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z-ai/glm-5v-turbo

Mean 0.967 · 31/36 perfect tests · $1.81 total · 36/40 tests completed — remaining tests discarded from study (endpoint timeouts after dedicated retries) · each card: the question → the correct answer (gold) → this model's actual answer

data-crm
stage-hygiene-audit0.67

? 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 $0.0892 · 116s · in 35,517 / out 3,783 tok
✓ noncomp_pct✗ no_ds1_0✗ regressed_0✓ per_owner_breakdown✓ definitions_first✓ three_fixes

✓ Correct Answer (ground truth)

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

Model's actual answer

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STAGE HYGIENE AUDIT — DEFINITIONS & FINDINGS

---

DEFINITIONS

- **Why-buys non-compliant**: deal at DS2+ whose why_buys is empty/missing, a placeholder (tbd/n/a/see notes), or under 15 characters. LONG(n) truncated values count as compliant.
- **No DS1 timestamp**: deal with blank/null t_ds1 field.
- **Regressed**: deal where any earlier-stage entry timestamp is later than a later-stage entry timestamp (i.e., timestamps do not monotonically increase).

---

WHY-BUYS COMPLIANCE (DS2+)

Total DS2+ deals: 124
Non-compliant: 101 = 81.5%
Compliant (LONG truncated): 23

By owner:

  Bryce Harmon:        15/18 non-compliant = 83.3%
  Dana Mercer:        18/22 non-compliant = 81.8%
  Alex Franklin:     48/61 non-compliant = 78.7%
  Cole Ingram: 19/22 non-compliant = 86.4%
  Farid Osman:         1/1  non-compliant = 100.0%

DS1 TIMESTAMP COVERAGE

Deals missing t_ds1: 0 of 156 (100% coverage)

REGRESSION CHECK

Deals with out-of-order stage timestamps: 0 of 156

---

THREE PROCESS FIXES

1. **Mandatory why-buys at DS2 gate**. Enforce a required-field guard in the CRM that blocks stage advancement to DS2 unless why_buys is populated with 15+ characters. This is the single largest gap — 81% of DS2+ pipeline has no recorded buying reason.

2. **Weekly hygiene sweep for DS3+ ghost fields**. Of the 23 compliant deals, all compliance comes from LONG() truncation artifacts — meaning the field is only populated on records that have extensive notes. Run a weekly report flagging every DS3+ deal with empty/placeholder why_buys and assign remediation to the owner before the next pipeline review.

3. **Stage-timestamp monotonicity validation at stage change**. While zero deals currently show regression, the dataset contains near-misses (e.g., Deal-D7E999 has t_ds1=t_ds2=2026-05-14, same-day back-to-back entries). Add a CRM validation rule that rejects a stage advancement if the new timestamp is earlier than the previous stage's entered_current_stage timestamp — this prevents data-quality drift from manual date edits or bulk imports.
owner-resolutionPASS 1.00

? 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 $0.0868 · 530s · in 14,084 / out 13,984 tok
✓ resolved_count✓ unmatched_none✓ deactivated_flagged✓ jake_amount✓ justin_amount✓ no_invented

✓ Correct Answer (ground truth)

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

Model's actual answer

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DEAL OWNER RESOLUTION REPORT

Deals resolved: 156 of 156
Deals with unmatched owner ID: (none -- all 156 matched)
Archived/deactivated owners with open deals: (none -- all matched owners are active)

Pipeline per resolved owner:

  Bryce Harmon          $1,054,144.00   (35 deals)
  Alex Franklin         $624,310.00   (67 deals)
  Dana Mercer            $341,195.00   (24 deals)
  Cole Ingram    $288,161.43   (22 deals)
  Farid Osman              $4,134.00    (7 deals)
  Elena Sinclair             $2,100.00    (1 deal)

Total open pipeline (all resolved): $2,314,044.43

Arithmetic check:
  1054144 + 624310 + 341195 + 288161.43 + 4134 + 2100 = 2,314,044.43

Notes:
- The owners table contains two archived entries (Gavin Porter 1520255671, Hugo Lindqvist 77260721) but neither owns any deal in the open pipeline.
- Owner ID 701163055 (Elena Sinclair) is listed as active and matches Deal-57FF13 ($2,100).
gong-hubspot-join-integritydiscarded

? 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.

✓ 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 answer

not yet run — check back after the runners complete
snowflake-sql-trapped-catalogPASS 1.00

? The Question

Using ONLY the catalog provided, write one Snowflake SQL query returning, per customer company, for its first calendar month as a customer: unique givers, recognition count, successful redemption count. Read the catalog notes carefully: some tables are stale or unpopulated and must be avoided, and one documented filter (excluding deleted givers) must NOT be applied to historical giving counts. Put a comment above each FROM or JOIN explaining the table choice. Return only fenced SQL.
cost $0.0264 · 38s · in 17,015 / out 1,501 tok
✓ avoids_stale_deals✓ avoids_unpopulated_live✓ uses_cohort_summary✓ succeeded_filter✓ no_giver_deletion_filter✓ returns_sql

✓ Correct Answer (ground truth)

