 19 |
| Deal-B23205 | DS1 | $45,000.00 | 16 |
| Deal-7BBDFA | DS3 | $37,440.00 | 46 |
| Deal-332637 | DS2 | $36,000.00 | 9 |
| Deal-1BEEBF | DS1 | $31,500.00 | 19 |
| Deal-C5658B | DS1 | $23,400.00 | 16 |
| Deal-40522D | DS3 | $21,000.00 | 19 |
| Deal-F0EBBB | DS3 | $11,400.00 | 24 |
| Deal-E25A09 | DS1 | $6,000.00 | 9 |
| Deal-C9C286 | DS2 | $5,502.00 | 9 |
| Deal-012CB1 | DS1 | $1.00 | 23 |

### Dana Mercer (14 stale deals, $261,645.00)
| Deal Alias | Stage | Amount | Days Since Last Contact |
|------------|-------|--------|--------------------------|
| Deal-44EA29 | DS2 | $60,000.00 | 10 |
| Deal-E51FB7 | DS2 | $43,875.00 | 12 |
| Deal-B42F46 | DS1 | $27,000.00 | 19 |
| Deal-BA3DDC | DS3 | $23,400.00 | 15 |
| Deal-9DDE86 | DS2 | $20,000.00 | 15 |
| Deal-215CCA | DS3 | $18,900.00 | 17 |
| Deal-5EED42 | DS3 | $16,250.00 | 11 |
| Deal-57887A | DS2 | $15,000.00 | 8 |
| Deal-B7EBD1 | DS5 | $9,000.00 | 16 |
| Deal-3974EB | DS4 | $9,000.00 | 8 |
| Deal-F40F04 | DS2 | $8,100.00 | 15 |
| Deal-87DDD1 | DS1 | $5,000.00 | 19 |
| Deal-F336B6 | DS3 | $4,200.00 | 15 |
| Deal-0660B4 | DS4 | $1,920.00 | 16 |

### Alex Franklin (19 stale deals, $109,536.00)
| Deal Alias | Stage | Amount | Days Since Last Contact |
|------------|-------|--------|--------------------------|
| Deal-CC08D1 | DS1 | $24,000.00 | 16 |
| Deal-E73427 | DS3 | $18,000.00 | 10 |
| Deal-885F45 | DS2 | $9,300.00 | 12 |
| Deal-C2FF3C | DS1 | $8,316.00 | 10 |
| Deal-3EED2C | DS2 | $7,200.00 | N/A (no engagement) |
| Deal-0D2F7A | DS3 | $5,100.00 | 12 |
| Deal-6C60D4 | DS3 | $4,800.00 | 12 |
| Deal-13FEBD | DS2 | $4,680.00 | 12 |
| Deal-9D0060 | DS3 | $3,840.00 | 12 |
| Deal-690476 | DS2 | $3,600.00 | 18 |
| Deal-C6D97A | DS4 | $3,240.00 | 8 |
| Deal-EE195F | DS3 | $3,120.00 | 8 |
| Deal-278DEC | DS3 | $2,700.00 | 8 |
| Deal-635B8E | DS3 | $2,600.00 | 18 |
| Deal-6883F3 | DS1 | $2,400.00 | 16 |
| Deal-4A13AD | DS3 | $2,160.00 | 26 |
| Deal-F67D31 | DS2 | $1,800.00 | 8 |
| Deal-5FDCE4 | DS3 | $1,600.00 | 12 |
| Deal-BA571A | DS4 | $1,080.00 | 18 |

### Cole Ingram (18 stale deals, $252,905.03)
| Deal Alias | Stage | Amount | Days Since Last Contact |
|------------|-------|--------|--------------------------|
| Deal-D04904 | DS2 | $58,529.25 | 11 |
| Deal-B25F40 | DS3 | $40,000.00 | 8 |
| Deal-813836 | DS2 | $32,175.00 | 11 |
| Deal-1BA595 | DS2 | $31,750.00 | 11 |
| Deal-CFE1E8 | DS3 | $18,000.00 | 11 |
| Deal-CD47A6 | DS2 | $12,168.00 | 11 |
| Deal-627646 | DS3 | $11,193.00 | 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 | $5,616.00 | 10 |
| Deal-175395 | DS3 | $4,779.88 | 11 |
| Deal-481E24 | DS3 | $4,140.00 | 10 |
| Deal-C7F9BF | DS2 | $3,360.00 | 11 |
| Deal-2F3A66 | DS3 | $3,334.80 | 11 |
| Deal-342E96 | DS2 | $2,700.00 | 24 |
| Deal-E568D5 | DS3 | $1,875.00 | 11 |
| Deal-FD9F4E | DS5 | $1,330.00 | 10 |

### Elena Sinclair (1 stale deal, $2,100.00)
| Deal Alias | Stage | Amount | Days Since Last Contact |
|------------|-------|--------|--------------------------|
| Deal-57FF13 | DS1 | $2,100.00 | N/A (no engagement) |

---

**Summary per owner:**

| Owner | Stale Deals | Total Stale Amount |
|-------|-------------|-------------------|
| Bryce Harmon | 13 | $626,243.00 |
| Dana Mercer | 14 | $261,645.00 |
| Alex Franklin | 19 | $109,536.00 |
| Cole Ingram | 18 | $252,905.03 |
| Elena Sinclair | 1 | $2,100.00 |
| **GRAND TOTAL** | **65** | **$1,252,429.03** |
activity-mix-vs-outcomePASS 1.00

? The Question

The snapshot date is 2026-09-05. For each sales rep (owner), compute the last-30-day totals of emails, calls, and meetings from the engagements table, and the number of deals that entered DS2 in the last 30 days (t_ds2 within 30 days of the snapshot). Report per rep: the activity mix as percentages (emails/calls/meetings share of that rep's total activities), total activities per DS2 entry, and rank the reps by that efficiency ratio (lowest activities per DS2 entry = most efficient). Name the most efficient rep and the highest-volume rep, and note if they differ. Do not attribute cause.
cost $0.0140 · 76s · in 20,643 / out 3,151 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
## Report: Activity Mix and Efficiency by Sales Rep

**Snapshot Date:** 2026-09-05  
**Window:** 2026-08-06 to 2026-09-05 (last 30 days)

---

### Per-Rep Activity Totals (Last 30 Days)

| Rep | Emails | Calls | Meetings | **Total** | Email % | Call % | Meeting % |
|-----|--------|-------|----------|-----------|---------|--------|-----------|
| **Alex Franklin** | 307 | 36 | 41 | **384** | 79.9% | 9.4% | 10.7% |
| **Bryce Harmon** | 162 | 0 | 43 | **205** | 79.0% | 0.0% | 21.0% |
| **Dana Mercer** | 84 | 18 | 11 | **113** | 74.3% | 15.9% | 9.7% |
| **Cole Ingram** | 96 | 14 | 1 | **111** | 86.5% | 12.6% | 0.9% |
| **Farid Osman** | 38 | 0 | 34 | **72** | 52.8% | 0.0% | 47.2% |

*Note: Elena Sinclair excluded (only 1 deal, still in DS1 with no DS2 entry in window).*

---

### DS2 Entries (Last 30 Days) and Efficiency

| Rep | DS2 Entries | Total Activities | Activities per DS2 Entry |
|-----|-------------|------------------|-------------------------|
| **Alex Franklin** | 18 | 384 | **21.3** |
| **Bryce Harmon** | 4 | 205 | 51.2 |
| **Cole Ingram** | 2 | 111 | 55.5 |
| **Farid Osman** | 1 | 72 | 72.0 |
| **Dana Mercer** | 1 | 113 | 113.0 |

**Ranked by efficiency (lowest = most efficient):**  
1. Alex Franklin — 21.3 activities/DS2  
2. Bryce Harmon — 51.2 activities/DS2  
3. Cole Ingram — 55.5 activities/DS2  
4. Farid Osman — 72.0 activities/DS2  
5. Dana Mercer — 113.0 activities/DS2  

---

### Summary

- **Most Efficient Rep:** Alex Franklin (21.3 activities per DS2 entry)
- **Highest-Volume Rep:** Alex Franklin (384 total activities)
- **Same Person:** Yes — Alex Franklin is both the most efficient and highest-volume rep.

*Do not attribute cause. This data shows correlation, not causation.*
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 $0.0097 · 2919s · in 19,664 / out 3,283 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)**

---

**1. BOOKINGS VS QUOTA**
- QTD Bookings: $150,000
- Quota: $200,000
- Attainment: 75.0%

**2. NEW VS EXPANSION SPLIT**
- New: $113,500 (5 deals)
- Expansion: $36,500 (3 deals)
- New %: 75.7%

**3. ACTIVE PIPELINE BY STAGE**
| Stage | Deals | Amount |
|-------|-------|--------|
| DS1 | 20 | $284,621 |
| DS2 | 28 | $353,760 |
| DS3 | 67 | $552,705 |
| DS4 | 5 | $23,574 |
| DS5 | 5 | $45,730 |
| **TOTAL** | **125** | **$1,260,390** |

**4. ROLLING 90-DAY DS2-TO-WON RATE**
- DS2 entries (last 90 days): 111 deals
- DS2-to-won: 8 deals
- Conversion rate: 7.2%

**5. WINS AND LOSSES**
- Wins: 8
- Losses: 27
- Win/Loss ratio: 0.30
- Top loss reason: "Lost- Timing (1 year or more)" — 13 deals (48% of all losses)

**6. ACTIVITY VOLUME (LAST 30 DAYS)**
- Emails: 807
- Calls: 112
- Meetings: 128
- Notes: 50
- **Total: 1,097 activities**

---

## THREE COACHING OBSERVATIONS

**1. Timing losses are killing your quarter.** 13 of 27 losses (48%) cite "Timing (1 year or more)" as the reason. This signals weak ICP fit at the top of funnel or deals being pursued without confirming buyer intent timeline. Coach: Implement a "3-month commit" check at discovery — don't advance deals where the buyer can't confirm a close within the quarter.

**2. DS2 conversion is severely underperforming.** At 7.2%, you're converting roughly 1-in-14 deals that reach DS2. Given $1.26M in pipeline, even a modest improvement to 12% would yield ~$90K additional bookings. Coach: The 111 DS2 entries in 90 days is strong volume, but quality is lacking. Review DS2 qualification criteria with the AE.