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

Model's actual answer

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```sql
-- Per-company first calendar month (M1) as a paying customer:
--   unique givers, recognition count, successful redemption count.
SELECT
  c.company_id,
  c.M1_USERS            AS unique_givers,
  c.M1_GIVES            AS recognition_count,
  c.M1_REDEMPTIONS      AS successful_redemption_count
FROM PRODUCTION.PLG.COMPANY_COHORT_SUMMARY AS c
  -- One row per self-serve company; carries pre-aggregated M1_* metrics
  -- computed without the deleted-giver exclusion (per business rule).
WHERE c.FIRST_SUB_PAYMENT_DATE IS NOT NULL
  -- Guard: only companies that reached paid status have a meaningful "first month".
```
crm-completeness-enrichmentPASS 1.00

? The Question

Audit the CRM extract for completeness and propose fixes. Deals need owner, stage, amount, close date, why-buys; companies need industry, employee count, HQ country; contacts need email, title, persona. Report completeness percent per field. Identify duplicate company clusters (name variants, shared domain) with a survivor per cluster. List invalid emails and domain mismatches. Fill missing company fields only where the enrichment export has a matching row; where CRM and enrichment disagree list both and recommend a source. Never invent a value. End with the 10 fixes with the most pipeline amount at stake.
cost $0.1510 · 338s · in 22,127 / out 18,547 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
Here's the complete CRM audit:

═══════════════════════════════════════
CRM DATA AUDIT RESULTS
═══════════════════════════════════════

Dataset: 34 companies | 52 contacts | 25 enrichment rows


=== 1. FIELD COMPLETENESS ===

  COMPANIES (n=34):
    industry          34/34 = 100.0%
    employee_count    25/34 = 73.5%   ← 9 gaps
    hq_country        28/34 = 82.4%   ← 6 gaps

  CONTACTS (n=52):
    email             52/52 = 100.0%
    title             39/52 = 75.0%   ← 13 missing
    persona           37/52 = 71.2%   ← 15 missing

  DEALS:
    No deals.csv provided — owner, stage, amount, close_date, and why_buys CANNOT be assessed.


=== 2. DUPLICATE COMPANY CLUSTERS (shared domain) ===

  Cluster 1 — domain: acme-corp.com
    SURVIVOR:  C-0A092931
    MERGE:     C-0A092932
    Note: employee_count differs (500 vs 510), industry differs ("Technology" vs "tech")

  Cluster 2 — domain: globex.io
    SURVIVOR:  C-0A092933
    MERGE:     C-0A092934
    Note: industry differs ("SaaS" vs "Technology"), all other fields identical


=== 3. INVALID / MISSING EMAILS (4 total) ===

  CT-0010: 'user0@'          → malformed (no domain)
  CT-0080: 'user0@'          → malformed (no domain)
  CT-0081: 'user1@'          → malformed (no domain)
  CT-0192: 'user2@'          → malformed (no domain)


=== 4. DOMAIN MISMATCHES (1 total) ===

  CT-0011: email='user1@other-domain.com' (domain=other-domain.com)
           vs company_domain='66d1fc.com'
  → This contact belongs to C-66D1FC but the email is from a different domain.


=== 5. MISSING FIELDS — ENRICHMENT FILL & CONFLICTS ===

  ENRICHMENT CAN FILL (8 fields — all employee_count):

    Company         Field           Value   Source
    ─────────────── ─────────────── ──────  ──────────────────
    C-EC3025       employee_count  400     zoominfo[ec3025.com]
    C-96039F       employee_count  400     zoominfo[96039f.com]
    C-44EA29       employee_count  400     zoominfo[44ea29.com]
    C-D04904       employee_count  400     zoominfo[d04904.com]
    C-B23205       employee_count  400     zoominfo[b23205.com]
    C-60C75F       employee_count  400     zoominfo[60c75f.com]
    C-7BBDFA       employee_count  400     zoominfo[7bbdfa.com]
    C-50D386       employee_count  400     zoominfo[50d386.com]

  VALUE CONFLICTS: None found.
  (All CRM↔Enrichment disagreements were formatting-only:
   "tech"/"Tech "/"Technology" ↔ "Computer Software",
   "US"/"USA" ↔ "United States",
   "health care" ↔ "Healthcare"
   → No action needed; these are equivalent values.)

  NO ENRICHMENT SOURCE AVAILABLE: 7 field gaps cannot be filled:
    C-2D1F1B.hq_country, C-D73B89.hq_country, C-D04904.hq_country,
    C-2C60E5.hq_country, C-EE9FFB.hq_country+employee_count,
    C-C9BB20.employee_count, C-93C8BF.employee_count+hq_country
  (These domains have no row in zoominfo_enrichment.csv)


=== 6. TOP FIXES BY PIPELINE AT STAKE ===

  Ranked by employee count as pipeline proxy:

  #1  C-EC3025.employee_count  (~400 emp)  → Fill: 400 from ZoomInfo
  #2  C-96039F.employee_count  (~400 emp)  → Fill: 400 from ZoomInfo
  #3  C-44EA29.employee_count  (~400 emp)  → Fill: 400 from ZoomInfo
  #4  C-D04904.employee_count  (~400 emp)  → Fill: 400 from ZoomInfo
  #5  C-B23205.employee_count  (~400 emp)  → Fill: 400 from ZoomInfo
  #6  C-60C75F.employee_count  (~400 emp)  → Fill: 400 from ZoomInfo
  #7  C-7BBDFA.employee_count  (~400 emp)  → Fill: 400 from ZoomInfo
  #8  C-50D386.employee_count  (~400 emp)  → Fill: 400 from ZoomInfo

  (Only 8 actionable enrichment fills exist. All other gaps lack a data source.)


=== SUMMARY OF ACTION ITEMS ===

  Priority 1 — Data integrity (blocks segmentation/reporting):
  [a] Merge duplicate clusters: absorb C-0A092932 into C-0A092931, C-0A092934 into C-0A092933
  [b] Fix 4 malformed emails (CT-0010, CT-0080, CT-0081, CT-0192) — re-research or mark invalid
  [c] Fix 1 domain mismatch (CT-0011) — confirm if contact actually belongs to C-66D1FC

  Priority 2 — Fill from enrichment (8 fields, zero cost):
  [d] Backfill the 8 employee_count values listed in section 5 above

  Priority 3 — Gaps with no source (7 fields, requires new research):
  [e] Run fresh enrichment or manual research for the 7 unfillable gaps

  Priority 4 — Contact hygiene:
  [f] Fill 13 missing titles across contacts
  [g] Fill 15 missing personas across contacts

Full audit script saved to /Users/amaniphipps/crm_audit.py
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 $0.0669 · 409s · in 21,129 / out 10,381 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
I'll classify every deal systematically, then produce the summary.

---

## FULL CLASSIFICATION (90 deals)

| # | Alias | Tag | Free-Text Reason | CATEGORY | SIDE |
|---|-------|-----|------------------|----------|------|
| 1 | Deal-DB0AAC | Lost-Timing | Putting on pause, will reconnect | **timing** | buyer |
| 2 | Deal-F7F635 | Competitor | Went in another direction | **competitor** | buyer |
| 3 | Deal-AC944F | MIA | unresponsive | **no decision** | unknown |
| 4 | Deal-214060 | MIA | unresponsive | **no decision** | unknown |
| 5 | Deal-91A056 | Lost-Timing | Reconnect early 2027 | **timing** | buyer |
| 6 | Deal-29326C | Lost-Timing | Timing | **timing** | buyer |
| 7 | Deal-5DB9B0 | Does not fit ICP | Spam | **other** | Bonusly |
| 8 | Deal-831B7B | Lost-Timing | Reconnect in new year | **timing** | buyer |
| 9 | Deal-F97C37 | Competitor | Other vendor had more diversified offerings | **competitor** | buyer |
| 10 | Deal-13E9CF | Doing nothing | R&R deprioritized by org | **no decision** | buyer |
| 11 | Deal-39E25C | Lost-Timing | Timing, reconnect next year | **timing** | buyer |
| 12 | Deal-7ED004 | Lost-Budget/Price | Did not get budget approval | **pricing** | buyer |
| 13 | Deal-21B045 | MIA | MIA | **no decision** | unknown |
| 14 | Deal-B3ABED | Lost-Timing | Revisit Q2 next year for 2028 budget | **timing** | buyer |
| 15 | Deal-422BA6 | Competitor | Chose ADP TotalSource PEO partner (integrations) | **competitor** | buyer |
| 16 | Deal-ED9AE7 | Lost DM | Timing, budget, authority | **other** | buyer |
| 17 | Deal-988493 | MIA | mia | **no decision** | unknown |
| 18 | Deal-381C8C | Competitor | Not moving forward with Bonusly | **competitor** | buyer |
| 19 | Deal-F308CA | MIA | No contact since April, ignored outreach | **no decision** | unknown |
| 20 | Deal-F1E8A6 | Competitor | Not moving forward with Bonusly | **competitor** | buyer |
| 21 | Deal-B6AC09 | Lost-Timing | Revisiting in 2027 | **timing** | buyer |
| 22 | Deal-70F704 | Lost DM | Only wanted anniversary awards, went MIA | **product gap** | buyer |
| 23 | Deal-E6E80A | Lost-Timing | Pushed into early 2027 | **timing** | buyer |
| 24 | Deal-B038F0 | Lost-Timing | Pushed back into early 2027 | **timing** | buyer |
| 25 | Deal-4664E1 | MIA | No contact after intro, ignored outreach | **no decision** | unknown |
| 26 | Deal-175756 | Lost-Timing | On hold until 2027, other priorities | **timing** | buyer |
| 27 | Deal-E74A73 | Doing nothing | Wants to test points manually first | **no decision** | buyer |
| 28 | Deal-DDAB52 | Competitor | Rippl: more features at same cost, easier budget | **competitor** | buyer |
| 29 | Deal-ACE061 | Competitor | Likely went with HeyTaco | **competitor** | buyer |
| 30 | Deal-BB78F3 | Lost-Timing | Leadership wants other survey actions first | **timing** | buyer |
| 31 | Deal-D48E0B | MIA | MIA | **no decision** | unknown |
| 32 | Deal-15DA99 | Lost-Timing | Early 2027 | **timing** | buyer |
| 33 | Deal-F4AF5D | Lost-Timing | Early next year | **timing** | buyer |
| 34 | Deal-79B7A1 | Lost-Timing | Timing | **timing** | buyer |
| 35 | Deal-583ADB | MIA | MIA | **no decision** | unknown |
| 36 | Deal-8E27DA | Feature Request | Went with swag provider, didn't want R&R | **no decision** | buyer |
| 37 | Deal-2D2F8D | Competitor | Different direction | **competitor** | buyer |
| 38 | Deal-E0441F | MIA | Stale from departed rep, no contact | **no decision** | unknown |
| 39 | Deal-7CB44D | MIA | No contact since demo, ignored outreach | **no decision** | unknown |
| 40 | Deal-0F96AA | Competitor | RFP, not advancing to finalist | **competitor** | buyer |
| 41 | Deal-1BCA50 | Competitor | Budget/gift cards; stakeholder down path with other vendor | **competitor** | buyer |
| 42 | Deal-7CC678 | Competitor | Nothing specific | **competitor** | unknown |
| 43 | Deal-FAC17C | Lost DM | Couldn't get Exec IT Director approval | **other** | buyer |
| 44 | Deal-242273 | Competitor | Other vendors digitize points + onsite spend | **competitor** | buyer |
| 45 | Deal-50E5D8 | Doing nothing | Leadership paused | **no decision** | buyer |
| 46 | Deal-A2C349 | Competitor | Sticking with Awardco (+surveying) | **competitor** | buyer |
| 47 | Deal-9F176A | Lost-Timing | Paused, maybe end of year | **timing** | buyer |
| 48 | Deal-7B2236 | Doing nothing | Budget + want simpler/cheaper | **pricing** | buyer |
| 49 | Deal-AFA56C | MIA | unresponsive | **no decision** | unknown |
| 50 | Deal-C7156E | Competitor | Selected another vendor | **competitor** | buyer |
| 51 | Deal-C33D91 | Lost-Budget/Price | Significant budget cuts | **pricing** | buyer |
| 52 | Deal-9048EB | MIA | Bad fit, multiple feature gaps | **product gap** | Bonusly |
| 53 | Deal-5E64CE | Doing nothing | High Nectar exit fee, contract thru Oct 2027 | **timing** | buyer |
| 54 | Deal-8A0992 | Competitor | Went with Canadian provider | **competitor** | buyer |
| 55 | Deal-D0C698 | Competitor | Past Kudos user, wants Kudos again | **competitor** | buyer |
| 56 | Deal-69CF3D | Lost-Timing | On Hold | **timing** | buyer |
| 57 | Deal-ECBF89 | Lost-Timing | On Hold | **timing** | buyer |
| 58 | Deal-3618CC | Lost DM | Wanted Surveys | **product gap** | buyer |
| 59 | Deal-EECC02 | Competitor | Another direction | **competitor** | buyer |
| 60 | Deal-5AD03E | Competitor | Wanted more defined budget access | **product gap** | buyer |
| 61 | Deal-D1A623 | Lost-Timing | timing | **timing** | buyer |
| 62 | Deal-413C56 | Doing nothing | Back to school priority, CEO not ready | **no decision** | buyer |
| 63 | Deal-47F1A1 | Competitor | Staying with WorkTango 12 mo | **competitor** | buyer |
| 64 | Deal-BF2A98 | Competitor | Recently deployed HiThrive | **competitor** | buyer |
| 65 | Deal-2A292B | Doing nothing | Building something internally | **no decision** | buyer |
| 66 | Deal-D1AABF | MIA | No response | **no decision** | unknown |
| 67 | Deal-FEDBCB | Doing thing | Not engaged, maybe end of year | **no decision** | buyer |
| 68 | Deal-1E7DA9 | Competitor | Selected another platform | **competitor** | buyer |
| 69 | Deal-2BBA21 | MIA | No contact, ignored nudges | **no decision** | unknown |
| 70 | Deal-286F9C | Competitor | Went with another platform | **competitor** | buyer |
| 71 | Deal-7FBAC6 | Doing nothing | Leadership paused (again) | **no decision** | buyer |
| 72 | Deal-369281 | Competitor | Using Paylocity solution | **competitor** | buyer |
| 73 | Deal-386F6E | MIA | No response | **no decision** | unknown |
| 74 | Deal-9FCD0D | Competitor | Chose Canadian company (CEO pref) | **competitor** | buyer |
| 75 | Deal-55867E | Lost-Timing | Not moving forward at this time | **timing** | buyer |
| 76 | Deal-DAFB82 | Lost-Budget/Price | Other priorities, not until 2028 | **pricing** | buyer |
| 77 | Deal-2FEDDB | Doing nothing | Unsure on timing | **no decision** | buyer |
| 78 | Deal-64B19A | Competitor | Likely stayed with Motivosity | **competitor** | buyer |
| 79 | Deal-3F86A0 | MIA | unresponsive | **no decision** | unknown |
| 80 | Deal-096750 | MIA | No contact, ignored attempts | **no decision** | unknown |
| 81 | Deal-F325A5 | Lost DM | Layoffs + leadership change | **champion left** | buyer |
| 82 | Deal-ABD14C | Doing nothing | Not interested | **no decision** | buyer |
| 83 | Deal-79E61A | MIA | Unresponsive | **no decision** | unknown |
| 84 | Deal-8A119B | Lost-Budget/Price | Didn't get approval | **pricing** | buyer |
| 85 | Deal-AE7C4E | MIA | Unresponsive | **no decision** | unknown |
| 86 | Deal-DAB4F1 | MIA | Unresponsive | **no decision** | unknown |
| 87 | Deal-B4B50F | MIA | Unresponsive | **no decision** | unknown |
| 88 | Deal-981AD4 | Feature Request | Doesn't fit UI, not UK focused | **product gap** | buyer |
| 89 | Deal-DC77FE | Competitor | Other system had more customization (points-as-dollars) | **competitor** | buyer |
| 90 | Deal-5885B9 | MIA | MIA | **no decision** | unknown |

---

## SUMMARY

### Category Counts

| Category | Count | % of 90 |
|----------|-------|---------|
| **no decision** | 33 | 36.7% |
| **competitor** | 27 | 30.0% |
| **timing** | 18 | 20.0% |
| **pricing** | 5 | 5.6% |
| **product gap** | 4 | 4.4% |
| **other** | 2 | 2.2% |
| **champion left** | 1 | 1.1% |
| **TOTAL** | **90** | **100%** |

Arithmetic: 33 + 27 + 18 + 5 + 4 + 2 + 1 = 90

### Side Split

| Side | Count | % of 90 |
|------|-------|---------|
| **buyer** | 65 | 72.2% |
| **unknown** (MIA/no-response) | 23 | 25.6% |
| **Bonusly** (disqualified by rep) | 2 | 2.2% |

Arithmetic: 65 + 23 + 2 = 90

### Tag-vs-Reason Disagreements (clear mismatches)

Deals where the structured tag and free-text reason tell fundamentally different stories:

1. **Deal-5AD03E** -- Tag: "Competitor" / Reason: "Wanted more defined budget access" (reason describes a product gap; no competitor named or implied)
2. **Deal-9048EB** -- Tag: "MIA" / Reason: "bad fit ... multiple feature gaps" (reason describes product-fit issues; deal was actively analyzed by CS+Sales leadership, not uncontactable)
3. **Deal-8E27DA** -- Tag: "Feature Request" / Reason: "moved forward with just a swag provider and didn't want R&R currently" (reason is about not wanting the product category, not a missing feature)

**Count: 3 deals** where the tag clearly disagrees with the free-text reason.

(Edge-case note: Deal-5E64CE tagged "Doing nothing" but reason is "locked in Nectar contract thru Oct 2027" -- this is really a timing/switching-cost story, but the tag isn't *clearly* wrong since staying put = doing nothing with Bonusly. Not counted as a clear disagreement.)

---

### Two Patterns Most Worth Acting On

**Pattern 1 -- The MIA Black Hole (33% of all losses, $268K+ aggregate amount)**

33 deals (36.7%) fell into "no decision," and 23 of those are pure MIA/unresponsive. These deals died without any articulated reason -- meaning there was no discovery of the real objection, no negotiation, and no relationship capital built. The MIA deals include some high-value ones: Deal-0F96AA ($76.8K), Deal-EECC02 ($66.7K), Deal-7B2236 ($72K), Deal-981AD4 ($36.9K), Deal-C33D91 ($72K), Deal-B4B50F ($21.1K).

Action: Earlier and harder qualification gates before allowing deals to age into MIA territory. If a deal goes 14 days without a response, force either a break-up call or closed-lost with a captured reason -- don't let it rot into MIA. The 23 MIA deals represent ~$377K in pipeline that vanished with zero learnings.

**Pattern 2 -- Competitor Feature Differentiation (30% of losses, 27 deals)**

Of the 27 competitor losses, at least 8 explicitly cite capability gaps that tipped the decision:
- Deal-F97C37: competitor had "more diversified offerings beyond rewards & recognition"
- Deal-DDAB52 (Rippl): "a lot more at the same cost"
- Deal-242273: competitors could "digitize internal points currency + onsite facility spend"
- Deal-DC77FE: competitor offered more customization ("label points as dollars")
- Deal-A2C349 (Awardco): kept them by adding "surveying functionality"
- Deal-422BA6: ADP partnership gave "pre-built integrations, dedicated contacts"
- Deal-3618CC: "Wanted Surveys"
- Deal-5AD03E: "Wanted more defined budget access"

The recurring theme: competitors are winning on **platform breadth** (surveys, points currency, onsite spend, deeper HRS integrations) not on price. Several reasons even say price was NOT the factor (Deal-DC77FE: "competitive in price so that wasn't a factor"; Deal-242273: both top vendors could meet core need, differentiator was the add-on capability).

Action: Build and loudly position a concrete integrations/partnership roadmap (especially ADP/HRIS embedding) and accelerate any surveys-or-bundling development. Arm reps with talk tracks that address "we're rewards-focused" head-on -- either by partnering to fill gaps or by sharpening the best-of-breed value prop so it reads as a strength, not a limitation. The 27 competitor losses represent ~$516K in pipeline where a capability narrative might have changed the outcome.
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 $0.0646 · 404s · in 6,360 / out 5,642 tok
✓ json_returned✓ tier_vocabulary✓ tier_counts_sum✓ risky_deals_listed✓ no_lock_violation_claimed

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
```json
{
  "tier_counts": {
    "LOCK": 6,
    "ACTION": 14,
    "BUILD": 98,
    "REVIVE": 3,
    "WATCH": 34,
    "RISKY": 1
  },
  "tier_examples": {
    "LOCK": ["Deal-25F752", "Deal-D348E1", "Deal-C26D20"],
    "ACTION": ["Deal-E53952", "Deal-5408B0", "Deal-9AAE5F"],
    "BUILD": ["Deal-499BF6", "Deal-C6FE92", "Deal-D73B89"],
    "REVIVE": ["Deal-0660B4", "Deal-2D1F1B", "Deal-7BBDFA"],
    "WATCH": ["Deal-B7EBD1", "Deal-6787C2", "Deal-A5E80A"],
    "RISKY": ["Deal-BA571A"]
  },
  "risky_deals": ["Deal-BA571A"],
  "lock_violations": 0,
  "pipeline_shape": "This is a classic top-heavy funnel: 71% of deals (105/156) and 78% of total value ($1.83M/$2.31M) sit in DS1–DS3 as PIPELINE, while only 15% (24 deals) have reached late-stage (DS4/DS5). Only 35% of deals show any meetings activity in the last 30 days, meaning the bulk of pipeline is engagement-light early-stage volume. The single RISKY deal (Deal-BA571A) is a BEST_CASE/DS4 with $1,080 amount that has zero recent meetings, stale contact (19 days), and only 5 total engagements — its forecast category overstates the evidence. The 3 REVIVE candidates are high-value or late-stage deals that have gone cold and need re-engagement. The 6 LOCKs are genuinely tight: all at DS4/DS5 with COMMIT/BEST_CASE, meetings in the last 30 days, and contact within 7 days."
}
```
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 $0.0126 · 70s · in 1,872 / out 1,615 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 — HR team of three cannot keep up manually"
    ],
    "pain_points": [
      "HR team of three cannot keep up with manual anniversary/birthday awards",
      "Tracking everything in a spreadsheet — people slip through the cracks"
    ],
    "stakeholders": ["Prospect (VP People)", "Prospect (HR Admin)"],
    "budget_signal": "$40k earmarked for engagement tools this fiscal year",
    "timeline_signal": "Live before open enrollment in November",
    "competitor_mentioned": "Achievers",
    "next_step": "Security review on September 12",
    "objections": [
      "Need SSO and audit logs for IT to sign off",
      "Achievers evaluated last year — was too heavy for their team size"
    ],
    "confidence": "HIGH"
  },
  {
    "transcript_id": "TX-002",
    "deal_alias": "Deal-70BB30",
    "why_buys": [
      "Tie recognition to retention for hourly workforce — regretted turnover over 30%"
    ],
    "pain_points": [
      "Regretted turnover in hourly workforce is over 30%"
    ],
    "stakeholders": ["Prospect (Head of Total Rewards)", "Prospect (CFO)"],
    "budget_signal": "$25k pilot budget approved by finance for this quarter",
    "timeline_signal": "Decision by end of September",
    "competitor_mentioned": null,
    "next_step": "Send pilot agreement — route to legal this week",
    "objections": [
      "Integration with Workday has to be rock solid — CFO's one condition"
    ],
    "confidence": "HIGH"
  },
  {
    "transcript_id": "TX-003",
    "deal_alias": "Deal-530B50",
    "why_buys": [
      "Make recognition visible across 12 retail locations"
    ],
    "pain_points": [
      "Store managers have zero budget autonomy for on-the-spot recognition",
      "Need CEO buy-in before anything moves forward — she decides anything people-related"
    ],
    "stakeholders": ["Prospect (People Ops Manager)", "CEO (not on call, but decision-maker)"],
    "budget_signal": null,
    "timeline_signal": "No rush until Q1",
    "competitor_mentioned": "Bucketlist",
    "next_step": "Schedule a call with CEO — prospect will send two times",
    "objections": [
      "CEO used Bucketlist at her last company and liked it",
      "CEO has to be sold first — she decides anything people-related",
      "No urgency until Q1"
    ],
    "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 talk to HRIS"
    ],
    "pain_points": [
      "Paying for three separate recognition tools — none integrate with HRIS",
      "Procurement cycle runs 6–8 weeks minimum",
      "Security review took three months for last vendor — IT Security Lead's hesitation"
    ],
    "stakeholders": ["Prospect (VP People)", "Prospect (IT Security Lead)"],
    "budget_signal": "Under $15k annually can be approved without going to board",
    "timeline_signal": null,
    "competitor_mentioned": null,
    "next_step": null,
    "objections": [
      "Procurement cycle runs 6–8 weeks minimum",
      "Security review took three months for previous vendor — hesitation from IT Security Lead",
      "CFO calendar check needed — no promises on follow-up"
    ],
    "confidence": "MEDIUM"
  },
  {
    "transcript_id": "TX-005",
    "deal_alias": "Deal-F8767A",
    "why_buys": [
      "Automate service milestones",
      "Get analytics on recognition equity across departments"
    ],
    "pain_points": [
      "Night-shift teams feel invisible — engagement scores run 20 points lower",
      "Exec team skeptical after failed rollout two years ago",
      "Mid-pilot with Nectar — would need to beat that experience"
    ],
    "stakeholders": ["Prospect (HR Director)", "Prospect (People Ops Coordinator)"],
    "budget_signal": "$12k approved under engagement line",
    "timeline_signal": "Running before January all-hands",
    "competitor_mentioned": "Nectar",
    "next_step": "Present to exec team on October 2",
    "objections": [
      "Mid-pilot with Nectar — need to beat that experience",
      "Exec team skeptical after a failed rollout two years ago"
    ],
    "confidence": "HIGH"
  },
  {
    "transcript_id": "TX-006",
    "deal_alias": "Deal-EE195F",
    "why_buys": [
      "Cut admin time on service awards"
    ],
    "pain_points": [
      "Spends five hours a month ordering and shipping plaques manually",
      "COO usually prefers building things in-house"
    ],
    "stakeholders": ["Prospect (HR Manager)", "COO (not on call, but decision-maker)"],
    "budget_signal": "Budget isn't the issue — time is (i.e., no explicit amount stated)",
    "timeline_signal": "Q1 start realistic after FY reset in February",
    "competitor_mentioned": null,
    "next_step": "Send one-page overview — forward to COO this week",
    "objections": [
      "COO usually prefers building things in-house",
      "Comparing against doing it internally (build vs. buy)"
    ],
    "confidence": "MEDIUM"
  }
]
```

Quick tally across the 6 transcripts:

| Metric | Count |
|---|---|
| Total transcripts | 6 |
| Explicit budget signals | 4 of 6 (TX-003, TX-006 lack dollar amounts) |
| Explicit timelines | 5 of 6 (TX-04 has none) |
| Competitors raised by prospect | 3: Achievers (TX-01), Bucketlist (TX-03), Nectar (TX-05) |
| Agreed next steps | 5 of 6 (TX-04 got "maybe/no promises") |
| HIGH confidence | 3 (TX-01, TX-02, TX-05 — clear budget + timeline + next step) |
| MEDIUM confidence | 3 (TX-03 missing budget; TX-04 no timeline/next step; TX-06 COO build-bias risk) |
next-to-closePASS 1.00

? The Question

Which 3 deals are closest to signature right now? Use the CRM data and the Slack thread; Slack often has the real status before the CRM does. For each: deal, amount, why it is close, what is left. Under 250 words.
cost $0.0088 · 49s · in 1,210 / out 860 tok
✓ exactly_three✓ top3_named✓ slack_first✓ blocked_excluded

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
**Top 3 Deals Closest to Signature**

**1. Deal-547B2B — $11,200 — Alex Franklin**
Close date: 2026-09-11. Slack confirms redlines came back clean, signing page is already out, and their VP People told Alex they are signing tomorrow (Sept 5). This is signature-imminent — nothing material left except ink.

**2. Deal-403845 — $9,000 — Alex Franklin**
Close date: 2026-09-11. DS5/COMMIT in CRM. Dana Mercer confirmed on Slack that it's "moving fine" and the order form is currently with their finance team. Remaining step: finance approval/sign-off.

**3. Deal-A2B47C — $6,360 — Alex Franklin**
Close date: 2026-09-11. DS5/COMMIT in CRM. Alex flagged on Slack as "still warm, just normal legal-review pace" — no blockers, proceeding through standard legal review. Remaining step: legal sign-off.

---

Honorable mention: Deal-B7EBD1 ($9,000, DS5/COMMIT, close 2026-09-10) has the earliest close date of any COMMIT deal but zero Slack signal — worth a quick ping to Dana to confirm status.

Note: Deal-2465CE ($5,400) looks commit-eligible in CRM but Dana explicitly pulled it — champion departed, procurement frozen, realistically Q4 now. It is excluded.
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 $0.0219 · 24s · in 16,819 / out 420 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
**True Product Gaps from Active Deals**

---

**1. SCIM User Provisioning**
- Deal: Deal-EC3025
- Prospect quote: "We need SCIM user provisioning — our security team will not approve any tool that requires manual account management."
- Classification: **REAL GAP** — Product docs confirm "SCIM user provisioning ... [is] NOT currently listed as a supported capability."

**2. ADP Workforce Now HRIS Integration**
- Deal: Deal-D0D6B5
- Prospect quote: "Our payroll runs on ADP Workforce Now and I don't see ADP anywhere in your integration list — that's a dealbreaker for us."
- Classification: **REAL GAP** — Product docs confirm "ADP Workforce Now integrations are NOT currently listed as supported capabilities." Supported HRIS list: Workday, BambooHR, Gusto, Rippling only.

---

**Excluded (not real gaps):**

| Transcript | Deal | Prospect Line | Why Excluded |
|---|---|---|---|
| TG-003 | Deal-CFE7F4 | Custom report combining recognition frequency with turnover | **Plan gate** — custom report builder exists on Enterprise tier; prospect is on lower tier |
| TG-004 | Deal-84DBA6 | Managers haven't given recognition; nobody trained them | **Rollout/enablement issue** — Slack integration is available on all plans; gap is adoption/training, not product |
| TG-005 | Deal-36C33F | (No prospect-raised gap) | Prospect said web version is fine; mobile app was volunteered by rep, not raised as a gap by prospect |

---

**Summary: 2 true product gaps**

1. **SCIM provisioning missing** — Deal-EC3025 (security gate)
2. **ADP Workforce Now integration missing** — Deal-D0D6B5 (dealbreaker)
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 $0.1856 · 604s · in 24,089 / out 23,461 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
STALE DEALS REPORT — Snapshot: 2026-09-05 | Cutoff: <= 2026-08-29 (7+ days)
Recency computed from engagements table (last_email / last_call / last_meeting)

====================================================================================================

  OWNER: Dana Mercer  —  14 stale deals | $261,645

  ALIAS            STAGE        AMOUNT   LAST CONTACT  DAYS AGO
  -------------- ---- ------------ ------------ --------
  Deal-44EA29      DS2         $60,000     2026-08-26        10
  Deal-E51FB7      DS2         $43,875     2026-08-24        12
  Deal-B42F46      DS1         $27,000     2026-08-17        19
  Deal-BA3DDC      DS3         $23,400     2026-08-21        15
  Deal-9DDE86      DS2         $20,000     2026-08-21        15
  Deal-215CCA      DS3         $18,900     2026-08-19        17
  Deal-5EED42      DS3         $16,250     2026-08-25        11
  Deal-57887A      DS2         $15,000     2026-08-28         8
  Deal-B7EBD1      DS5          $9,000     2026-08-20        16
  Deal-3974EB      DS4          $9,000     2026-08-28         8
  Deal-F40F04      DS2          $8,100     2026-08-21        15
  Deal-87DDD1      DS1          $5,000     2026-08-17        19
  Deal-F336B6      DS3          $4,200     2026-08-21        15
  Deal-0660B4      DS4          $1,920     2026-08-20        16

====================================================================================================

  OWNER: Alex Franklin  —  19 stale deals | $109,536

  ALIAS            STAGE        AMOUNT   LAST CONTACT  DAYS AGO
  -------------- ---- ------------ ------------ --------
  Deal-CC08D1      DS1         $24,000     2026-08-20        16
  Deal-E73427      DS3         $18,000     2026-08-26        10
  Deal-885F45      DS2          $9,300     2026-08-24        12
  Deal-C2FF3C      DS1          $8,316     2026-08-26        10
  Deal-3EED2C      DS2          $7,200        (never)       N/A
  Deal-0D2F7A      DS3          $5,100     2026-08-24        12
  Deal-6C60D4      DS3          $4,800     2026-08-24        12
  Deal-13FEBD      DS2          $4,680     2026-08-24        12
  Deal-9D0060      DS3          $3,840     2026-08-24        12
  Deal-690476      DS2          $3,600     2026-08-18        18
  Deal-C6D97A      DS4          $3,240     2026-08-28         8
  Deal-EE195F      DS3          $3,120     2026-08-28         8
  Deal-278DEC      DS3          $2,700     2026-08-28         8
  Deal-635B8E      DS3          $2,600     2026-08-18        18
  Deal-6883F3      DS1          $2,400     2026-08-20        16
  Deal-4A13AD      DS3          $2,160     2026-08-10        26
  Deal-F67D31      DS2          $1,800     2026-08-28         8
  Deal-5FDCE4      DS3          $1,600     2026-08-24        12
  Deal-BA571A      DS4          $1,080     2026-08-18        18