**3. You have a pipeline coverage problem, not an activity problem.** $1.26M pipeline against $200K quota (6.3x coverage) yet only 75% attainment. Activity is high (1,097 touches), but the weighted forecast is missing. Coach: Focus on advancing DS3→DS4 deals where the commit is real, not adding more DS1 volume. Your DS4+DS5 pipeline is only $69K — that's <1 month of quota.
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 $0.0085 · 79s · in 5,601 / out 5,591 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
The files provided (deal_contacts.csv and unengaged_contacts.csv) do not contain deal **amount** or **stage** data. I can only analyze threading based on the contact data provided.

---

## Flagged Deals: Single-Threaded or Under-Threaded

| Deal Alias | Company | Active Contacts | Personas Present | Personas Missing | Flag | Unengaged on File |
|------------|---------|-----------------|-------------------|-------------------|------|-------------------|
| Deal-EC3025 | C-FDD0C7 | 1 | champion | HR admin, IT security, economic buyer, finance | **SINGLE-THREADED** | CT-6827DB: Chief People Officer (economic buyer) |
| Deal-92D97D | C-E23238 | 1 | HR admin | IT security, champion, economic buyer, finance | **SINGLE-THREADED** | none on file |
| Deal-36C33F | C-077A0E | 1 | IT security | HR admin, champion, economic buyer, finance | **SINGLE-THREADED** | CT-1DB73E: Chief People Officer (economic buyer) |
| Deal-FCBE5B | C-737030 | 1 | champion | HR admin, IT security, economic buyer, finance | **SINGLE-THREADED** | none on file |
| Deal-F9A08A | C-0D15DF | 1 | champion | HR admin, IT security, economic buyer, finance | **SINGLE-THREADED** | CT-697541: Chief People Officer (economic buyer) |
| Deal-50D386 | C-EB10E4 | 2 | champion, HR admin | IT security, economic buyer, finance | **UNDER-THREADED** | CT-A1C4B3: Chief People Officer (economic buyer) |
| Deal-5BFE3B | C-535D36 | 2 | champion | HR admin, IT security, economic buyer, finance | **UNDER-THREADED** | none on file |
| Deal-885F45 | C-5E8EFB | 2 | champion, economic buyer | HR admin, IT security, finance | **UNDER-THREADED** | CT-B3F25D: IT Security Lead (IT security) |
| Deal-5408B0 | C-2AE3AA | 2 | champion, HR admin | IT security, economic buyer, finance | **UNDER-THREADED** | CT-07FA76: Chief People Officer (economic buyer) |
| Deal-D0D6B5 | C-32918E | 3 | champion | HR admin, IT security, economic buyer, finance | **UNDER-THREADED** | CT-1FA4DB: Chief People Officer (economic buyer) |
| Deal-C6D97A | C-5A8FC2 | 3 | champion | HR admin, IT security, economic buyer, finance | **UNDER-THREADED** | none on file |

---

### Data Missing

The following were requested but not present in the provided files:
- **Deal amount** — not in deal_contacts.csv or unengaged_contacts.csv
- **Deal stage** — not in deal_contacts.csv or unengaged_contacts.csv  
- **Most valuable persona to add given the stage** — cannot determine without stage data

To complete the analysis with amount, stage, and stage-based persona recommendations, please provide a file containing deal amounts and stages.
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 $0.0064 · 33s · in 18,118 / out 1,419 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
## Analysis: Alex Franklin — Last 10 Calls

### Lead (first 5 minutes)

- **8 of 10 calls**: "Quick one to kick off: a 400-person retailer we work with cut regretted turnover 18% in two quarters after automating milestone awards..."
- **1 call**: "I put together a short agenda — security review first, then pricing." (TT-004)
- **1 call**: "You asked for straight pricing last time, so let's start there." (TT-009)

### Three most common objections and responses

**Objection 1 — Budget locked** ("budget is locked until next fiscal year — I can't add a new line item right now")
- Response: "Totally fair. Most teams fund this out of turnover savings — that retailer saved about $210k in avoided backfills, which is how their finance team signed off."

**Objection 2 — Revisit next quarter** ("This is good, but can we revisit it next quarter? Open enrollment starts in October and we're underwater.")
- Response: "Makes sense. What if we scope a 90-day pilot with one department so you have internal data before next quarter's planning?"

**Objection 3 — Spreadsheets work** ("We already do recognition with a spreadsheet and quarterly gift cards — why would we change?")
- Response: "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 agreement rate

7 / 10 = **70%** of calls ended with a concrete next step confirmed.

Calls without a locked next step: TT-004, TT-007, TT-010

### Competitors raised by prospects

- **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."
- **Workhuman** — "And unlike Workhuman, our pricing includes the full rewards catalog with no extra margin." (spoken by Alex, not raised by prospect)

### Coaching notes

1. **One call broke pattern** (TT-009) — Alex led with pricing instead of the case study and skipped the budget objection entirely. That call still closed with a next step, suggesting the tailored opener worked. Consider testing the case study vs. direct pricing more deliberately.

2. **Three calls stalled on committee/inaction** — TT-004, TT-007, and TT-010 produced no next step. In each case the rep either accepted "I'll leave it with you" (TT-004) or mirrored the prospect's deferral language. A stronger close such as "What would you need to bring to the committee?" could surface blockers earlier and recover the deal.
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 $0.0143 · 163s · in 22,177 / out 7,251 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

**Quarter:** 2026-07-01 to 2026-09-30  
**Weighting:** 100% COMMIT + 35% BEST_CASE. PIPELINE = 0.

---

### Category Totals (Inside Q3 Only)

| Category | Count | Total Amount |
|----------|-------|--------------|
| COMMIT | 7 | $44,729.00 |
| BEST_CASE | 24 | $203,565.00 |
| PIPELINE | 23 | $201,637.40 (counts as $0) |

---

### Weighted Forecast Calculation

```
COMMIT (100%):     $44,729.00
BEST_CASE (35%):   $203,565.00 × 0.35 = $71,247.75
─────────────────────────────────────────────────
WEIGHTED FORECAST: $115,976.75
```

---

### Excluded Deals (Outside Q3)

- **Count:** 32 deals
- **Total amount:** $227,575.00

*(These have close dates from 2026-10-01 to 2026-10-15)*

---

### Top 5 BEST_CASE Deals by Amount (Inside Q3)

| Rank | Deal Alias | Amount | Close Date |
|------|------------|--------|------------|
| 1 | Deal-2D7423 | $38,935.00 | 2026-09-30 |
| 2 | Deal-25F752 | $24,000.00 | 2026-09-25 |
| 3 | Deal-E53952 | $19,656.00 | 2026-09-30 |
| 4 | Deal-5EED42 | $16,250.00 | 2026-09-30 |
| 5 | Deal-FA32A0 | $11,116.00 | 2026-09-25 |

---

## Data Quality

Four data quality problems make this forecast unreliable if run unattended: (1) the `owner` field is empty on most deals, making it impossible to assign credit or identify whose pipeline this is; (2) the `why_buys_chars` field is mostly zero with no buy signal details, so BEST_CASE and COMMIT deals cannot be validated against actual customer intent; (3) one amount contains a decimal (`Deal-42326B` at $2,480.4) while all others are integers, indicating inconsistent data entry; and (4) the extract was pulled on 2026-09-05 but contains deals closing through 2026-10-15, meaning the data was not a point-in-time snapshot but rather includes future-dated pipeline that would shift categories between runs as close dates approach.
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 $0.0139 · 331s · in 14,717 / out 5,283 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
⚠ Stream stalled mid tool-call (execute_code); the action was not executed. Ask me to retry if you want to continue.## Activation Hypothesis Test Results

### 2x2 Matrix: Cohort Size & 24-Month Retention Rate

| Signal Combination | Cohort Size | Active at 24mo | Retention Rate |
|---|---|---|---|
| **Both signals** (m1_users >= 5 AND m1_redemptions >= 1) | 47 | 31 | **66.0%** |
| **Givers-only** (m1_users >= 5, m1_redemptions = 0) | 49 | 23 | **46.9%** |
| **Redemption-only** (m1_users < 5, m1_redemptions >= 1) | 29 | 9 | **31.0%** |
| **Neither** (m1_users < 5, m1_redemptions = 0) | 95 | 38 | **40.0%** |

### Exclusions
**None.** All 220 companies fit into one of the four categories.

### Single Signal with Largest Retention Lift
**GIVERS (m1_users >= 5)** — lift of **+6.9 percentage points** vs the baseline "neither" rate (40.0% → 46.9%). The redemption signal alone shows negative lift (-9.0 pp).

### What This Does and Does Not Prove

**DOES PROVE:**
- Strong correlation between dual-signal activation and retention: companies with both 5+ unique givers AND 1+ redemptions in month 1 retain at 66.0%, 26 points above the baseline.
- The givers signal is the stronger single predictor: 46.9% (givers-only) vs 40.0% (neither) vs 31.0% (redemption-only).
- Synergistic effect exists: both (66%) > givers-only (47%), meaning redemption adds value when the company already has givers.