====================================================================================================

  OWNER: Cole Ingram  —  18 stale deals | $252,905.03

  ALIAS            STAGE        AMOUNT   LAST CONTACT  DAYS AGO
  -------------- ---- ------------ ------------ --------
  Deal-D04904      DS2      $58,529.25     2026-08-25        11
  Deal-B25F40      DS3         $40,000     2026-08-28         8
  Deal-813836      DS2         $32,175     2026-08-25        11
  Deal-1BA595      DS2         $31,750     2026-08-25        11
  Deal-CFE1E8      DS3         $18,000     2026-08-25        11
  Deal-CD47A6      DS2         $12,168     2026-08-25        11
  Deal-627646      DS3         $11,193     2026-08-25        11
  Deal-FF809F      DS2       $7,781.20     2026-08-25        11
  Deal-AF932D      DS2       $7,225.40     2026-08-25        11
  Deal-A71728      DS2       $6,947.50     2026-08-25        11
  Deal-8BC9F5      DS2          $5,616     2026-08-26        10
  Deal-175395      DS3       $4,779.88     2026-08-25        11
  Deal-481E24      DS3          $4,140     2026-08-26        10
  Deal-C7F9BF      DS2          $3,360     2026-08-25        11
  Deal-2F3A66      DS3       $3,334.80     2026-08-25        11
  Deal-342E96      DS2          $2,700     2026-08-12        24
  Deal-E568D5      DS3          $1,875     2026-08-25        11
  Deal-FD9F4E      DS5          $1,330     2026-08-26        10

====================================================================================================

  OWNER: Bryce Harmon  —  13 stale deals | $626,243

  ALIAS            STAGE        AMOUNT   LAST CONTACT  DAYS AGO
  -------------- ---- ------------ ------------ --------
  Deal-2D1F1B      DS1        $240,000     2026-06-16        81
  Deal-66D1FC      DS1         $99,000     2026-08-20        16
  Deal-950043      DS1         $70,000     2026-08-17        19
  Deal-B23205      DS1         $45,000     2026-08-20        16
  Deal-7BBDFA      DS3         $37,440     2026-07-21        46
  Deal-332637      DS2         $36,000     2026-08-27         9
  Deal-1BEEBF      DS1         $31,500     2026-08-17        19
  Deal-C5658B      DS1         $23,400     2026-08-20        16
  Deal-40522D      DS3         $21,000     2026-08-17        19
  Deal-F0EBBB      DS3         $11,400     2026-08-12        24
  Deal-E25A09      DS1          $6,000     2026-08-27         9
  Deal-C9C286      DS2          $5,502     2026-08-27         9
  Deal-012CB1      DS1              $1     2026-08-13        23

====================================================================================================

  OWNER: Elena Sinclair  —  1 stale deal | $2,100

  ALIAS            STAGE        AMOUNT   LAST CONTACT  DAYS AGO
  -------------- ---- ------------ ------------ --------
  Deal-57FF13      DS1          $2,100        (never)       N/A

====================================================================================================

SUMMARY BY OWNER:
  Bryce Harmon:        13 stale deals   |   $626,243
  Dana Mercer:        14 stale deals   |   $261,645
  Cole Ingram: 18 stale deals |   $252,905.03
  Alex Franklin:     19 stale deals   |   $109,536
  Elena Sinclair:      1 stale deal    |     $2,100
  ──────────────────────────────────────────────
  TOTAL:             65 stale deals   | $1,252,429.03

NOTES:
- Farid Osman has 0 stale deals (all 5 of his open deals had engagement within the last 7 days).
- 2 deals have NO engagement record at all (no email/call/meeting ever logged):
    Deal-3EED2C (Alex Franklin, $7,200)
    Deal-57FF13 (Elena Sinclair, $2,100)
- The single worst-offender by days-since-contact is Deal-2D1F1B (Bryce Harmon, $240,000) at 81 days since any email/call/meeting.
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 $0.0263 · 180s · in 2,027 / out 1,811 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
**Snapshot: 2026-09-05 | L30D = 2026-08-06 to 2026-09-05**

Per-rep activity mix & DS2 efficiency:

  Owner               Emails  Calls  Mtgs  Total   E%     C%    M%   DS2 entries  Act/DS2
  ────────────────────────────────────────────────────────────────────────────────────────────
  Bryce Harmon             162      0    43     205  79.0%   0.0% 21.0%       4        51.2
  Dana Mercer              84     18    11     113  74.3%  15.9%  9.7%       1       113.0
  Alex Franklin          307     36    41     384  79.9%   9.4% 10.7%      18        21.3
  Cole Ingram      96     14     1     111  86.5%  12.6%  0.9%       2        55.5
  Farid Osman             38      0    34      72  52.8%   0.0% 47.2%       1        72.0

  Elena Sinclair — no engagements data; 0 DS2 entries in window.

Ranked by efficiency (lowest activities per DS2 entry):

  #  Owner              Total Act  DS2 new  Act/DS2
  1  Alex Franklin            384       18     21.3
  2  Bryce Harmon               205        4     51.2
  3  Cole Ingram       111        2     55.5
  4  Farid Osman               72        1     72.0
  5  Dana Mercer               113        1    113.0

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

They are the same person.
ae-qtd-scorecarddiscarded

? 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.

✓ 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 answer

not yet run — check back after the runners complete
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 $0.0448 · 178s · in 17,966 / out 5,816 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
I'll work through this systematically using the provided data.

**Reference date:** 2026-09-08. Active = engaged on or after **2026-07-10** (last 60 days) AND `is_former = false`.

---

## FLAGGED DEALS (11 of 14 deals flagged)

---

### 1. Deal-EC3025 (C-FDD0C7) — SINGLE-THREADED

Active contacts (1):
| Contact | Title | Persona | Last Engaged |
|---------|-------|---------|-------------|
| CT-047C54 | Head of Employee Experience | champion | 2026-09-02 |

Excluded: CT-F2C1AE (economic buyer) — is_former = true

Amount: **not provided in source data**
Stage: **not provided in source data**
Active contact count: **1**
Personas present: **champion**
Personas missing: **economic buyer, HR admin, IT security, finance**
Most valuable persona to add: **economic buyer** (no budget holder engaged; former EB is gone)
On-file unengaged contact: **CT-6827DB, Chief People Officer, economic buyer**

---

### 2. Deal-92D97D (C-E23238) — SINGLE-THREADED

Active contacts (1):
| Contact | Title | Persona | Last Engaged |
|---------|-------|---------|-------------|
| CT-01F5B4 | HRIS Manager | HR admin | 2026-08-28 |

Excluded: CT-A902AE (champion) — last_engaged 2026-06-01 (91 days ago, outside 60-day window)

Amount: **not provided**
Stage: **not provided**
Active contact count: **1**
Personas present: **HR admin**
Personas missing: **economic buyer, champion, IT security, finance**
Most valuable persona to add: **champion** (only an HR admin is engaged — no internal advocate or decision maker)
On-file unengaged contact: **none on file**

---

### 3. Deal-50D386 (C-EB10E4) — UNDER-THREADED (2 contacts, 2 personas)

Active contacts (2):
| Contact | Title | Persona | Last Engaged |
|---------|-------|---------|-------------|
| CT-AA41B2 | Head of Employee Experience | champion | 2026-09-01 |
| CT-B9C35B | HRIS Manager | HR admin | 2026-08-25 |

Amount: **not provided**
Stage: **not provided**
Active contact count: **2**
Personas present: **champion, HR admin**
Personas missing: **economic buyer, IT security, finance**
Most valuable persona to add: **economic buyer** (champion + admin engaged but no signatory)
On-file unengaged contact: **CT-A1C4B3, Chief People Officer, economic buyer**

---

### 4. Deal-D0D6B5 (C-32918E) — UNDER-THREADED (3 contacts, ALL same persona)

Active contacts (3):
| Contact | Title | Persona | Last Engaged |
|---------|-------|---------|-------------|
| CT-87CED4 | People Ops Manager | champion | 2026-09-02 |
| CT-DE6D7C | Head of Employee Experience | champion | 2026-08-19 |
| CT-FD70B2 | Head of Employee Experience | champion | 2026-08-07 |

Amount: **not provided**
Stage: **not provided**
Active contact count: **3**
Personas present: **champion (only)**
Personas missing: **economic buyer, HR admin, IT security, finance**
Most valuable persona to add: **economic buyer** (three champions but zero budget holders — deal can't close without a signer)
On-file unengaged contact: **CT-1FA4DB, Chief People Officer, economic buyer**

---

### 5. Deal-5BFE3B (C-535D36) — UNDER-THREADED (2 contacts, ALL same persona)

Active contacts (2):
| Contact | Title | Persona | Last Engaged |
|---------|-------|---------|-------------|
| CT-57123B | People Ops Manager | champion | 2026-08-31 |
| CT-5CE757 | Head of Employee Experience | champion | 2026-08-12 |

Amount: **not provided**
Stage: **not provided**
Active contact count: **2**
Personas present: **champion (only)**
Personas missing: **economic buyer, HR admin, IT security, finance**
Most valuable persona to add: **economic buyer** (two champions, no other personas represented)
On-file unengaged contact: **none on file**

---

### 6. Deal-36C33F (C-077A0E) — SINGLE-THREADED

Active contacts (1):
| Contact | Title | Persona | Last Engaged |
|---------|-------|---------|-------------|
| CT-4FE556 | IT Security Lead | IT security | 2026-08-15 |

Excluded: CT-405B45 (champion) — is_former = true; CT-86B22F (economic buyer) — is_former = true

Amount: **not provided**
Stage: **not provided**
Active contact count: **1**
Personas present: **IT security (only)**
Personas missing: **economic buyer, champion, HR admin, finance**
Most valuable persona to add: **champion** (only technical evaluator alive — both champion and EB are former, need new internal sponsor first)
On-file unengaged contact: **CT-1DB73E, Chief People Officer, economic buyer** (note: this is an EB, but champion is higher priority to rebuild from scratch; still worth engaging as dual-purpose)

---

### 7. Deal-885F45 (C-5E8EFB) — UNDER-THREADED (2 contacts, 2 personas)

Active contacts (2):
| Contact | Title | Persona | Last Engaged |
|---------|-------|---------|-------------|
| CT-51C81E | VP People | economic buyer | 2026-08-26 |
| CT-D9A0E8 | People Ops Manager | champion | 2026-08-11 |

Amount: **not provided**
Stage: **not provided**
Active contact count: **2**
Personas present: **economic buyer, champion**
Personas missing: **HR admin, IT security, finance**
Most valuable persona to add: **IT security** (EB + champion in place; security is typically the #1 blocker in evaluation stages)
On-file unengaged contact: **CT-B3F25D, IT Security Lead, IT security**

---

### 8. Deal-FCBE5B (C-737030) — SINGLE-THREADED

Active contacts (1):
| Contact | Title | Persona | Last Engaged |
|---------|-------|---------|-------------|
| CT-4A5317 | People Ops Manager | champion | 2026-08-29 |

Amount: **not provided**
Stage: **not provided**
Active contact count: **1**
Personas present: **champion (only)**
Personas missing: **economic buyer, HR admin, IT security, finance**
Most valuable persona to add: **economic buyer**
On-file unengaged contact: **none on file**

---

### 9. Deal-5408B0 (C-2AE3AA) — UNDER-THREADED (2 contacts, 2 personas)

Active contacts (2):
| Contact | Title | Persona | Last Engaged |
|---------|-------|---------|-------------|
| CT-D33AE4 | Head of Employee Experience | champion | 2026-09-01 |
| CT-8742FD | HRIS Manager | HR admin | 2026-08-18 |

Amount: **not provided**
Stage: **not provided**
Active contact count: **2**
Personas present: **champion, HR admin**
Personas missing: **economic buyer, IT security, finance**
Most valuable persona to add: **economic buyer**
On-file unengaged contact: **CT-07FA76, Chief People Officer, economic buyer**

---

### 10. Deal-C6D97A (C-5A8FC2) — UNDER-THREADED (3 contacts, ALL same persona)

Active contacts (3):
| Contact | Title | Persona | Last Engaged |
|---------|-------|---------|-------------|
| CT-223DDC | People Ops Manager | champion | 2026-08-31 |
| CT-B03555 | Head of Employee Experience | champion | 2026-08-20 |
| CT-4E8A2B | People Ops Manager | champion | 2026-08-05 |

Amount: **not provided**
Stage: **not provided**
Active contact count: **3**
Personas present: **champion (only)**
Personas missing: **economic buyer, HR admin, IT security, finance**
Most valuable persona to add: **economic buyer** (classic champion-only echo chamber — three advocates, zero signers)
On-file unengaged contact: **none on file**

---

### 11. Deal-F9A08A (C-0D15DF) — SINGLE-THREADED

Active contacts (1):
| Contact | Title | Persona | Last Engaged |
|---------|-------|---------|-------------|
| CT-931B10 | Head of Employee Experience | champion | 2026-09-03 |

Excluded: CT-913581 (economic buyer) — last_engaged 2026-06-20 (79 days ago, outside 60-day window)

Amount: **not provided**
Stage: **not provided**
Active contact count: **1**
Personas present: **champion (only)**
Personas missing: **economic buyer, HR admin, IT security, finance**
Most valuable persona to add: **economic buyer** (EB exists on file but hasn't engaged in 79 days — re-engage or find replacement)
On-file unengaged contact: **CT-697541, Chief People Officer, economic buyer** (this may be the same person as stale CT-913581 or a different CPO — key action either way)

---

## SUMMARY TABLE

```
Deal           Company     Flag            Actives  Personas Present              Missing Personas                           Unengaged On-File?
------------   ---------- --------------- ------- ------------------------------ ------------------------------------------ ------------------
Deal-EC3025    C-FDD0C7    SINGLE          1       champion                      EB, HR, ITsec, fin                       CT-6827DB (EB)
Deal-92D97D    C-E23238    SINGLE          1       HR admin                      EB, champ, ITsec, fin                    none
Deal-50D386    C-EB10E4    UNDER (<3)      2       champ, HR                     EB, ITsec, fin                           CT-A1C4B3 (EB)
Deal-D0D6B5    C-32918E    UNDER (1 perso  3       champ (only)                  EB, HR, ITsec, fin                       CT-1FA4DB (EB)
Deal-5BFE3B    C-535D36    UNDER (<3+1per  2       champ (only)                  EB, HR, ITsec, fin                       none
Deal-36C33F    C-077A0E    SINGLE          1       IT security                   EB, champ, HR, fin                       CT-1DB73E (EB)
Deal-885F45    C-5E8EFB    UNDER (<3)      2       EB, champ                     HR, ITsec, fin                           CT-B3F25D (ITsec)
Deal-FCBE5B    C-737030    SINGLE          1       champ                         EB, HR, ITsec, fin                       none
Deal-5408B0    C-2AE3AA    UNDER (<3)      2       champ, HR                     EB, ITsec, fin                           CT-07FA76 (EB)
Deal-C6D97A    C-5A8FC2    UNDER (1 perso  3       champ (only)                  EB, HR, ITsec, fin                       none
Deal-F9A08A    C-0D15DF    SINGLE          1       champ                         EB, HR, ITsec, fin                       CT-697541 (EB)
```

---

## KEY FINDINGS

- **11 of 14 deals (79%)** are flagged as single-threaded or under-threaded
- **5 deals** are single-threaded (1 active contact): EC3025, 92D97D, 36C33F, FCBE5B, F9A08A
- **3 deals** are multi-contact but champion-only (persona monoculture): D0D6B5, 5BFE3B, C6D97A
- **Economic buyer is the #1 gap** — missing from 10 of 11 flagged deals; it is the top-priority add for 9 of 11
- **6 of 11** flagged deals have an on-file unengaged contact who fills the highest-value gap (all 6 are EBs except Deal-885F45 which has an IT security lead)
- **Deal-36C33F** is the most fragile: champion AND economic buyer are both marked former; only IT security remains active
- Amount and stage were not in the provided files — if you attach a deals table with those fields, I can prioritize by value-at-risk and stage-appropriate persona strategy
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 $0.0335 · 102s · in 18,812 / out 2,723 tok
✓ objection_count✓ next_step_rate✓ competitors✓ coaching✓ quote_used

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
## CALL ANALYSIS — ALEX FRANKLIN, LAST 10 CALLS (TT-001 → TT-010)

---

### 1. WHAT HE LEADS WITH (FIRST 5 MINUTES)

**8 of 10 calls** — identical social-proof opener at minute 0:
> "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."

**TT-004 (Deal-403845)** diverged:
> "I put together a short agenda — security review first, then pricing."

**TT-009 (Deal-1E2498)** diverged:
> "You asked for straight pricing last time, so let's start there."

**TT-005 (Deal-C61CF7)** added a competitive jab at minute 2:
> "And unlike Workhuman, our pricing includes the full rewards catalog with no extra margin."

---

### 2. THREE MOST COMMON OBJECTIONS + HANDLING

| # | Objection | Count | How Alex Handles It |
|---|-----------|-------|----------------------|
| 1 | **"Budget is locked until next fiscal year"** | 4x (TT-001, TT-003, TT-006, TT-010) | "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." |
| 2 | **"Revisit it next quarter" / open enrollment crunch** | 3x (TT-002, TT-005, TT-008) | "Makes sense. What if we scope a 90-day pilot with one department so you have internal data before next quarter's planning?" |
| 3 | **"We already do recognition with a spreadsheet and quarterly gift cards"** | 3x (TT-004, TT-007, TT-009) | "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." |

Two secondary objections each appeared once:
- TT-004, TT-010: Committee/stakeholder gate ("I'll leave it with you" / "Understood, thanks for the candor.") — **no pivot, call ends softly**
- TT-007: "I need to think about it — there's no urgency on our side." → "Fair enough." — **no urgency create**

---

### 3. CONCRETE NEXT-STEP AGREEMENT RATE

Calls with a confirmed next step: **TT-001, TT-002, TT-003, TT-005, TT-006, TT-008, TT-009** = **7**

Calls without: **TT-004, TT-007, TT-010** = **3**

Rate: **7/10 = 70%**

All 7 agreements were the same outcome:
> "Yes, Thursday at 2pm works — send the invite and I'll bring our HRIS manager."

---

### 4. COMPETITORS RAISED BY PROSPECTS

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

Workhuman was mentioned by **Alex** (TT-005, minute 2), not raised by the prospect — so it does not qualify per your criterion.

Total competitors raised by prospects: **2**

---

### COACHING NOTES

1. **The three calls that lost the next step (TT-004, TT-007, TT-010) all ended on a soft deferral — "I'll leave it with you," "Fair enough," "Understood, thanks for the candor."** There was no attempt to seed a smaller ask (e.g., "Would a 15-min intro for your HRIS manager be worth locking before committee?") or attach a timeline ("When should I circle back?"). Compare that to the 7 wins where Alex explicitly asked "Should we lock the next step" — the pattern is clear: he asks boldly on some calls and concedes quietly on others. The difference appears to be whether the prospect's final objection felt like a hard no or a soft maybe, but the data shows the close rate is determined by whether he asks, not by the objection type.

2. **The opener is word-for-word identical on 8/10 calls, and the next-step line is verbatim on all 7 wins.** This signals strong consistency — which is good for repeatability — but it also means every prospect hears the same 400-retailer story even when it may not fit (TT-004 and TT-009 both started differently, suggesting Alex sometimes adapts, then snaps back to script). The two competitor mentions (Awardco, Kudos) were both handled competently by pivoting to differentiation (automation/analytics), but only after the prospect raised them — there's no competitive landmine proactively set before the prospect goes shopping. Consider pre-empting the "we use spreadsheets" objection in the opener itself since it appeared 3 times and always at minute 6.
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 $0.0950 · 204s · in 28,275 / out 12,737 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: COMMIT x 100%  +  BEST_CASE x 35%  +  PIPELINE x 0%

COMMIT total:      $44,729.00   (7 deals)
BEST_CASE total:   $203,565.00  (24 deals)
PIPELINE total:    $201,637.40  (23 deals) — excluded from weighted

WEIGHTED FORECAST:
  $44,729.00 + ($203,565.00 x 0.35)
= $44,729.00 + $71,247.75
= $115,976.75

Excluded (outside Q3): 32 deals totaling $227,575.00

Top 5 BEST_CASE deals inside Q3:
  1. Deal-2D7423 — $38,935 | DS3 | 2026-09-30
  2. Deal-25F752 — $24,000 | DS4 | 2026-09-25
  3. Deal-E53952 — $19,656 | DS4 | 2026-09-30
  4. Deal-5EED42 — $16,250 | DS3 | 2026-09-30
  5. Deal-FA32A0 — $11,116 | DS3 | 2026-09-25

## Data quality
1) Owner field is blank on 85 of 86 deals (only Deal-C9C286 has a name), so deal accountability is effectively absent. 2) Three stage/category mismatches undermine forecast logic: Deal-C61CF7 sits at DS5 (contract stage) but is labeled BEST_CASE rather than COMMIT, while Deal-A5E80A (DS1/discovery) and Deal-499BF6 (DS2) carry COMMIT flags despite being in early stages with near-zero qualification. 3) The why_buys_chars field is zero/empty on 71 of 86 deals (83%), indicating missing or unrecorded buying intent data across most pipeline. 4) One amount uses non-cent precision (Deal-42326B = $2,480.4), suggesting possible data-entry or ETL truncation issues.
aha-moment-2x2discarded

? 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.

✓ Correct Answer (ground truth)

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

Model's answer

not yet run — check back after the runners complete
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 $0.1343 · 307s · in 36,090 / out 16,862 tok
✓ billing_total✓ crm_total✓ variance✗ buckets_sum✓ violations_listed fabricated: C-0D66DFE

✓ 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 — Billing vs CRM as of 2026-09-05
========================================================================

TOTALS

  Billing ARR  (37 active subs, MRR x 12):    $604,739.28
    Active MRR = $50,394.94 x 12 = $604,739.28

  CRM ARR      (39 HubSpot companies):        $603,581.76

  VARIANCE     (CRM - Billing):               $-1,157.52
    (Billing exceeds CRM by $1,157.52)

------------------------------------------------------------------------
VARIANCE DECOMPOSITION (sums exactly to $-1,157.52) ✓
------------------------------------------------------------------------

  Bucket                                              Impact
  -------------------------------------------------- ------------
  1. Missing CRM record (billing-only active)       $-28,449.24
  2. Status mismatch (cancelled in bill, live CRM)  $+13,158.48
  3. Missing from billing (no subscription at all)   $+16,497.24
  4. Matched active per-account diffs               $-2,364.00
     |- Rounding (<=$0.01): 33 accts                     $+0.00
     `- Material (>$0.01): 3 accts                      $-2,364.00
  -------------------------------------------------- ------------
  SUM OF BUCKETS                                  $-1,157.52
  ACTUAL VARIANCE                                 $-1,157.52

  VERIFIED — decomposition sums to variance


MISMATCHED ACCOUNTS + SUGGESTED OWNERS
========================================================================

BILLING-ONLY ACTIVE (no CRM company record)
  C-21629AA4 (SUB-0004)  Billing ARR=$28,449.24
  -> Owner: Data Ops / CRM Admin

STATUS MISMATCH (cancelled in billing, still live in CRM)
  C-0C8323BF (SUB-000E)  CRM ARR=$4,905.24
  -> Owner: Revenue Ops / CS
  C-0DC4FB8C (SUB-000F)  CRM ARR=$8,253.24
  -> Owner: Revenue Ops / CS

MISSING FROM BILLING (CRM record, no subscription exists)
  C-0D5BBE3A  CRM ARR=$16,497.24
  -> Owner: Finance / Billing Ops

MATERIAL PER-ACCOUNT DIFFS (matched active, |diff| > $0.01)
  C-0D66DFE   Bill=$23,184.00  CRM=$23,200.00  Diff=+$16.00 (CRM higher)
  -> Finance (minor — monitor)
  C-0F7269D7  Bill=$26,796.00  CRM=$24,396.  Diff=-$2,400.00 (Billing higher)
  -> Deal Desk / Finance ($2,400 gap ~1.07 mo MRR — possible price change not synced to CRM)
  C-14D70CE0  Bill=$18,180.00  CRM=$18,200.00  Diff=+$20.00 (CRM higher)
  -> Finance (minor — monitor)


TERM VIOLATION CHECK
Rule: term != 12 months requires cf_agreement_end_date populated
------------------------------------------------------------------------
  OK     SUB-000C (C-0DB48281)  term=24mo  end=2027-11-30  status=active
  OK     SUB-001A (C-0FC4DBB8)  term=36mo  end=2027-11-30  status=active
  *VIOLATION*  SUB-0002 (C-1794A52C)  term=24mo  cf_agreement_end_date=BLANK  status=active
  *VIOLATION*  SUB-0019 (C-22170CA1)  term=36mo  cf_agreement_end_date=BLANK  status=active

  Total violations: 2
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 $0.0310 · 151s · in 6,067 / out 3,833 tok
✓ four_kvms✓ biggest_mover_named✓ direction_correct✓ segment_named✓ word_cap

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
**KVM MoM Report: 2026-08 vs 2026-07 (N=30 companies each month)**

| KVM | Aug | Jul | Abs Δ | Rel Δ | Direction |
|---|---|---|---|---|---|
| Giving Rate | 0.6027 | 0.6023 | +0.0004 | +0.07% | UP |
| Redemptions/User | 1.7302 | 1.7300 | +0.0002 | +0.01% | UP |
| 1:1 Meetings Eng. | 0.4338 | 0.4469 | -0.0130 | -2.92% | DOWN |
| Pulse Check Eng. | **0.5086** | **0.6006** | **-0.0920** | **-15.31%** | **DOWN** |

**Largest relative move: Pulse Check Engagement at -15.3%**, and the data does support identifying the driver.

The drop is entirely concentrated in the **enterprise size band**: pulse fell from 0.5500 to 0.2743 (**-50.1%**) across all 10 enterprise accounts (all tier_three). Mid-market (+0.2%) and SMB (-0.2%) were essentially flat. Plan tier adds no signal — every company is tier_three, so it cannot differentiate the driver.
rewards-ytd-sectiondiscarded

? 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.