**DOES NOT PROVE:**
- **Causation.** This is observational. Companies with both signals may have been more likely to succeed regardless of early engagement.
- **Redemption alone is not predictive.** Actually shows negative correlation (-9.0 pp vs baseline) — small companies with early redemptions churn more, not less.
- **No control for confounders.** Company size, industry, product tier, geography, and sales source are not controlled.
- **Small cell sizes.** Redemption-only (n=29) is insufficient for strong inference.
- **Cannot prove intervention works.** Driving more givers/redemptions in low-engagement companies may not cause retention improvement.
arr-rollforward-reconciliation0.80

? The Question

Reconcile ARR between CRM company records and billing subscriptions as of 2026-09-05. Billing ARR is MRR x 12. Report both totals, the variance, and a decomposition into buckets (status mismatch, rounding, missing records, other) that sums exactly to the variance. List each mismatched account with a suggested owner. Business rule: any subscription with a term other than 12 months must have cf_agreement_end_date populated; list violations.
cost $0.0187 · 1766s · in 15,387 / out 11,685 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 — 2026-09-05

### TOTALS
| Source | ARR |
|--------|-----|
| Billing (active MRR × 12) | $604,739.28 |
| Company (HubSpot) | $603,581.76 |
| **Variance** | **-$1,157.52** |

---

### VARIANCE DECOMPOSITION (sums to -$1,157.52)

| Bucket | Amount | Explanation |
|--------|--------|-------------|
| **Status Mismatch - Missing in Company** | -$28,449.24 | In billing but no CRM record |
| **Status Mismatch - Missing in Billing** | +$29,655.72 | In CRM but no active billing |
| **Rounding (≤$20 diff)** | +$36.00 | Small numeric differences |
| **Other (>$20 diff)** | -$2,400.00 | Same account, large variance |

---

### MISMATCHED ACCOUNTS

**Status Mismatch — Missing in Company:**
| Company Alias | Billing ARR | Suggested Owner |
|--------------|------------|-----------------|
| C-21629AA4 | $28,449.24 | CRM data entry error — verify if this ID exists in HubSpot |

**Status Mismatch — Missing in Billing (all 3 are cancelled in Chargebee):**
| Company Alias | Company ARR | Suggested Owner |
|--------------|------------|-----------------|
| C-0C8323BF | $4,905.24 | Verify subscription status — this is cancelled in billing |
| C-0DC4FB8C | $8,253.24 | Verify subscription status — this is cancelled in billing |
| C-0D5BBE3A | $16,497.24 | Verify subscription status — no subscription record |

**Other (>$20 variance):**
| Company Alias | Billing ARR | Company ARR | Diff | Suggested Owner |
|--------------|------------|-------------|------|-----------------|
| C-0F7269D7 | $26,796.00 | $24,396.00 | -$2,400.00 | Review contract terms / multi-year discount |

---

### BUSINESS RULE VIOLATIONS

Term ≠ 12 months but `cf_agreement_end_date` is NOT populated:

| Subscription ID | Company Alias | Term | End Date |
|-----------------|---------------|------|----------|
| SUB-0002 | C-1794A52C | 24 months | *(MISSING)* |
| SUB-0019 | C-22170CA1 | 36 months | *(MISSING)* |

---

### ARITHMETIC VERIFICATION
```
Variance = -$1,157.52
Bucket Sum = +$29,655.72 - $28,449.24 + $36.00 - $2,400.00 = -$1,157.52 ✓
```
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 $0.0063 · 89s · in 5,943 / out 3,507 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
**Four Core KVMs: 2026-08 vs 2026-07**

| KVM | Aug | Jul | Abs Change | Rel Change | Dir |
|-----|-----|-----|------------|------------|-----|
| Giving Rate | 0.6027 | 0.6023 | +0.0004 | +0.07% | ↑ |
| Redemptions per 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 | **-15.31%** | ↓ |

**Largest relative move:** Pulse Engagement at **-15.31%** (downward). The segment driving this is **enterprise** — pulse engagement dropped from 0.5500 in July to 0.2743 in August, a -50.13% decline for enterprise accounts. SMB and mid-market showed minimal movement (-0.22% and +0.21%, respectively). The data shows all companies in the enterprise segment experienced this drop, suggesting a structural shift specific to enterprise users rather than a company-specific anomaly.
rewards-ytd-section0.50

? The Question

Produce the redemption section of the weekly rewards report through the last completed month (2026-08): redemption count, spend, unique redeemers, redemptions per redeemer, provider mix as percent of spend, top 5 countries by redemptions. Say which month is the last completed month. Provider shares must sum to 100.
cost $0.0031 · 143s · in 905 / out 1,027 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
**Last completed month: 2026-08 (August 2026)**

---

**Redemption Count:** 48  
**Spend:** $3,536.00  
**Unique Redeemers:** 46  
**Redemptions per Redeemer:** 1.04  

---

**Provider Mix (% of spend):**

| Provider | Spend | % of Total |
|----------|-------|------------|
| Tremendous | $1,145.00 | 32.4% |
| custom | $1,057.00 | 29.9% |
| Snappy | $834.00 | 23.6% |
| TangoCard | $500.00 | 14.1% |
| **Total** | **$3,536.00** | **100.0%** |

---

**Top 5 Countries by Redemptions:**

1. US: 31
2. CA: 7
3. GB: 2
4. SG: 2
5. AU: 2
customer-success
churn-save-eligibility0.60

? 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 $0.0040 · 72s · in 1,282 / out 2,953 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 cutoff:** 2027-01-03 (2026-09-05 + 120 days)

---

### ELIGIBLE ACCOUNTS (All 3 rules met: R1 + R2 + R3)

| Account | Health Score | Eligible Amount | Renewal Date | At Stake | Play | Signal |
|---------|-------------|-----------------|--------------|----------|------|--------|
| C-0F6C0F34 | 51 | $49,707 | 2026-10-03 | $49,707 | Executive Touch | champion_active = false |
| C-0B827671 | 56 | $25,365 | 2026-11-14 | $25,365 | Usage Revival | usage_trend_3m = declining |
| C-0B360C78 | 57 | $35,748 | 2026-10-28 | $35,748 | Commercial Concession | champion_active = true, usage_trend_3m = growing (no urgent signal) |
| C-0B0F1BAB | 38 | $5,494 | 2026-09-23 | $5,494 | Executive Touch | champion_active = false |
| C-0CA21961 | 58 | $16,829 | 2026-12-28 | $16,829 | Commercial Concession | usage_trend_3m = flat, champion_active = true |
| C-0E9C27D1 | 39 | $41,235 | 2026-09-24 | $41,235 | Commercial Concession | usage_trend_3m = flat, champion_active = true |
| C-0D3278C7 | 54 | $17,602 | 2026-11-12 | $17,602 | Usage Revival | usage_trend_3m = declining |
| C-0F876796 | 47 | $19,958 | 2027-02-06 | $19,958 | Executive Touch | champion_active = false |

**Total at stake: $211,938**

---

### AT-RISK ACCOUNTS THAT DO NOT QUALIFY

| Account | Health Score | Eligible Amount | Renewal Date | Why Disqualified |
|---------|-------------|-----------------|--------------|------------------|
| C-0BC71BDD | 55 | **$0.00** | 2026-10-27 | R2 fails: churn_save_eligible_amount = 0 |
| C-0F6694C3 | 43 | **$0.00** | 2027-03-21 | R2 fails: churn_save_eligible_amount = 0 |
| C-0BE96399 | 54 | **$0.00** | 2026-10-29 | R2 fails: churn_save_eligible_amount = 0 |
| C-0FCCD2DF | 43 | **$0.00** | 2027-04-23 | R2 fails: churn_save_eligible_amount = 0 |
| C-10A56B0F | 54 | **$0.00** | 2026-12-12 | R2 fails: churn_save_eligible_amount = 0 |
| C-0BA71F12 | 52 | $6,824 | **2027-04-11** | R3 fails: renewal is 248 days after snapshot (beyond 120-day window) |
| C-0B843542 | 63 | $0.00 | 2027-02-01 | R1 fails: health_score = 63 (≥60); R2 fails: amount = 0 |
| C-14264ABD | 87 | $0.00 | 2027-05-13 | R1 fails: health_score = 87 (≥60); R2 fails: amount = 0 |
| C-0B2895EF | 73 | $0.00 | 2027-05-05 | R1 fails: health_score = 73 (≥60); R2 fails: amount = 0 |
| C-0EE1203E | 88 | $0.00 | 2027-04-15 | R1 fails: health_score = 88 (≥60); R2 fails: amount = 0 |
| C-0FC7A215 | 62 | $0.00 | 2027-05-28 | R1 fails: health_score = 62 (≥60); R2 fails: amount = 0 |
| C-0D035262 | 85 | $0.00 | 2026-10-20 | R1 fails: health_score = 85 (≥60); R2 fails: amount = 0 |
| C-0D6CC8E3 | 77 | $0.00 | 2027-04-05 | R1 fails: health_score = 77 (≥60); R2 fails: amount = 0 |
| C-0B2213A9 | 69 | $0.00 | 2026-12-03 | R1 fails: health_score = 69 (≥60); R2 fails: amount = 0 |
| C-0D0B047C | 70 | $0.00 | 2027-05-08 | R1 fails: health_score = 70 (≥60); R2 fails: amount = 0 |
| C-21FEBCBB | 65 | $0.00 | 2027-06-30 | R1 fails: health_score = 65 (≥60); R2 fails: amount = 0 |
| C-172EEFBC | 75 | $0.00 | 2026-11-24 | R1 fails: health_score = 75 (≥60); R2 fails: amount = 0 |
| C-0C3848D2 | 73 | $0.00 | 2027-03-05 | R1 fails: health_score = 73 (≥60); R2 fails: amount = 0 |
| C-0D890324 | 81 | $0.00 | 2027-04-10 | R1 fails: health_score = 81 (≥60); R2 fails: amount = 0 |
| C-0AAA9434 | 75 | $0.00 | 2026-10-14 | R1 fails: health_score = 75 (≥60); R2 fails: amount = 0 |
| C-8C2E8F00 | 73 | $0.00 | 2027-05-09 | R1 fails: health_score = 73 (≥60); R2 fails: amount = 0 |

---

### PLAY ASSIGNMENT LOGIC

- **Usage Revival**: usage_trend_3m = "declining" → account needs usage recovery to stabilize
- **Executive Touch**: champion_active = "false" → account lacks internal sponsor, needs executive engagement
- **Commercial Concession**: usage_trend_3m = "flat" or "growing" AND champion_active = "true" → no usage/champion issues, but commercial intervention needed to secure renewal
expansion-kit0.83

? The Question

A customer replied to outreach (reply included). Build the expansion kit: seat coverage (licensed over headcount), two lines on usage health, headroom in seats and ARR at the current per-seat rate, who replied and whether they can buy, the right buyer from our contacts, and a reply email under 150 words that moves toward the right person without being pushy and cites one real usage fact.
cost $0.0017 · 26s · in 446 / out 1,081 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 seats: **150**
- Headcount: **400**
- Coverage ratio: **37.5%** (150 ÷ 400)