✓ 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 answer

not yet run — check back after the runners complete
customer-success
churn-save-eligibilityPASS 1.00

? The Question

Which at-risk accounts qualify for a churn-save offer under the documented eligibility rules, what amount is at stake per account and in total, and which play fits each (usage revival, executive touch, commercial concession)? Cite the signal that justifies each play. List accounts that look at risk but do not qualify and why.
cost $0.0668 · 124s · in 23,940 / out 7,243 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

**Eligibility rules applied (all three must pass):**
- R1: health_score < 60
- R2: churn_save_eligible_amount > $0
- R3: renewal_date within 120 days of snapshot 2026-09-05 (i.e., <= 2027-01-03)

---

### QUALIFIED ACCOUNTS — 8 accounts, **$224,601** at stake

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

---

**C-0F6C0F34** — At Stake: $49,707 | HS: 51 | ARR: $86,741
  Renewal: 2026-10-03 (28d out) | Usage: growing | Seats: 308/395 (78%) | Champion: No
  PLAY: Executive Touch
  Signal: champion_active=false -- no internal advocate despite growing usage and decent seat util

**C-0B827671** — At Stake: $25,365 | HS: 56 | ARR: $72,088
  Renewal: 2026-11-14 (70d out) | Usage: declining | Seats: 113/202 (56%) | Champion: Yes
  PLAY: Usage Revival
  Signal: usage_trend_3m=declining with only 56% seat utilization; champion exists to co-drive re-adoption

**C-0B360C78** — At Stake: $35,748 | HS: 57 | ARR: $60,427
  Renewal: 2026-10-28 (53d out) | Usage: growing | Seats: 246/327 (75%) | Champion: Yes
  PLAY: Commercial Concession
  Signal: health_score still <60 despite growing usage AND an active champion -- risk driver is almost certainly commercial/pricing, not product/adoption

**C-0B0F1BAB** — At Stake: $5,494 | HS: 38 | ARR: $15,391
  Renewal: 2026-09-23 (18d out) | Usage: flat | Seats: 238/363 (66%) | Champion: No
  PLAY: Executive Touch
  Signal: champion_active=false + critically low HS=38 with renewal in 18 days -- urgent exec engagement needed

**C-0CA21961** — At Stake: $16,829 | HS: 58 | ARR: $31,501
  Renewal: 2026-12-28 (114d out) | Usage: flat | Seats: 84/325 (26%) | Champion: Yes
  PLAY: Usage Revival
  Signal: catastrophic 26% seat utilization (84/325) with flat trend; champion present to work adoption plan

**C-0E9C27D1** — At Stake: $41,235 | HS: 39 | ARR: $75,093
  Renewal: 2026-09-24 (19d out) | Usage: flat | Seats: 134/157 (85%) | Champion: Yes
  PLAY: Commercial Concession
  Signal: HS=39 (critically low) with $41,235 at stake and renewal in 19 days; 85% seat util rules out adoption -- commercial issue

**C-0CEF69FD** — At Stake: $32,621 | HS: 53 | ARR: $79,324
  Renewal: 2026-11-21 (77d out) | Usage: growing | Seats: 97/136 (71%) | Champion: No
  PLAY: Executive Touch
  Signal: champion_active=false with no internal sponsor; HS=53 and growing usage mask the missing advocate gap

**C-0D3278C7** — At Stake: $17,602 | HS: 54 | ARR: $33,815
  Renewal: 2026-11-12 (68d out) | Usage: declining | Seats: 126/380 (33%) | Champion: Yes
  PLAY: Usage Revival
  Signal: usage_trend_3m=declining with very low 33% seat utilization (126/380); champion can partner on revival

---

### PLAY SUMMARY

| Play | Accounts | Total at Stake |
|------|----------|---------------|
| Executive Touch (no champion) | C-0F6C0F34, C-0B0F1BAB, C-0CEF69FD | $87,822 |
| Usage Revival (declining/flat util + champion) | C-0B827671, C-0CA21961, C-0D3278C7 | $59,796 |
| Commercial Concession (HS low despite good signals) | C-0B360C78, C-0E9C27D1 | $76,983 |

---

### AT-RISK BUT DO NOT QUALIFY -- 7 accounts

**FAIL R2 ($0 eligible amount -- no approved save budget):**

- **C-0BC71BDD** -- HS=55, ARR=$54,515, Elig=$0, renewing 2026-10-27. No churn-save funds allocated.
- **C-0F6694C3** -- HS=43, ARR=$11,243, Elig=$0, renewing 2027-03-21. No churn-save funds allocated.
- **C-0BE96399** -- HS=54, ARR=$52,319, Elig=$0, renewing 2026-10-29. No churn-save funds allocated.
- **C-0FCCD2DF** -- HS=43, ARR=$65,957, Elig=$0, renewing 2027-04-23. No churn-save funds allocated.
- **C-10A56B0F** -- HS=54, ARR=$25,717, Elig=$0, renewing 2026-12-12. No churn-save funds allocated.

**FAIL R3 (renewal > 120 days from snapshot -- outside offer window):**

- **C-0BA71F12** -- HS=52, eligible $6,824, but renews 2027-04-11 (218 days out, exceeds 120d window).
- **C-0F876796** -- HS=47, eligible $19,958, but renews 2027-02-06 (154 days out, exceeds 120d window).

---

### KEY TAKEAWAYS

- 15 of 30 accounts are at-risk (HS < 60)
- Only 8 qualify for funded churn-save offers: **$224,601 total at stake**
- The 2 highest-stake qualifying accounts are both urgent: C-0E9C27D1 ($41,235, renews in 19 days) and C-0F6C0F34 ($49,707, renews in 28 days)
- 5 at-risk accounts have $0 eligible -- these need a budget reallocation request before any offer can be made
- 2 accounts have eligible funds but renew too far out -- queue them for the next eligibility window or request an exception
expansion-kitPASS 1.00

? The Question

A customer replied to outreach (reply included). Build the expansion kit: seat coverage (licensed over headcount), two lines on usage health, headroom in seats and ARR at the current per-seat rate, who replied and whether they can buy, the right buyer from our contacts, and a reply email under 150 words that moves toward the right person without being pushy and cites one real usage fact.
cost $0.0238 · 37s · in 16,466 / out 998 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

150 licensed / 400 headcount = **37.5%** covered (250 seats unlicensed)

## Usage Health

- **Strong upward trajectory:** MAU grew 88 → 126 (+43%) over Mar–Aug 2026, accelerating to a consistent +8 users/mo the last three months (Jun→Jul→Aug).
- **Approaching capacity ceiling:** 126 of 150 licensed seats now active (**84% utilization**); at current +8/mo pace, seats exhaust in ~3 months (Nov/Dec 2026).

## Headroom

| Metric | Value | Arithmetic |
|---|---|---|
| Remaining licensed seats | 24 | 150 − 126 |
| Immediate seat-expansion ARR | $1,440 | 24 seats × ($9,000 ÷ 150) = 24 × $60 |
| Full-headcount expansion ARR | $15,000 | (400 − 150) × $60 = 250 × $60 |
| Current per-seat rate | **$60/yr** | $9,000 ÷ 150 |

## Who Replied / Can They Buy?

**Maria S.**, People Operations Coordinator — replied 2026-09-02. **Cannot buy.** Explicitly stated budget and seat expansion sit elsewhere; offered an introduction.

## Right Buyer

**Dana R., VP People** (last engaged 2026-05-18). Maria named her directly as the decision owner for budget and seat expansion, and noted Dana has been asking about usage numbers lately.

---

## Reply Email (< 150 words)

> Subject: Re: Growing your team's recognition program
>
> Hi Maria,
>
> This is really helpful context — thank you.
>
> That usage trend is great to hear, and the numbers back it up: your active users climbed from 88 to 126 since March, and you're now at 84% of your current license. At that pace, you'll be at capacity inside Q4, so it makes sense for Dana to be looped in sooner rather than later.
>
> An introduction would be great whenever you have a moment — no rush. I'll keep it brief and come with the usage snapshot she's already been asking about.
>
> Thanks again for the steer,
>
> Cole

**Word count: 108 words.**
onboarding-status0.75

? The Question

Prep the CSM for the mid-onboarding call: what is complete (integrations connected, allowance set, admins added, first recognition given, first redemption), what is not, early engagement signals, and the three things to cover on the call. Mark nothing complete without a data field that shows it.
cost $0.0080 · 74s · in 395 / out 924 tok
✓ complete_items✓ incomplete_items✓ three_items✗ grounded

✓ Correct Answer (ground truth)

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

Model's actual answer

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

## Onboarding Milestones

| Milestone | Status | Evidence |
|-----------|--------|----------|
| Slack integration | COMPLETE | integration_slack = 2026-08-12 |
| HRIS integration | NOT COMPLETE | integration_hris is blank/empty |
| Allowance set | COMPLETE | allowance_set = 2026-08-13 |
| Admins added | COMPLETE | admins_added = 2 (value present) |
| First recognition given | COMPLETE | first_recognition_at = 2026-08-15 14:22 |
| First redemption | NOT COMPLETE | first_redemption_at is blank/empty |

**Complete:** 4 of 6
**Not complete:** 2 of 6 (HRIS integration, first redemption)

---

## Early Engagement Signals

- **Signup:** 2026-08-11
- **Days since signup through 2026-09-04:** 24 days
- **Active givers trend (daily):**

```
Day 1 (Aug 11):   3
Day 2 (Aug 12):   3
...
Day 10 (Aug 20):  6
Day 15 (Aug 25):  9
Day 20 (Aug 30): 13  ← peak so far
Day 24 (Sep 04): 15  ← new high
```

- **Growth math:**
  - Started at **3** active givers (day 1)
  - Now at **15** active givers (day 24)
  - Growth: (15 - 3) / 3 = **12 / 3 = 4.0x** (400% increase over 24 days)
  - Most recent 7-day average (Aug 29 – Sep 4): (11 + 13 + 11 + 13 + 13 + 15 + 15) / 7 = **91 / 7 ≈ 13.0** active givers/day
  - Prior 7-day average (Aug 22 – Aug 28): (8 + 9 + 9 + 9 + 11 + 10 + 10) / 7 = **66 / 7 ≈ 9.4** active givers/day
  - Week-over-week active-giver momentum: **+38%** ((13.0 − 9.4) / 9.4)

- **No zero-activity days** — engagement has been continuous since day 1.
- **No regression days** — the lowest value after a higher value is only Aug 16 (4, down from 5) and small single-day dips; the overall trajectory is strictly upward with normal variance.

- **Signal strength: STRONG.** Sustained daily participation, 5x giver growth in 24 days, consistent week-over-week lift.

---

## Three Things to Cover on the Call

1. **HRIS integration — unblock it.** This is the biggest gap. Without HRIS, auto-provisioning/deprovisioning and allowance distribution by department are manual or impossible. Ask what's blocking (security review? vendor approval? missing credentials?) and set a target date.

2. **Redemption adoption — nobody has redeemed yet.** Recognitions are flowing (first one on day 5), but 24 days in with zero redemptions means users either don't know how to redeem, don't see reward options they want, or haven't accumulated enough points. Demo redemption, confirm reward catalog fits this company's culture, and consider announcing a "first redemption" moment internally to drive trial.

3. **Sustain & expand the giver base.** Engagement is strong and growing (3 → 15 givers), but 15 is likely a fraction of total employees. Use this momentum window to: (a) identify non-participating teams/departments via admin reporting, (b) plan a recognition challenge or manager-led nudge for the next 30 days, and (c) confirm whether the 2 admins have visibility into who isn't giving yet. The 38% week-over-week lift proves the product is sticking — now is the time to push breadth before the novelty curve flattens.
renewal-risk-conflicting-datesPASS 1.00

? The Question

Produce a 90-day renewal risk brief. Two systems hold renewal dates and disagree on some accounts; decide which to trust per account and say why (multi-year contracts are known to be wrong in ChurnZero). For every renewal: company, CSM, ARR, date used, seat utilization, 3-month usage trend, risk rating with one sentence of evidence. Flag every disagreement. Close with total ARR renewing and ARR at risk.
cost $0.1239 · 304s · in 38,562 / out 16,377 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
Here's the complete 90-day renewal risk brief:

===================================================================================
  90-DAY RENEWAL RISK BRIEF  |  as of 2026-09-08
===================================================================================

C-0B7D2C30  |  CSM: Dana Mercer       |  ARR: $65,901  |  Renewal: 2026-09-15 (Chargebee) *** DISAGREE ***
         Util: 57.6% (274/476)  |  3mo: down 97->94->84 (net -13)  |  RISK: HIGH
         Evidence: Low seat utilization (57.6%); declining 3mo usage.

C-0BCDB8C2  |  CSM: Cole Ingram|  ARR: $54,427  |  Renewal: 2026-09-18 (Chargebee) *** DISAGREE ***
         Util: 54.7% (232/424)  |  3mo: down 127->118->110 (net -17) |  RISK: HIGH
         Evidence: Low seat utilization (54.7%); steep 3mo usage decline.

C-0D2AB865  |  CSM: Elena Sinclair     |  ARR: $38,022  |  Renewal: 2026-09-22 (Chargebee) *** DISAGREE ***
         Util: 61.4% (250/407)  |  3mo: down 125->117->109 (net -16) |  RISK: HIGH
         Evidence: Low seat utilization (61.4%); steep 3mo usage decline.

C-0BBE3E60  |  CSM: Dana Mercer       |  ARR: $30,993  |  Renewal: 2026-09-26 (Chargebee) *** DISAGREE ***
         Util: 64.9% (74/114)   |  3mo: down 39->35->33 (net -6)    |  RISK: MEDIUM
         Evidence: Low seat utilization (64.9%).

C-0F5D2323  |  CSM: Cole Ingram|  ARR: $90,647  |  Renewal: 2026-09-29 (Chargebee) *** DISAGREE ***
         Util: 28.5% (111/390)  |  3mo: down 20->21->18 (net -2)    |  RISK: HIGH
         Evidence: Critical underutilization (28.5% seated).

C-0EC6999D  |  CSM: Elena Sinclair     |  ARR: $79,419  |  Renewal: 2026-10-03 (Both agree)
         Util: 27.7% (31/112)   |  3mo: down 17->16->15 (net -2)    |  RISK: HIGH
         Evidence: Critical underutilization (27.7% seated).

C-0B20DB64  |  CSM: Dana Mercer       |  ARR: $21,770  |  Renewal: 2026-10-07 (Both agree)
         Util: 56.6% (214/378)  |  3mo: flat 294->298->294 (net 0)  |  RISK: MEDIUM
         Evidence: Low seat utilization (56.6%).

C-0BBC4E7A  |  CSM: Cole Ingram|  ARR: $56,374  |  Renewal: 2026-10-10 (Both agree)
         Util: 67.7% (228/337)  |  3mo: down 142->141->139 (net -3)  |  RISK: LOW
         Evidence: Healthy 67.7% seat util, stable usage, 32d to renewal.

C-0FD551AB  |  CSM: Elena Sinclair     |  ARR: $48,815  |  Renewal: 2026-10-14 (Both agree)
         Util: 55.9% (210/376)  |  3mo: up 123->122->126 (net +3)    |  RISK: MEDIUM
         Evidence: Low seat utilization (55.9%).

C-0F9F8F13  |  CSM: Dana Mercer       |  ARR: $46,230  |  Renewal: 2026-10-18 (Both agree)
         Util: 56.5% (199/352)  |  3mo: down 185->185->182 (net -3)  |  RISK: MEDIUM
         Evidence: Low seat utilization (56.5%).

C-0BC34584  |  CSM: Cole Ingram|  ARR: $16,740  |  Renewal: 2026-10-22 (Both agree)
         Util: 66.2% (327/494)  |  3mo: up 104->104->106 (net +2)    |  RISK: LOW
         Evidence: Healthy 66.2% seat util, rising usage, 44d to renewal.

C-0B7A7546  |  CSM: Elena Sinclair     |  ARR: $35,062  |  Renewal: 2026-10-25 (Both agree)
         Util: 88.8% (182/205)  |  3mo: flat 64->65->63 (net -1)      |  RISK: LOW
         Evidence: Healthy 88.8% seat util, stable usage, 47d to renewal.

C-0B369871  |  CSM: Dana Mercer       |  ARR: $85,128  |  Renewal: 2026-10-29 (Both agree)
         Util: 75.1% (317/422)  |  3mo: up 326->330->333 (net +7)    |  RISK: LOW
         Evidence: Healthy 75.1% seat util, strong adoption curve, 51d to renewal.

C-0B144C78  |  CSM: Cole Ingram|  ARR: $30,899  |  Renewal: 2026-11-02 (Both agree)
         Util: 75.4% (169/224)  |  3mo: up 101->101->106 (net +5)    |  RISK: LOW
         Evidence: Healthy 75.4% seat util, rising usage, 55d to renewal.

C-0FC4DBB8  |  CSM: Elena Sinclair     |  ARR: $94,732  |  Renewal: 2026-11-05 (Both agree)
         Util: 76.7% (356/464)  |  3mo: up 189->191->193 (net +4)    |  RISK: LOW
         Evidence: Healthy 76.7% seat util, steady growth, 58d to renewal.

C-0D5BBE3A  |  CSM: Dana Mercer       |  ARR: $39,740  |  Renewal: 2026-11-09 (Both agree)
         Util: 83.3% (85/102)   |  3mo: up 88->90->91 (net +3)        |  RISK: LOW
         Evidence: Healthy 83.3% seat util, rising usage, 62d to renewal.

C-0FB9D5AF  |  CSM: Cole Ingram|  ARR: $63,158  |  Renewal: 2026-11-13 (Both agree)
         Util: 72.4% (144/199)  |  3mo: up 173->173->176 (net +3)    |  RISK: LOW
         Evidence: Healthy 72.4% seat util, rising usage, 66d to renewal.

C-0B344485  |  CSM: Elena Sinclair     |  ARR: $64,384  |  Renewal: 2026-11-16 (Both agree)
         Util: 78.0% (224/287)  |  3mo: up 238->240->244 (net +6)    |  RISK: LOW
         Evidence: Healthy 78.0% seat util, consistent growth, 69d to renewal.

C-0CB2C1B4  |  CSM: Dana Mercer       |  ARR: $40,628  |  Renewal: 2026-11-20 (Both agree)
         Util: 81.6% (386/473)  |  3mo: up 47->48->49 (net +2)        |  RISK: LOW
         Evidence: Healthy 81.6% seat util, rising usage, 73d to renewal.

C-22170CA1  |  CSM: Cole Ingram|  ARR: $45,646  |  Renewal: 2026-11-24 (Both agree)
         Util: 85.4% (251/294)  |  3mo: up 143->148->146 (net +3)    |  RISK: LOW
         Evidence: Healthy 85.4% seat util, rising usage, 77d to renewal.

--------------------------------------------------------------------------------------------------------------
DATE DISAGREEMENTS (ChurnZero vs Chargebee)
--------------------------------------------------------------------------------------------------------------
  C-0B7D2C30: CZ=2026-09-10 / CB=2026-09-15 --> Using CHARGEBEE: multi-year contract;
               CZ off by 5d and known unreliable for MY terms.
  C-0BCDB8C2: CZ=2027-09-18 / CB=2026-09-18 --> Using CHARGEBEE: multi-year contract;
               CZ off by exactly 1 year (classic MY mis-entry).
  C-0D2AB865: CZ=2026-09-10 / CB=2026-09-22 --> Using CHARGEBEE: multi-year contract;
               CZ off by 12d.
  C-0BBE3E60: CZ=2027-09-26 / CB=2026-09-26 --> Using CHARGEBEE: multi-year contract;
               CZ off by exactly 1 year (classic MY mis-entry).
  C-0F5D2323: CZ=2026-09-10 / CB=2026-09-29 --> Using CHARGEBEE: multi-year contract;
               CZ off by 19d; all 5 disagreements are on multi-year accounts as expected.

All 15 single-year (12-month term) accounts have matching dates between systems.
All 5 date disagreements are exclusively on multi-year (24/36-month) contracts,
consistent with the known ChurnZero multi-year data quality issue.

===================================================================================
TOTAL ARR RENEWING (90-day):  $1,048,715
ARR AT RISK (MED + HIGH):      $476,224  (45% of renewal pool)
  -- HIGH risk only:           $328,416  ($65,901 + $54,427 + $38,022 + $90,647 + $79,419)
  -- MEDIUM risk only:         $147,808  ($30,993 + $21,770 + $48,815 + $46,230)
Accounts in window:            20 of 20
Date disagreements:             5 (all multi-year; all resolved in favor of Chargebee)
===================================================================================

KEY CALL-OUTS:

1. Top 3 by dollar risk: C-0F5D2323 ($90,647 / 28.5% util), C-0EC6999D ($79,419 / 27.7%
   util), C-0B7D2C30 ($65,901 / steep decline). These three alone = $235,987 at HIGH
   risk and all renew within 23 days. Immediate outreach recommended.

2. The two biggest accounts by ARR are polar opposites:
   - WORST:  C-0F5D2323 ($90,647) -- 28.5% util, flat-low usage, renews Sep 29
   - BEST:   C-0FC4DBB8 ($94,732) -- 76.7% util, growing, renews Nov 5

3. Dana Mercer carries the heaviest near-term risk load: 4 of her 6 renewing accounts
   are MED/HIGH risk, representing $209,750 of her $295,621 renewal book (71%).
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 $0.0938 · 209s · in 31,066 / out 11,446 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
Q3 SUPPORT TICKET THEME SYNTHESIS (80 tickets | Jun 1 – Aug 29)

Ranked by ARR exposure (not volume). All 80 tickets categorized; zero uncategorized.

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
THEME #1 — HRIS PROVISIONING FAILURES
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  Count:        12     Share: 15.0%
  Distinct accts: 3    ARR exposed: $114,000
  Sample IDs:   IC-460059, IC-460055
  Accounts:     C-0DDFC9A7 ($48K), C-0B2213A9 ($36K), C-0F6C0F34 ($30K)
  Recommend:    Add error-logging to the HRIS sync worker; silent failures ("provisioning log shows no errors") mask the root cause — likely an API schema change or auth token expiry on the provider side.

  → BROAD PATTERN. Three mid/large accounts all hitting the same breakage over 10 weeks. Highest ARR at risk.

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
THEME #2 — REDEMPTION & GIFT CARD FAILURES
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  Count:        18     Share: 22.5%
  Distinct accts: 7    ARR exposed: $68,800
  Sample IDs:   IC-460025, IC-460030
  Accounts:     C-14264ABD ($11K), C-0B827671 ($10.7K), C-0B0F1BAB ($10.3K),
                C-0FCCD2DF ($9.6K), C-0D9CA315 ($9.6K), C-0CEF69FD ($8.9K),
                C-0F876796 ($8.7K)
  Recommend:    Make redemption transactional — points should deduct only after gift-card fulfillment succeeds; add checkout timeout handling to prevent the spin-then-fail loop.

  → BROAD PATTERN. Seven distinct accounts, two sub-patterns: checkout hang (spin forever) and gift card error with points already deducted (worst-case UX: user loses points, gets nothing).

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
THEME #3 — BILLING / INVOICE ERRORS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  Count:        16     Share: 20.0%
  Distinct accts: 1    ARR exposed: $52,000
  Sample IDs:   IC-460071, IC-460069
  Accounts:     C-0E9C27D1 ($52K) — ONLY ACCOUNT
  Recommend:    Audit the seat-count calculation pipeline and freeze auto-renewal billing for this account until tier-price logic is verified against contract terms. Two sub-patterns repeat: wrong seat count (200 vs 150 licensed) and wrong tier price on annual renewal.

  → SINGLE-ACCOUNT NOISE (but high-stakes). One $52K account generated 20% of all tickets with three repeating complaints. Renewal/churn risk here is acute.

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
THEME #4 — POINTS NOT POSTING / MISSING BALANCES
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  Count:        20     Share: 25.0%  ← highest volume
  Distinct accts: 9    ARR exposed: $31,100
  Sample IDs:   IC-460004, IC-416
  Accounts:     C-0BF20542 ($4.5K), C-0D0B047C ($4.5K), C-0D6CC8E3 ($4.2K),
                C-0D3278C7 ($3.5K), C-0D284E42 ($3.4K), C-21FEBCBB ($2.9K),
                C-0B2895EF ($2.9K), C-0BE96399 ($2.7K), C-0DD0626C ($2.5K)
  Recommend:    Investigate the points-posting job queue for stuck/failed workers; the "recognition shows delivered but points never arrived" pattern suggests async credit pipeline breakage between the recognition service and ledger.

  → BROAD PATTERN. Most tickets (25%), most accounts (9), but smallest accounts (all <$5K ARR). Core product functionality degraded — erodes trust even if ARR-per-ticket is low.

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
THEME #5 — SLACK INTEGRATION BREAKAGE
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  Count:        14     Share: 17.5%
  Distinct accts: 4    ARR exposed: $18,900
  Sample IDs:   IC-460041, IC-460047
  Accounts:     C-10A56B0F ($5.4K), C-8C2E8F00 ($5.2K), C-0B843542 ($4.4K),
                C-0BA71F12 ($3.9K)
  Recommend:    Review recent Slack app permission changes or API deprecations; the toggle-reset + re-auth-doesn't-stick + slash-command-error triad points to OAuth token scope issues or a broken webhook registration.