### Usage Health (Two Lines)
1. **Utilization rate**: 126 users / 150 licensed seats = **84%** of license in use (August 2026)
2. **Growth trend**: Users grew from 88 (March) to 126 (August) — **+43% over 6 months**, averaging ~6-8 new users/month

### Headroom
- **Seats**: 150 licensed − 126 active = **24 seats available**
- **ARR expansion potential**: $9,000 ÷ 150 seats = **$60/seat/year** → 24 × $60 = **$1,440 ARR** at current rate

### Who Replied & Buying Authority
- **Replier**: Maria S., People Operations Coordinator
- **Can they buy?**: **No** — explicitly stated "I'm not the right person for purchasing decisions"

### Right Buyer
- **Dana R.**, VP People — owns budget and seat expansion decisions; "been asking about our usage numbers lately" (per Maria's reply)

---

### Reply Email (138 words)

> Hi Maria,
>
> Thanks so much for the quick reply — really glad to hear the team is getting value from Bonusly. And no worries at all, I appreciate you clarifying the routing.
>
> I'd love to connect with Dana. Given that she's been asking about usage numbers, I think there's a strong story to tell: we've grown from 88 monthly users to 126 over the last six months, now at 84% license utilization with room to grow. That kind of organic uptake often makes the case for expansion pretty straightforward.
>
> If you're able to make an intro, that would be fantastic. Either way, thank you for being such a great champion internally!
>
> Best,
> Cole
onboarding-statusPASS 1.00

? The Question

Prep the CSM for the mid-onboarding call: what is complete (integrations connected, allowance set, admins added, first recognition given, first redemption), what is not, early engagement signals, and the three things to cover on the call. Mark nothing complete without a data field that shows it.
cost $0.0016 · 24s · in 451 / out 955 tok
✓ complete_items✓ incomplete_items✓ three_items✓ grounded

✓ Correct Answer (ground truth)

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

Model's actual answer

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

### ✅ What's Complete (Data Confirmed)

| Milestone | Data Field | Value |
|-----------|------------|-------|
| Integration connected (Slack) | `integration_slack` | 2026-08-12 |
| Allowance set | `allowance_set` | 2026-08-13 |
| Admins added | `admins_added` | 2 |
| First recognition given | `first_recognition_at` | 2026-08-15 14:22 |

### ❌ What's NOT Complete (Data Missing)

| Milestone | Data Field | Status |
|-----------|------------|--------|
| Integration HRIS | `integration_hris` | **EMPTY** — not connected |
| First redemption | `first_redemption_at` | **EMPTY** — not completed |

---

### Early Engagement Signals

From `onboarding_usage.csv`, active givers grew from **3 → 15** over 25 days:

```
Aug 11:  3  →  Aug 18:  5  →  Aug 25: 9  →  Sept 1: 13
Aug 12:  3  →  Aug 19:  7  →  Aug 26: 11 →  Sept 2: 13  
Aug 13:  4  →  Aug 20:  6  →  Aug 27: 10 →  Sept 3: 15
Aug 14:  4  →  Aug 21:  9  →  Aug 28: 10 →  Sept 4: 15
Aug 15:  5  →  Aug 22:  8  →  Aug 29: 11
Aug 16:  4  →  Aug 23:  9  →  Aug 30: 13
Aug 17:  7  →  Aug 24:  9  →  Aug 31: 11
```

- **Trend**: +12 active givers (400% growth)
- **Peak**: 15 on Sept 3-4
- **Pattern**: Consistent upward trajectory with minor dips; strong sustained engagement

---

### Three Things to Cover on the Call

1. **HRIS Integration** — Still shows as not connected. Ask: What's blocking? Need help with setup?
2. **First Redemption** — No redemption recorded yet. Ask: Are rewards resonating? Any friction in the redemption flow?
3. **Engagement Celebration + Expansion** — Highlight the 15 active givers. Discuss: What's driving adoption? Any department or use case to double down on?
renewal-risk-conflicting-dates0.75

? 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 $0.0122 · 1702s · in 20,369 / out 6,187 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

## Date Source Decisions & Disagreements Flagged

| Account | CZ Date | CB Date | Disagree? | Decision | Rationale |
|---------|---------|---------|-----------|----------|-----------|
| C-0B7D2C30 | 2026-09-10 | 2026-09-15 | Yes (5 days) | **CB** | Trust CB; multi-year (36mo) known wrong in CZ |
| C-0BCDB8C2 | 2027-09-18 | 2026-09-18 | **YES — 1 year off** | **CB** | CZ shows 2027; CB shows 2026. CZ multi-year bug confirmed. Use CB: 2026-09-18 |
| C-0D2AB865 | 2026-09-10 | 2026-09-22 | Yes (12 days) | **CB** | Trust CB; multi-year (24mo) known wrong in CZ |
| C-0BBE3E60 | 2027-09-26 | 2026-09-26 | **YES — 1 year off** | **CB** | CZ shows 2027; CB shows 2026. CZ multi-year bug confirmed. Use CB: 2026-09-26 |
| C-0F5D2323 | 2026-09-10 | 2026-09-29 | Yes (19 days) | **CB** | Trust CB; multi-year (24mo) known wrong in CZ |
| C-0EC6999D | 2026-10-03 | 2026-10-03 | No | **CZ** | Single-year; dates agree |
| C-0B20DB64 | 2026-10-07 | 2026-10-07 | No | **CZ** | Single-year; dates agree |
| C-0BBC4E7A | 2026-10-10 | 2026-10-10 | No | **CZ** | Single-year; dates agree |
| C-0FD551AB | 2026-10-14 | 2026-10-14 | No | **CZ** | Single-year; dates agree |
| C-0F9F8F13 | 2026-10-18 | 2026-10-18 | No | **CZ** | Single-year; dates agree |
| C-0BC34584 | 2026-10-22 | 2026-10-22 | No | **CZ** | Single-year; dates agree |
| C-0B7A7546 | 2026-10-25 | 2026-10-25 | No | **CZ** | Single-year; dates agree |
| C-0B369871 | 2026-10-29 | 2026-10-29 | No | **CZ** | Single-year; dates agree |
| C-0B144C78 | 2026-11-02 | 2026-11-02 | No | **CZ** | Single-year; dates agree |
| C-0FC4DBB8 | 2026-11-05 | 2026-11-05 | No | **CZ** | Single-year; dates agree |
| C-0D5BBE3A | 2026-11-09 | 2026-11-09 | No | **CZ** | Single-year; dates agree |
| C-0FB9D5AF | 2026-11-13 | 2026-11-13 | No | **CZ** | Single-year; dates agree |
| C-0B344485 | 2026-11-16 | 2026-11-16 | No | **CZ** | Single-year; dates agree |
| C-0CB2C1B4 | 2026-11-20 | 2026-11-20 | No | **CZ** | Single-year; dates agree |
| C-22170CA1 | 2026-11-24 | 2026-11-24 | No | **CZ** | Single-year; dates agree |

**Disagreements flagged: 5** — All involve multi-year contracts where CZ erroneously adds 1 year to renewal date.

---

## Renewal Detail by Account (Sorted by Renewal Date)

### September 2026

**C-0B7D2C30** | CSM: Dana Mercer | ARR: $65,901 | Date: **2026-09-15**
- Seat Utilization: 274/476 = **57.6%**
- 3-Month Usage Trend: May-Jul avg 99.3 → Jun-Aug avg 91.7 (**-7.7%**, declining)
- Risk: **MEDIUM** — Seat utilization moderate but usage trending down 8% over 3 months.

**C-0BCDB8C2** | CSM: Cole Ingram | ARR: $54,427 | Date: **2026-09-18**
- Seat Utilization: 232/424 = **54.7%**
- 3-Month Usage Trend: Mar-May avg 146.3 → Jun-Aug avg 118.3 (**-19.1%**, declining sharply)
- Risk: **HIGH** — Usage dropped 19% quarter-over-quarter with only moderate seat utilization.

**C-0D2AB865** | CSM: Elena Sinclair | ARR: $38,022 | Date: **2026-09-22**
- Seat Utilization: 250/407 = **61.4%**
- 3-Month Usage Trend: Mar-May avg 144.3 → Jun-Aug avg 126.3 (**-12.5%**, declining)
- Risk: **MEDIUM** — Usage down 12.5% QoQ though seat utilization sits just above 60%.

**C-0BBE3E60** | CSM: Dana Mercer | ARR: $30,993 | Date: **2026-09-26**
- Seat Utilization: 74/114 = **64.9%**
- 3-Month Usage Trend: Mar-May avg 44.3 → Jun-Aug avg 38.3 (**-13.5%**, declining)
- Risk: **HIGH** — Usage declined 13.5% QoQ despite decent seat utilization.

**C-0F5D2323** | CSM: Cole Ingram | ARR: $90,647 | Date: **2026-09-29**
- Seat Utilization: 111/390 = **28.5%**
- 3-Month Usage Trend: Mar-May avg 19.0 → Jun-Aug avg 19.7 (flat)
- Risk: **HIGH** — Critical: only 28.5% seat utilization indicates severe under-adoption.

---

### October 2026

**C-0EC6999D** | CSM: Elena Sinclair | ARR: $79,419 | Date: **2026-10-03**
- Seat Utilization: 31/112 = **27.7%**
- 3-Month Usage Trend: Mar-May avg 14.7 → Jun-Aug avg 16.0 (up 9%)
- Risk: **HIGH** — Only 27.7% seat utilization despite slight usage uptick.

**C-0B20DB64** | CSM: Dana Mercer | ARR: $21,770 | Date: **2026-10-07**
- Seat Utilization: 214/378 = **56.6%**
- 3-Month Usage Trend: Mar-May avg 295.3 → Jun-Aug avg 298.7 (+1.2%, stable)
- Risk: **LOW** — Usage stable, seat utilization acceptable.

**C-0BBC4E7A** | CSM: Cole Ingram | ARR: $56,374 | Date: **2026-10-10**
- Seat Utilization: 228/337 = **67.7%**
- 3-Month Usage Trend: Mar-May avg 142.0 → Jun-Aug avg 140.7 (**-0.9%**, flat)
- Risk: **LOW** — High seat utilization, usage essentially flat.

**C-0FD551AB** | CSM: Elena Sinclair | ARR: $48,815 | Date: **2026-10-14**
- Seat Utilization: 210/376 = **55.9%**
- 3-Month Usage Trend: Mar-May avg 125.3 → Jun-Aug avg 123.7 (**-1.3%**, flat)
- Risk: **LOW** — Stable usage with moderate seat utilization.

**C-0F9F8F13** | CSM: Dana Mercer | ARR: $46,230 | Date: **2026-10-18**
- Seat Utilization: 199/352 = **56.5%**
- 3-Month Usage Trend: Mar-May avg 183.7 → Jun-Aug avg 184.0 (flat)
- Risk: **LOW** — Usage flat, seat utilization moderate.

**C-0BC34584** | CSM: Cole Ingram | ARR: $16,740 | Date: **2026-10-22**
- Seat Utilization: 327/494 = **66.2%**
- 3-Month Usage Trend: Mar-May avg 103.7 → Jun-Aug avg 104.3 (+0.6%, stable)
- Risk: **LOW** — Good seat utilization, usage stable.

**C-0B7A7546** | CSM: Elena Sinclair | ARR: $35,062 | Date: **2026-10-25**
- Seat Utilization: 182/205 = **88.8%**
- 3-Month Usage Trend: Mar-May avg 61.3 → Jun-Aug avg 64.0 (+4.4%, growing)
- Risk: **LOW** — High seat utilization, usage growing.

**C-0B369871** | CSM: Dana Mercer | ARR: $85,128 | Date: **2026-10-29**
- Seat Utilization: 317/422 = **75.1%**
- 3-Month Usage Trend: Mar-May avg 316.0 → Jun-Aug avg 325.0 (+2.8%, growing)
- Risk: **LOW** — Strong seat utilization, usage trending up.

---

### November 2026

**C-0B144C78** | CSM: Cole Ingram | ARR: $30,899 | Date: **2026-11-02**
- Seat Utilization: 169/224 = **75.4%**
- 3-Month Usage Trend: Mar-May avg 99.7 → Jun-Aug avg 102.0 (+2.3%, stable)
- Risk: **LOW** — High seat utilization, usage stable.

**C-0FC4DBB8** | CSM: Elena Sinclair | ARR: $94,732 | Date: **2026-11-05**
- Seat Utilization: 356/464 = **76.7%**
- 3-Month Usage Trend: Mar-May avg 183.3 → Jun-Aug avg 188.3 (+2.7%, growing)
- Risk: **LOW** — Strong seat utilization, usage growing.

**C-0D5BBE3A** | CSM: Dana Mercer | ARR: $39,740 | Date: **2026-11-09**
- Seat Utilization: 85/102 = **83.3%**
- 3-Month Usage Trend: Mar-May avg 86.3 → Jun-Aug avg 88.3 (+2.3%, growing)
- Risk: **LOW** — Very high seat utilization, usage growing.

**C-0FB9D5AF** | CSM: Cole Ingram | ARR: $63,158 | Date: **2026-11-13**
- Seat Utilization: 144/199 = **72.4%**
- 3-Month Usage Trend: Mar-May avg 166.3 → Jun-Aug avg 172.3 (+3.6%, growing)
- Risk: **LOW** — Good seat utilization, usage trending up.

**C-0B344485** | CSM: Elena Sinclair | ARR: $64,384 | Date: **2026-11-16**
- Seat Utilization: 224/287 = **78.0%**
- 3-Month Usage Trend: Mar-May avg 234.0 → Jun-Aug avg 240.7 (+2.9%, growing)
- Risk: **LOW** — Strong utilization, usage growing.

**C-0CB2C1B4** | CSM: Dana Mercer | ARR: $40,628 | Date: **2026-11-20**
- Seat Utilization: 386/473 = **81.6%**
- 3-Month Usage Trend: Mar-May avg 49.0 → Jun-Aug avg 47.7 (**-2.7%**, slight decline)
- Risk: **MEDIUM** — High seat utilization but usage dipped 2.7% QoQ.

**C-22170CA1** | CSM: Cole Ingram | ARR: $45,646 | Date: **2026-11-24**
- Seat Utilization: 251/294 = **85.4%**
- 3-Month Usage Trend: Mar-May avg 142.0 → Jun-Aug avg 145.7 (+2.6%, growing)
- Risk: **LOW** — Excellent utilization, usage growing.

---

## Summary

| Risk Level | Count | Total ARR |
|------------|-------|-----------|
| **HIGH** | 4 | $255,486 |
| **MEDIUM** | 2 | $104,529 |
| **LOW** | 14 | $746,406 |
| **TOTAL** | 20 | **$1,106,421** |

### ARR at Risk (HIGH + MEDIUM):
**$360,015** at risk (32.5% of total renewing ARR)

---

### CSM Workload in Renewal Window

| CSM | Accounts | Total ARR |
|-----|----------|-----------|
| Dana Mercer | 6 | $245,160 |
| Cole Ingram | 7 | $357,932 |
| Elena Sinclair | 7 | $503,329 |

**Highest-risk accounts by CSM:**
- Cole: C-0F5D2323 ($90,647, HIGH), C-0BCDB8C2 ($54,427, HIGH)
- Dana: C-0BBE3E60 ($30,993, HIGH), C-0CB2C1B4 ($40,628, MEDIUM)
- Elena: C-0EC6999D ($79,419, HIGH)
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 $0.0113 · 1639s · in 12,630 / out 6,519 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 Theme Analysis

**80 total tickets | $1,598,900 total ARR affected**

---

### BROAD PATTERNS (ranked by ARR exposure)

**1. HRIS Provisioning Failures**
- Count: 12 (15%)
- ARR: $456,000
- Distinct accounts: 3
- Ticket IDs: IC-460059, IC-460055, IC-460062
- Recommendation: Investigate HRIS sync job for null/empty field handling; add bulk provisioning retry logic

**2. Checkout/Redemption Failures**
- Count: 18 (22.5%)
- ARR: $177,300
- Distinct accounts: 7
- Ticket IDs: IC-460025, IC-460035, IC-460031
- Recommendation: Audit checkout timeout handling and gift card email dispatch for race conditions

**3. Points Not Posting**
- Count: 20 (25%)
- ARR: $70,200
- Distinct accounts: 9
- Ticket IDs: IC-460004, IC-460016, IC-460001
- Recommendation: Add point ledger transaction rollback on recognition delivery failure

**4. Slack Integration Failures**
- Count: 14 (17.5%)
- ARR: $63,400
- Distinct accounts: 4
- Ticket IDs: IC-460041, IC-460047, IC-460045
- Recommendation: Persist Slack OAuth tokens and re-auth state to prevent toggle resets

---

### SINGLE-ACCOUNT NOISE (excluded from ranking)

**5. Billing Seat Count Errors**
- Count: 16 (20%)
- ARR: $832,000
- Distinct accounts: 1 (C-0E9C27D1 only — 16 of 16 tickets)
- Ticket IDs: IC-460071, IC-460069, IC-460078
- Recommendation: Escalate to C-0E9C27D1 dedicated support; this is account-specific, not a product-wide issue

---

### Summary

| Theme | Count | Share | ARR | Accounts |
|-------|-------|-------|-----|----------|
| HRIS Provisioning | 12 | 15% | $456,000 | 3 |
| Checkout/Redemption | 18 | 22.5% | $177,300 | 7 |
| Points Not Posting | 20 | 25% | $70,200 | 9 |
| Slack Integration | 14 | 17.5% | $63,400 | 4 |
| Billing (C-0E9C27D1 only) | 16 | 20% | $832,000 | 1 |
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 $0.0019 · 33s · in 704 / out 1,183 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
## Top 3 Similar Customers with Case Studies

| Rank | Company | Matching Fields | Non-Matching |
|------|---------|-----------------|---------------|
| **1** | C-64171065 | **Industry, Size Band, Use Case** | Region (NA-East vs NA-West) |
| **2** | C-A13C193D | **Industry, Size Band, Region** | Use Case (retention vs employee_recognition) |
| **3** | C-11C31562 | **Size Band, Use Case, Region** | Industry (Manufacturing vs Technology) |

### Reasoning

The prospect (C-82AF3719) is **Technology / Mid-Market / employee_recognition / NA-West**.

**Scoring methodology**: Each matching field gets 1 point.

| Company | Industry | Size Band | Use Case | Region | Total |
|---------|----------|-----------|----------|--------|-------|
| C-64171065 | ✓ | ✓ | ✓ | ✗ | **3** |
| C-A13C193D | ✓ | ✓ | ✗ | ✓ | **3** |
| C-11C31562 | ✗ | ✓ | ✓ | ✓ | **3** |
| C-9D69D147 | ✗ | ✓ | ✗ | ✓ | 2 |
| C-CD4829A7 | ✓ | ✓ | ✗ | ✗ | 2 |

The three-way tie at 3 points was broken by weighting the most common/prospective valuable fields:
1. **C-64171065**: Same technology industry + same mid-market segment + same use case — strongest alignment
2. **C-A13C193D**: Same technology industry + same region (critical for NA-West expansion)
3. **C-11C31562**: Same region + same use case + same size band — strongest operational match

Eight customers total have case studies; five were evaluated above.
channel-attribution-mathPASS 1.00

? The Question

Compute channel performance for the trailing 6 months. Paid: spend, SQMs, SQOs, cost per SQM, cost per SQO, SQM-to-SQO rate, pipeline amount, pipeline per dollar. Organic: volume, SQO rate, pipeline. Report a channel with spend and zero SQMs as undefined, not zero. Flag rows where SQO date precedes SQM date. Finish with a reallocation recommendation and your confidence given sample sizes.
cost $0.0147 · 1286s · in 20,046 / out 6,835 tok
✓ undefined_channel✓ undefined_not_zero✓ flagged_rows✓ best_channel✓ reallocation

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
## CHANNEL PERFORMANCE REPORT — TRAILING 6 MONTHS (Mar–Aug 2026)

---

### PAID CHANNELS

| Channel | Spend | SQMs | SQOs | Cost/SQM | Cost/SQO | SQM→SQO Rate | Pipeline | Pipeline/$ |
|---------|-------|------|------|----------|----------|--------------|----------|------------|
| paid_search | $36,000 | 40 | 18 | $900 | $2,000 | 45.0% | $720,000 | $20.00 |
| linkedin_ads | $24,000 | 25 | 8 | $960 | $3,000 | 32.0% | $96,000 | $4.00 ⚠️ |
| paid_social | $18,000 | 0 | 0 | **undefined** | **undefined** | N/A | $0 | $0.00 |
| webinars | $9,000 | 12 | 5 | $750 | $1,800 | 41.7% | $60,000 | $6.67 |

### ORGANIC CHANNELS

| Channel | Volume | SQMs | SQOs | SQO Rate | Pipeline |
|---------|--------|------|------|----------|----------|
| organic_search | 30 | 30 | 10 | 33.3% | $90,000 |
| referral | 15 | 15 | 6 | 40.0% | $48,000 |

---

### DATE FLAGS — SQO precedes SQM

- **linkedin_ads**: CT-000044 (SQM: 2026-07-23, SQO: 2026-07-18), CT-000041 (SQM: 2026-06-14, SQO: 2026-06-09)

These 2 records are data errors — SQO cannot occur before first touch.

---

### REALLOCATION RECOMMENDATION

**Efficiency ranking (Pipeline per dollar):**
1. **paid_search**: $20.00/$
2. **webinars**: $6.67/$
3. **linkedin_ads**: $4.00/$
4. **paid_social**: $0.00/$ (undefined — zero SQMs from spend)

**Action**: Shift budget away from paid_social ($18,000) toward paid_search and webinars, which show 3x–5x better pipeline efficiency.

- paid_search generates **$20 pipeline per dollar** — the clear winner
- webinars delivers **$6.67** — solid secondary ROI
- linkedin_ads is underperforming at **$4.00** but has higher ASPs ($12K vs $40K); worth testing angle optimization before cutting
- paid_social: $18K spent, zero attributed SQMs — **pause immediately**

---

### CONFIDENCE ASSESSMENT

| Channel | SQMs | SQOs | Confidence |
|---------|------|------|------------|
| paid_search | 40 | 18 | HIGH — n > 30 |
| linkedin_ads | 25 | 8 | MODERATE — n ≈ 25 |
| webinars | 12 | 5 | LOW-MODERATE — n < 15 |
| paid_social | 0 | 0 | NONE — no data |
| organic_search | 30 | 10 | HIGH |
| referral | 15 | 6 | MODERATE |

Sample sizes: paid_search and organic_search are statistically meaningful (n≥30). LinkedIn and webinars are suggestive but need more volume. Paid_social has **no attribution data** — cannot evaluate.
competitor-battlecard-updatePASS 1.00

? The Question

Update the battlecard for the competitor. Sections: one-line positioning, pricing with source and date (newer source wins, note the conflict), where they win, where we win, objections and responses, recent changes, our 12-month win/loss record against them. Cite a snippet id for every factual claim. Rep opinion on a call is not a fact about the competitor. Anything from the old card you cannot re-source gets marked unverified.
cost $0.0027 · 32s · in 1,351 / out 1,809 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 September 2026)

## One-Line Positioning
Points-based employee recognition platform targeting mid-market and EU enterprise companies, with recent expansion into engagement surveys. (S02, S12, S06)

---

## Pricing

**Current Price: $7 per user/month** (annual billing required)

| Date | Source | Price |
|------|--------|-------|
| 2026-01-20 | Pricing page (S03) | $5/user/mo |
| 2026-04-01 | Pricing page (S08) | $5/user/mo |
| 2026-08-12 | Pricing page (S17) | $7/user/mo |
| 2026-08-14 | Call notes (S18) | $7/user/mo list, 15% discount for 3-year |

**Conflict noted:** Pricing increased from $5 to $7 between April and August 2026. One deal (S13, June 2026) quoted $6.50/user/mo for a 500-seat prospect—indicating discounting started before the official price change.

---

## Where They Win

1. **EU data residency requirements** — Prospect C mentioned Rivally pitched this capability (S05). Dublin office opened July 2026; EU data residency generally available (S15).
2. **Distributed EU teams** — Multi-language support praised by EU enterprise reviewer (S12).
3. **Fast implementation** — Mid-market reviewer: setup under a week, Slack integration worked out of the box (S04).
4. **Recognition feed engagement** — Reviewers consistently praise the social recognition feed (S02, S16).
5. **Support responsiveness** — G2 review praises sub-4-hour response time (S22).

---

## Where We Win

1. **Analytics depth** — 800-seat prospect chose Bonusly over Rivally citing analytics depth (S25). Rivally reviews cite "basic" and "limited" analytics (S02, S07).
2. **SCIM provisioning** — Enterprise reviewer notes manual user management is painful; lacks SCIM (S10).
3. **Admin tooling** — Reviewer cites admin tooling lags peers; lacks bulk recognition editing (S16, S24).
4. **Migration/offboarding** — Migration off Rivally hard; CSV-only exports (S20).
5. **Rewards catalog (EMEA)** — TrustRadius review: EMEA rewards catalog thinner than US (S14).

---

## Objections and Responses

| Objection | Response |
|-----------|----------|
| "Their analytics is basic" | S02, S07, S25 — Confirm. Our analytics depth won an 800-seat deal. |
| "No SCIM provisioning" | S10 — True. Manual user management required; painful at scale. |
| "EU data residency needed" | S05, S15 — They offer it now (Dublin office, GA July 2026). Competitive differentiator eroded. |
| "Migration was hard" | S20 — True, CSV-only exports. Our export capabilities are superior. |
| "Thinner EMEA rewards" | S14 — True. US catalog stronger than EMEA. |

---

## Recent Changes

| Date | Change | Source |
|------|--------|--------|
| 2026-08-12 | Pricing increased to $7/user/mo | S17 |
| 2026-08-20 | Microsoft Teams app v2 in public preview | S19 |
| 2026-09-01 | Rivally Pulse add-on exits beta, now paid add-on | S23 |
| 2026-07-19 | Dublin office opened; EU data residency GA | S15 |
| 2026-05-09 | Hired ex-Workday VP EMEA | S11 |
| 2026-03-05 | Launched Rivally Pulse (survey add-on) | S06 |

---

## Our 12-Month Win/Loss Record Against Rivally

**Period: September 2025 – August 2026**

| Month | Wins | Losses |
|-------|------|--------|
| 2025-09 | 1 | 1 |
| 2025-10 | 2 | 0 |
| 2025-11 | 1 | 1 |
| 2025-12 | 1 | 1 |
| 2026-01 | 2 | 0 |
| 2026-02 | 2 | 0 |
| 2026-03 | 1 | 1 |
| 2026-04 | 0 | 2 |
| 2026-05 | 0 | 2 |
| 2026-06 | 1 | 0 |
| 2026-07 | 1 | 0 |
| 2026-08 | 1 | 0 |
| **TOTAL** | **13** | **7** |

**Win Rate: 65%** (13/20 deals)

---

## Items from Old Card — Status

| Old Card Claim | Status |
|---------------|--------|
| Positioning: points-based recognition for mid-market | **VERIFIED** — Consistent across reviews (S02) |
| Pricing: starts at $5 per user/month (as of 2026-01) | **VERIFIED BUT STALE** — Now $7 (S17) |
| Rivally lacks a Slack integration | **UNVERIFIED** — S04 says Slack integration worked "out of the box" |
| Rivally was acquired by WorkHuman in 2025 | **UNVERIFIED** — No source in snippets |
| Strong in EU enterprise with multi-language support | **VERIFIED** — S12 confirms |
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 $0.0272 · 1378s · in 39,611 / out 13,908 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 Analysis

| Sequence | Step | Sent | Open Rate | Reply Rate | Meeting Rate | Weakest Step |
|----------|------|------|-----------|------------|-------------|--------------|
| **New Logo Nurture** | 1 | 500 | 42.0% | 8.40% | 2.40% | — |
| | 2 | 458 | 34.9% | 6.55% | 1.97% | — |
| | 3 | 428 | 28.0% | 4.21% | 1.40% | Step 3 (reply ↓) |
| **Expansion Nurture** | 1 | 300 | 43.3% | 7.33% | 1.67% | — |
| | 2 | 300 | 113.3%* | 8.33% | 1.33% | Step 2 (tracking error) |
| | 3 | 275 | 34.5% | 4.36% | 1.09% | Step 3 (reply ↓) |
| **Cold Outbound - HR Leaders** | 1 | 600 | 40.0% | **0.83%** | 0.00% | **Step 1** |
| | 2 | 595 | 29.4% | **0.34%** | 0.00% | — |
| | 3 | 590 | 22.0% | **0.17%** | 0.00% | — |
| **Cold Outbound - People Ops** | 1 | 400 | 37.5% | 3.50% | 0.75% | — |
| | 2 | 386 | 28.5% | 2.33% | 0.52% | — |
| | 3 | 377 | 21.2% | **1.59%** | 0.27% | **Step 3** |

*Tracking error: 340 opened > 300 sent

### Tracking Errors
- **Expansion Nurture Step 2**: 340 opened exceeds 300 sent — likely duplicate opens counted or CRM sync error

### Audience Overlap
- **Cold Outbound - HR Leaders ∩ Cold Outbound - People Ops**: 21 contacts (e.g., CT-001285, CT-001311, CT-001130)
- **New Logo Nurture ∩ Expansion Nurture**: 2 contacts (CT-000301, CT-000624)

### Failure Modes (<2% Reply)
- **Cold Outbound - HR Leaders** all 3 steps: 0.83% → 0.34% → 0.17% — message mismatch to HR audience, no personalization
- **Cold Outbound - People Ops** Step 3: 1.59% — sequence fatigue, diminishing relevance

### One Change Per Weak Sequence + Priority

1. **Cold Outbound - HR Leaders (PRIORITY 1)**: Revise Step 1 with HR-specific value prop + personalization trigger — current 0.83% reply is catastrophic
2. **Cold Outbound - People Ops**: Add a different hook at Step 3 or shorten to 2 steps — Step 3 under 2%
3. **Expansion Nurture**: Fix tracking before Step 2 (duplicate open issue), then review Step 3 content
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 $0.0015 · 12s · in 328 / out 893 tok
✓ metrics_present✓ deltas✓ coverage_ratio✓ pace✓ paragraph

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
## Weekly Marketing Goals Update — Q3-2026 (as of Day 66)

| Metric | QTD Actual | Target | Delta | Pace |
|---|---|---|---|---|
| SQMs | 230 | 300 | -70 | **Ahead** (230 vs 215 expected) |
| SQOs | 84 | 120 | -36 | Behind (84 vs 86 expected) |
| DS2s | 40 | 75 | -35 | Behind (40 vs 54 expected) |
| Closed-Lost MIA Rate | 0.20 (5/25) | 0.10 | +0.10 | Behind (20% vs 10% target) |
| Same-Quarter Closes | 10 | 20 | -10 | Behind (10 vs 14 expected) |
| Active Pipeline | $3.00M | $4.00M | -$1.00M | **Ahead** ($3.00M vs $2.87M expected) |

**What moved this week:** SQMs remain the strongest performer, now exceeding pace by 15 units — likely driven by the trade show leads from early August converting to meetings. DS2s and same-quarter closes are the biggest gaps; the DS2 shortfall (-35) suggests bottleneck in demo-to-close progression. Closed-lost MIA rate ticked up (5 out of 25 closed-lost = 20% vs 10% target), indicating reps are abandoning deals without sufficient discovery. Pipeline coverage is healthy at 75% ($3M/$4M) but needs $1M more to hit target by quarter-end.
communication
ceo-slack-compression0.80

? The Question

Compress the forecast analysis below into a single Slack message for the CEO. Rules: at most three sentences; under 80 words; lead with the decision or the so-what; keep every distinct number that matters and the one caveat that changes the conclusion; cut any sentence that does not carry a claim, a number, or a directive. No em dashes, no throat-clearing phrases.
cost $0.0012 · 20s · in 602 / out 580 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: **$115,977** (7 COMMIT deals at $44,729 + 35% of $203,565 BEST_CASE). Caveat: 85 of 86 deals missing owner, 71 missing why-buy including all COMMIT, and 32 deals closing in October—close dates appear pushed rather than re-qualified. This forecast looks precise but isn't.
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 $0.0010 · 9s · in 439 / out 439 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 pricing we sent

Hi Alex,

I wanted to check in since we haven't heard back since sending over the pricing for the 150-seat plan back on August 5th.

I know things get busy — just wanted to see if you had any questions or if there's anything else I can clarify? Happy to jump on a quick call or answer via email.

Let me know how you'd like to proceed.

Best,
Amani
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 $0.0014 · 13s · in 420 / out 774 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
# Weekly GTM Digest

**Marketing** kicked off the week strong with 46 SQMs, coming in at 88.5% against the 52 target. The webinar channel delivered 18 of those—good for 39.1% of total SQMs—showing solid momentum in that channel. Paid search kept acquisition efficient at $150 cost per SQM, holding steady from prior weeks.

**Sales** converted 14 SQOs and set 9 DS2 meetings, building pipeline at a steady clip. The team created $310,000 in new pipeline and closed 3 deals in the same quarter this week.

**Customer Success** saved 2 renewals and pushed team NPS up to 61, reflecting strong customer relationships. The team is tracking 3 open red-flag accounts heading into next week—close monitoring in progress.

**PLG** had a standout week with 412 new signups and 31% activation, showing healthy product traction. Thirty-eight companies hit the aha moment milestone of giving 10 recognitions—proof that users are finding value and spreading the appreciation culture.
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 $0.0015 · 13s · in 608 / out 850 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 — August 24 to September 04, 2026

**Partner-Sourced Pipeline: 5 deals | $550,000**

---

## Active Partners

**Apex Rewards Co**
- Status: Active
- Sourced Pipeline: 2 deals | $275,000
- Deals: Deal-DDAAF2 ($180,000), Deal-2CF33E ($95,000)
- Activity: Co-webinar locked for 09-15; 2 sourced opps in DS1 (confirmed UTM Source = Partner)

**HRCloud Partners**
- Status: Active
- Sourced Pipeline: 1 deal | $140,000
- Deals: Deal-F1CDA5 ($140,000)
- Activity: Integration referral advanced to DS2; 1 sourced opp this period

**CultureBridge**
- Status: Active
- Sourced Pipeline: 2 deals | $135,000
- Deals: Deal-096E1D ($60,000), Deal-067213 ($75,000)
- Activity: Lunch-and-learn produced 2 sourced opps (early stage)

**WorkWell Group**
- Status: Active
- Sourced Pipeline: 0 deals | $0
- Activity: Planning call booked for 09-09 to restart joint playbook for Q4

---

## 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 $0.0015 · 12s · in 476 / out 855 tok
✓ answered_count✓ unanswerable_flagged✓ citations✓ no_guess

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
# Vendor Response to Security Questionnaire

**Q1: Is customer data encrypted at rest? If so, how?**
Yes. All customer data is encrypted at rest using AES-256. [P1]

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

**Q3: Do you hold a SOC 2 report, and can we obtain a copy?**
Yes. Bonusly maintains a SOC 2 Type II report, renewed annually. The report is available to prospects under NDA. [P2]

**Q4: How long is customer data retained after contract termination?**
Customer data is retained for the duration of the contract plus 90 days, after which it is deleted from production systems. [P3]

**Q5: Where is your subprocessor list published and how are changes notified?**
The subprocessor list is maintained at trust.bonusly.com. Customers receive 30 days notice before a new subprocessor is added. [P4]

**Q6: Do you support SAML single sign-on?**
Yes. SAML 2.0 single sign-on is available on the Enterprise plan (along with SCIM user provisioning). [P5]

**Q7: What is your production backup schedule and retention?**
Production databases are backed up daily. Backups are encrypted and retained for 35 days. [P6]

**Q8: Do you hold an ISO 27001 certification?**
Unanswerable from provided excerpts. This information would be in a certification or compliance directory document.

**Q9: What is your contractual uptime SLA percentage?**
Unanswerable from provided excerpts. This information would be in the master services agreement or SLA terms document.

**Q10: Will you sign a HIPAA Business Associate Agreement?**
Unanswerable from provided excerpts. This would be addressed in contract terms or a dedicated HIPAA compliance document.
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 $0.0254 · 38s · in 80,004 / out 3,557 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 Set Reconciliation Report

## Finding 1: Duplicate ALWAYS-Trigger Phrases
**Severity:** CRITICAL  
**Action Type:** REVIEW  
**Issue:** `email-drafter` and `comms-drafter` have identical ALWAYS trigger phrases:
- "write me an email"
- "draft a follow-up"
- "help me reply"
- "what should I say"
- "bump email"
- "contract nudge"

Both skills handle email drafting. `comms-drafter` explicitly covers "outbound prospecting, follow-ups, post-demo recaps" — overlap is direct. `email-drafter` is more specific to AEs/SDRs/CSMs, while `comms-drafter` is company-wide.

**Proposal:** Designate one as primary. Recommendation: keep `email-drafter` for revenue team emails, expand `comms-drafter` scope to non-email communications (Slack, Intercom) or consolidate under one skill with role-based routing.

---

## Finding 2: No Circular Delegation Chains
**Severity:** N/A  
**Action Type:** N/A  
No circular chains detected. Delegations flow outward:
- `pipeline-intelligence-report` → `closed-lost-analysis`
- `deal-strategy-coach` → `prospect-research-multithreading` (external)
- `comms-drafter`/`email-drafter` → `deal-strategy-coach` (reference only, not delegation)

---

## Finding 3: No Dangling Delegation Targets
**Severity:** INFO  
**Action Type:** TRIM_DESC  
No dangling targets. Skills reference external systems (`bonusly-data-questions`, `bonusly-product-questions`, `prospect-research-multithreading`) that exist outside this manifest scope.

---

## Finding 4: Version Conflicts
**Severity:** WARNING  
**Action Type:** UPDATE_BODY  

| Skill | Manifest Declares | Body States | Conflict |
|-------|------------------|-------------|----------|
| `analysis-validator` | (none) | Frontmatter: v3.6, Trail: v3.2 | Version in validation trail (line ~918) says "analysis-validator v3.2" but frontmatter and changelog say v3.6 |
| `pipeline-intelligence-report` | (none) | Description: "v6 · May 2026" | Version in description but not frontmatter |

**Proposal:** The skill body should survive — update manifest to include version field. For `analysis-validator`, change validation trail reference from v3.2 to v3.6 to match current state.

---

## Finding 5: Manifest Descriptions Exceeding 1,024 Characters
**Severity:** INFO  
**Action Type:** TRIM_DESC  

Count: **0** — None exceed 1,024 characters.

| Skill | Characters |
|-------|------------|
| pipeline-intelligence-report | 1006 |
| signalforge-claim-compressor | 1006 |
| partner-digest | 1004 |

All are below the 1,024 threshold.

---

## Finding 6: Hardcoded Page IDs, Dates, and Person Names
**Severity:** CRITICAL  
**Action Type:** UPDATE_BODY  

**Hardcoded dates:**
- `analysis-validator`: "April 26, 2026" (Created), "May 9, 2026" (Last Updated), "May 4, 2026" (CALL_SPOTLIGHT_BRIEF removal)
- `closed-lost-analysis`: "May 2026" (AI field sample), "May 4, 2026" (roster)
- `deal-strategy-coach`: "April 2026" (Playbook), "May 4, 2026" (roster)
- `model-selection`: "2026-05-19" (last_checked)
- `next-to-close`: "May 2026", "May 4, 2026"
- `pipeline-intelligence-report`: "May 2026"
- `stale-pipeline-report`: "2026-06-10" (v1.0)
- `sales-forecast`: "April 27, 2026", "Q2 2026"

**Hardcoded person names:**
- `analysis-validator`: "Manish", "Amani" (escalation contacts)
- `closed-lost-analysis`: Full AE roster with names (Bryce Harmon, Hugo Lindqvist, Dana Mercer, etc.)
- `deal-strategy-coach`: Full GTM roster with names
- `pipeline-intelligence-report`: Full AE roster with names + "Alaina Loori"

**Hardcoded HubSpot IDs:**
- Multiple skills embed owner IDs: 83155923 (Dana Mercer), 83155924 (Cole Ingram), 84342457 (Alex Franklin), 1520255671 (Gavin Porter), etc.

**Hardcoded page IDs:**
- `partner-digest`: Folder ID `2286616609`, Space ID `1958248479`
- `pipeline-intelligence-report`: Confluence references with page IDs
- `signalforge-feedback`: Page ID `2295136266`

**Proposal:** Replace hardcoded dates with dynamic queries or "current quarter" language. Move person rosters to external reference tables. Keep HubSpot IDs only where necessary for join logic, not for display.

---

## Finding 7: Manifest Drift
**Severity:** INFO  
**Action Type:** REVIEW  

| Direction | Status |
|-----------|--------|
| Files with no manifest row | **NONE** — all 14 files have corresponding rows |
| Manifest rows with no file | **NONE** — all 14 rows have corresponding files |

**Drift:** None detected. 14 files ↔ 14 rows — exact match.

---

## Summary Table

| Finding | Severity | Count | Action Type |
|---------|----------|-------|-------------|
| Duplicate trigger phrases | CRITICAL | 1 pair | REVIEW |
| Circular delegation | — | 0 | N/A |
| Dangling delegation | — | 0 | — |
| Version conflicts | WARNING | 2 skills | UPDATE_BODY |
| Long descriptions (>1K chars) | INFO | 0 | — |
| Hardcoded IDs/dates/names | CRITICAL | 9 skills | UPDATE_BODY |
| Manifest drift | INFO | 0 | — |
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 $0.0017 · 14s · in 647 / out 1,044 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 — #incident-2026-08-29-reward-queue

## Step 1: Acknowledge alert and assume incident command
- **Who:** Bryce Harmon
- **Action:** PagerDuty alert acknowledged, IC taken
- **Verification:** Alert acknowledged in PagerDuty
- **Rollback:** N/A — administrative action
- **Trace:** M01

---

## Step 2: Check current queue depth
- **Who:** Farid Osman
- **Action:** `bundle exec rake sidekiq:queue_depth`
- **Verification:** Command returns pending job count; reported 48,213
- **Rollback:** N/A — read-only diagnostic
- **Trace:** M02

---

## Step 3: Inspect dead set for failed jobs
- **Who:** Farid Osman
- **Action:** Examined dead set in Sidekiq
- **Verification:** Found 112 jobs, all Redis::TimeoutError timestamped ~13:58
- **Rollback:** N/A — read-only diagnostic
- **Trace:** M03

---

## Step 4: Pause auto-recognition enqueue to stop the bleed
- **Who:** Farid Osman
- **Action:** `bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'`
- **Verification:** Check that new jobs stop appearing in queue (manual observation)
- **Rollback:** `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'`
- **Trace:** M04

---

## Step 5: Clear the dead set
- **Who:** Elena Sinclair
- **Action:** Cleared dead set via Rails console
- **Verification:** Dead set confirmed empty
- **Rollback:** N/A — jobs already failed; no recovery path in thread
- **Trace:** M05

> **Note:** Command used for clearing not specified in thread — needs confirmation

---

## Step 6: Scale up reward-worker deployment
- **Who:** Bryce Harmon
- **Action:** `kubectl scale deployment/reward-worker --replicas=6`
- **Verification:** `kubectl get deployment reward-worker` shows 6 replicas; queue depth decreases
- **Rollback:** `kubectl scale deployment/reward-worker --replicas=3`
- **Trace:** M06

---

## Step 7: Verify queue depth declining
- **Who:** Farid Osman
- **Action:** `bundle exec rake sidekiq:queue_depth`
- **Verification:** Reported 9,400 and falling ~1,200/min
- **Rollback:** N/A — read-only diagnostic
- **Trace:** M07

---

## Step 8: Confirm queue drained and error rate normalized
- **Who:** Cole Ingram
- **Action:** `bundle exec rake sidekiq:queue_depth` + check Datadog
- **Verification:** Queue at 0; Datadog error rate at baseline
- **Rollback:** N/A — read-only diagnostic
- **Trace:** M08

---

## Step 9: Re-enable auto-recognition enqueue
- **Who:** Bryce Harmon
- **Action:** `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'`
- **Verification:** Observed 40 new jobs processed cleanly in 3 minutes
- **Rollback:** `bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'`
- **Trace:** M09

---

## Step 10: Scale workers back to normal
- **Who:** Bryce Harmon
- **Action:** `kubectl scale deployment/reward-worker --replicas=3`
- **Verification:** Queue remains at 0; stable
- **Rollback:** `kubectl scale deployment/reward-worker --replicas=6`
- **Trace:** M10
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 $0.0022 · 15s · in 1,799 / out 1,249 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 (Root Cause)
- **Timestamp:** 2026-09-03T14:01:12Z
- **Service:** reward-service
- **Message:** `Redis::TimeoutError: Connection to Redis at redis-primary:6379 timed out after 5s`

### Cascade (in order)
| Timestamp | Service | Message |
|-----------|---------|---------|
| 14:01:20Z | reward-service | ERROR retry exhausted for RewardGiveJob |
| 14:01:30Z | reward-service | ERROR retry exhausted for RewardGiveJob |
| 14:01:40Z | reward-service | ERROR retry exhausted for RewardGiveJob |
| 14:01:40Z | sidekiq | ERROR RewardGiveJob failed: Redis::TimeoutError; retrying in 60s |
| 14:02:28Z | sidekiq | ERROR RewardGiveJob failed: Redis::TimeoutError; retrying |
| 14:02:30Z | sidekiq | WARN Queue reward depth above 10,000 |
| 14:03:05Z | api-gateway | ERROR 502 upstream timeout calling reward-service /gives |
| 14:03:30Z | web-app | ERROR Give form submission failed: upstream 502 from api-gateway |
| 14:03:31Z+ | sidekiq | ERROR RewardGiveJob failed (repeated through 14:06:47Z) |

### Service and Job Involved
- **Service:** reward-service (primary), sidekiq (job processor), api-gateway (upstream)
- **Job:** RewardGiveJob (class: `RewardGiveJob`)

### Datadog Query to Confirm First Error
```
service:reward-service level:error "Redis::TimeoutError" "redis-primary:6379"
```
Time range: 2026-09-03T14:01:00Z to 2026-09-03T14:01:30Z

### What the Logs Do NOT Show
- **Root cause of the Redis timeout** — no preceding Redis logs showing memory pressure, connection exhaustion, or network issues
- **Recovery trigger** — no logs explaining how/why Redis connection was restored at 14:22:10Z
- **Alert notifications** — no PagerDuty, Slack, or email alerts in the slice
- **Peak queue depth** — the WARN at 14:02:30Z says "above 10,000" but doesn't give the exact peak
- **Impact scope** — no logs showing how many Give form submissions failed, or customer-facing impact duration
- **Resolution actions** — no operator intervention logs, no Redis config changes, no restarts
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 $0.0015 · 1231s · in 579 / out 874 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 | Segment/Targeting | Companies |
|------|-------|------------------|-------------------|------------------|
| recognition_streaks_v2 | ON | Triggers StreakTracker.record(give) to track recognition streaks | segment:beta_companies | 42 |
| points_budget_guardrails | ON | Runs BudgetService.enforce!(giver, points) to enforce point budget limits | all_companies | 220 |
| slack_dm_nudges | ON | Sends Slack DM nudges via SlackDm.send_nudge(user) | segment:region_na | 87 |
| redeem_flow_redesign | OFF | Renders RedeemV2Component (on) vs RedeemV1Component (off) | targeted_list | 12 |
| analytics_dashboard_v3 | ON | Loads AnalyticsV3 dashboard | segment:tier_three | 65 |
| ms_teams_app_v2 | OFF | Installs TeamsAppV2 via TeamsAppV2.install(company) | targeted_list | 9 |

## Flags with NO code reference

| Flag | State | Segment/Targeting | Companies | Issue |
|------|-------|-------------------|-----------|-------|
| legacy_give_modal | OFF | segment:legacy_plan | 14 | No code found in flag_code.md |
| survey_boosters_q3 | ON | segment:legacy_plan | 7 | No code found in flag_code.md |
| paused_offboard_cleanup | OFF | (none) | 0 | No code found in flag_code.md |

## Flags with zero targets

- **paused_offboard_cleanup**: company_count = 0. Targeting rules column is empty, meaning no companies are assigned to this flag.
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 $0.0014 · 12s · in 678 / out 769 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 Triage

### NDA-1 — [PARTY A] / Bonusly
**Triage: GREEN**

Reasoning: Standard mutual NDA with reasonable terms (2-year term, 3-year survival). Contains all standard carve-outs (publicly available, prior knowledge, third-party receipt, independent development, legal disclosure). Delaware law is favorable. No non-solicits, non-competes, or concerning restrictions.

---

### NDA-2 — [PARTY B] / Bonusly
**Triage: YELLOW**

Reasoning: Contains a non-solicitation clause restricting hiring of employees "with whom it came into contact under this Agreement" for 18 months post-termination. The clause is somewhat narrow (only applies to employees contacted during the NDA), but introduces a post-term restriction that warrants counsel review. All other terms are standard.

*Key clause:* "During the term of this Agreement and for eighteen (18) months thereafter, neither party shall solicit for employment or hire any employee of the other party with whom it came into contact under this Agreement."

---

### NDA-3 — [PARTY C] / Bonusly
**Triage: RED**

Reasoning: Multiple high-risk issues requiring full legal review:

1. **One-way confidentiality**: Only protects [PARTY C]. Bonusly receives no confidentiality protection — it's purely the disloser.

2. **Non-compete is overly broad**: "Recipient shall not, directly or indirectly, engage in or provide services to any business that competes with Discloser's business" for 3 years — no geographic limitation, no customer/prospect carve-out, no reasonableness test.

3. **No carve-outs stated**: Missing exceptions for publicly available information, prior knowledge, independent development, or legally required disclosure. This creates ambiguous exposure.

4. **Irish jurisdiction**: Foreign governing law adds complexity and potential enforcement challenges.

*Key clause:* "For a period of three (3) years following the Effective Date, Recipient shall not, directly or indirectly, engage in or provide services to any business that competes with Discloser's business."