  → BROAD PATTERN. Four accounts, three distinct failure modes that all point to the same integration surface.

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
SUMMARY ARITHMETIC
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  Total tickets:           80
  Total ARR across themes: $284,800 (sum of unique account ARR per theme;
                                 accounts appear in one theme only = no double-count)
  Themes spanning 2+ accts:  4 of 5  (only Billing is single-account)
  Highest volume:          Points Not Posting (20 tickets, 25%)
  Highest ARR risk:       HRIS Provisioning ($114K, 3 accts)
  Most urgent single-acct: C-0E9C27D1 — 16 tickets, $52K ARR,
                           repeating seat-count + tier-price errors
                           on every invoice since mid-June
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 $0.0235 · 39s · in 16,744 / out 857 tok
✓ top3✓ decoys_excluded✓ cites_fields✓ no_billing

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
## Social Proof Ranking for Prospect C-82AF3719

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

Only 8 of 24 customers have `has_case_study=true`. Scoring each on field-by-field match (industry / size_band / use_case / region):

| Rank | Company | Score | Match Fields | Mismatch Fields |
|------|---------|-------|-------------|-----------------|
| **1** | **C-64171065** | **3/4** | industry (Technology), size_band (Mid-Market), use_case (employee_recognition) | region (NA-East vs NA-West) |
| **2** | **C-11C31562** | **3/4** | size_band (Mid-Market), use_case (employee_recognition), region (NA-West) | industry (Manufacturing vs Technology) |
| **3** | **C-A13C193D** | **2/4** | industry (Technology), size_band (Mid-Market), region (NA-West) | use_case (retention vs employee_recognition) |

### Runners-up (not in top 3):

- **C-CD4829A7** — 2/4 (Technology, Mid-Market; mismatched use_case=retention, region=NA-East)
- **C-9D69D147** — 2/4 (Mid-Market, NA-West; mismatched industry=Financial Services, use_case=retention)

Three customers scored 0/4 and are omitted: C-FEFCC020, C-11209339, C-0CE315B2.

### Tiebreak logic (Rank 1 vs 2):
C-64171065 edges C-11C31562 because **industry match** (same vertical = stronger proof point) outweighs region match (same geography). A Technology prospect will more readily identify with a Technology customer than one from Manufacturing, even if the latter is co-located.

### Tiebreak logic (Rank 3 vs runner-ups):
C-A13C193D beats C-CD4829A7 on **region match** (NA-West = prospect's exact region), giving it the third slot despite identical 2/4 scores.
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 $0.0891 · 209s · in 29,208 / out 10,961 tok
✓ undefined_channel✓ undefined_not_zero✓ flagged_rows✓ best_channel✓ reallocation

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
CHANNEL PERFORMANCE — TRAILING 6 MONTHS (MAR–AUG 2026)

═══════════════════════════════════════════════════════════════════════════════
Channel          |    Spend | SQMs | SQOs | Cost/SQM | Cost/SQO | SQM→SQO% |     Pipeline |  Pipe/$
═══════════════════════════════════════════════════════════════════════════════
PAID:
  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| undefined|          $0  |  $0.00
  webinars      |  $9,000  |   12 |    5 | $750     | $1,800   | 41.7%    |     $60,000  |  $6.67

ORGANIC (no spend):
  organic_search| N/A      |   30 |   10 | N/A      | N/A      | 33.3%    |     $90,000  |   N/A
  referral      | N/A      |   15 |    6 | N/A      | N/A      | 40.0%    |     $48,000  |   N/A
═══════════════════════════════════════════════════════════════════════════════

Arithmetic checks:
- paid_search spend:  $6,000 × 6 = $36,000
- linkedin_ads spend: $4,000 × 6 = $24,000
- paid_social spend:  $3,000 × 6 = $18,000
- webinars spend:     $1,500 × 6 = $9,000
- Total paid spend:   $87,000
- Total pipeline:     $1,014,000

DATE ANOMALIES FLAGGED — SQO date precedes SQM date (linkedin_ads):
  CT-000041: SQM=2026-06-14 → SQO=2026-06-09  (SQO 5 days BEFORE SQM)
  CT-000044: SQM=2026-07-23 → SQO=2026-07-18  (SQO 5 days BEFORE SQM)

These 2 records have inverted dates — likely a data-entry or ETL issue. Pipeline counts for both are included above since the contacts did convert; flag for data hygiene review.

---

REALLOCATION RECOMMENDATION:

1. PAUSE paid_social entirely — $18,000 spent, 0 SQMs, 0 SQOs, $0 pipeline. This is either completely ineffective or has a tracking/UTM break. Either way, stop spending until resolved.

2. SHIFT the $18K/mo freed from paid_search into paid_search (best paid ROI at $20 pipeline per dollar) and webinars ($6.67/$). Suggested split: $12K → paid_search, $6K → webinars (doubling webinar budget).

3. DOUBLE DOWN on organic_search — it's your highest-volume organic channel (30 touches, 33.3% SQO rate, $90K pipeline at zero marginal cost). Invest in SEO and content to amplify what's already converting.

4. LINKEDIN_ADS needs investigation — $3,000/SQO is 50% worse than paid_search's $2,000/SQO, plus 2 date-anomaly records suggest possible attribution or data-quality issues. Don't kill it yet, but audit before increasing spend.

---

CONFIDENCE: MEDIUM

Why not HIGH:
- Sample size is 122 contacts over 6 months — individual channel counts are small (e.g., webinars only 12 SQMs). A few deals swinging either way noticeably changes rates.
- paid_social at $18K / 0 output is extreme — points to a likely tracking gap rather than true zero performance, which means the "pause" rec captures real money but the root cause is unknown.
- 2 date anomalies on linkedin_ads (SQO < SQM) introduce noise into that channel's conversion metrics.
- No seasonality baseline (only one 6-month window).

Directionally the ranking is solid: paid_search >> webinars > linkedin_ads >> paid_social. Exact dollar efficiencies will shift as more data accumulates.
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 $0.0373 · 116s · in 17,393 / out 4,095 tok
✓ competitor_named✓ pricing✓ win_loss✓ unverified_marked✓ citations

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
# Battlecard: Rivally (updated 2026-09-03)

---

## Positioning
Points-based employee recognition platform targeting mid-market and EU-distributed teams. [S02, S04, S12]

---

## Pricing
**Current list: $7 per user/month, annual billing required** (pricing page, 2026-08-12). [S17]

**Prior price: $5 per user/month, annual billing required** (pricing page, 2026-01-20). [S03] Price confirmed still at $5 on 2026-04-01. [S08]

**Conflict note:** Pricing page showed $5/user/mo from 2026-01-20 through at least 2026-04-01, then updated to $7/user/mo on 2026-08-12. Deal evidence supports the increase — one prospect quoted $6.50/user/mo (2026-06-02, 500-seat, annual) [S13] and another quoted $7/user/mo list with 15% discount for a 3-year term (2026-08-14). [S18]

**Discounting observed:** 15% off list for 3-year commitment (single data point, 2026-08-14). [S18] (AE opinion about aggressive discounting noted but unconfirmed — rep opinion only.) [S21]

---

## Where Rivally Wins

| Strength | Source |
|---|---|
| Recognition feed UX is engaging and points-based model resonates with end users | [S02, S16] |
| Strong fit for distributed **EU teams**; multi-language support praised by EU enterprise reviewers | [S12] |
| **EU data residency** now generally available (Dublin office opened 2026-07-01); a key differentiator they pitch in competitive evals | [S05, S15] |
| Fast setup — mid-market reviewer reported under a week to live | [S04] |
| **Slack integration works out of the box** | [S04] |
| Support response time praised (under 4 hours) | [S22] |
| Microsoft Teams app v2 now in public preview (2026-08-20) | [S19] |

---

## Where We Win (Rivally Gaps / Weaknesses)

| Gap / Differentiator | Source |
|---|---|
| **Analytics / reporting is a consistent weak point** — reviewers describe dashboards as "basic compared to enterprise tools"; analytics exports are CSV-only making migration off Rivally hard; we have won at least one deal (800-seat) specifically on analytics depth | [S02, S07, S20, S25] |
| **Admin tooling lags peers** — admin console lacks bulk recognition editing; manual user management is painful | [S10, S16, S24] |
| **No SCIM provisioning** — enterprise reviewer called manual user management painful | [S10] |
| **Rewards catalog thinner in EMEA** than US catalog — a gap in their strongest region | [S14] |
| **Rivally Pulse (engagement surveys)** is a paid add-on, not bundled — creates upsell cost surprise | [S06, S23] |

---

## Objections & Responses

| Prospect Objection | Response (grounded in data) | Source for objection |
|---|---|---|
| "Rivally has EU data residency — you don't." | Acknowledge. Pivot: EU data residency matters for a subset of regulated industries. Trade off against our analytics depth, SCIM provisioning, and admin tooling — gaps that grow more painful at scale (see enterprise reviews citing manual user management pain). | [S05, S15] |
| "Rivally's recognition feed is more engaging." | Agree that end-users like the feed (reviewers confirm). Counter: engagement without admin visibility (basic dashboards, CSV-only exports, no bulk editing) creates operational debt for HR/ops teams. The 800-seat prospect who chose us cited analytics depth as the deciding factor. | [S02, S16, S25] |
| "Rivally is cheaper." | Their list price is now **$7/user/mo** (up from $5 in Jan). We've seen $6.50–$7 quotes with modest discounts (15% for 3-year). Price is no longer a clear differentiator — and add-ons like Rivally Pulse are unbundled. Get their full quote including add-ons before comparing. | [S03, S13, S17, S18, S23] |
| "Rivally has Slack/Teams integrations." | They do — Slack OOTB, Teams v2 in preview. This is table stakes, not a differentiator. The question is what happens after recognition data flows in: can you report on it, export it cleanly, manage users at scale? That's where we pull ahead. | [S04, S19] |
| "Rivally setup is fast (under a week)." | Acknowledge. Fast setup is good. Ask: fast setup into *what* — a platform where you'll hit admin tooling walls in month 3? SCIM gap alone costs IT hours monthly. | [S04, S10] |

---

## Recent Changes (Last 90 Days)

| Date | Change | Source |
|---|---|---|
| 2026-08-12 | **Price increase**: Recognition Starter tier raised from $5 → $7/user/mo | [S17] |
| 2026-08-14 | Observed: $7/list with 15% discount offered for 3-year term | [S18] |
| 2026-08-20 | **Microsoft Teams app v2** entered public preview | [S19] |
| 2026-08-25 | Reviewer flagged: analytics exports CSV-only (migration friction) | [S20] |
| 2026-08-30 | Support response time praised (<4 hours) | [S22] |
| 2026-09-01 | **Rivally Pulse exits beta** — now a paid add-on (not bundled) | [S23] |
| 2026-09-02 | Admin console still missing bulk recognition editing | [S24] |
| 2026-09-03 | **Loss recorded**: 800-seat prospect chose Bonusly over Rivally, citing analytics depth | [S25] |

Older notable changes (60-120 days):
- 2026-07-01: Dublin office opened; **EU data residency GA** [S15]
- 2026-05-09: Hired ex-Workday VP EMEA to lead European expansion [S11]
- 2026-03-05: Rivally Pulse (engagement survey add-on) first launched [S06]

---

## Our 12-Month Win/Loss Record vs. Rivally

Window: 2025-09-01 through 2026-08-31 (rolling 12 months from last deal dated 2026-08).

```
MONTH     WINS                    LOSSES                  TOTAL
------    ----                    ------                  -----
2025-09   Deal-072E31             Deal-7767F5             1W / 1L
2025-10   Deal-A9FD43, Deal-F65C8F                       2W / 0L
2025-11   Deal-7AA785             Deal-D263E0             1W / 1L
2025-12   Deal-44C524             Deal-935746             1W / 1L
2026-01   Deal-0D0CD6, Deal-E46EAB                        2W / 0L
2026-02   Deal-D5B790, Deal-1D2392                        2W / 0L
2026-03   Deal-5C636E             Deal-9066A6             1W / 1L
2026-04                           Deal-5645A5, Deal-72A02F 0W / 2L
2026-05                           Deal-C6FFAA             0W / 1L
2026-06   Deal-67BE14                                     1W / 0L
2026-07   Deal-1B6969                                     1W / 0L
2026-08   Deal-F03E7B                                     1W / 0L
------                                                   -----
TOTAL     13 wins / 7 losses      WIN RATE: 65%
```

**Streak note:** We lost 4 of 5 deals between March–May 2026 (Deal-9066A6, Deal-5645A5, Deal-72A02F, Deal-C6FFAA) but have won every deal since June 2026 (4 consecutive: Deal-67BE14, Deal-1B6969, Deal-F03E7B, plus Deal-25/S25 call note).

---

## Claims from Previous Battlecard — Re-sourced or Marked Unverified

| Old Claim | Verdict | Evidence |
|---|---|---|
| "Points-based recognition for mid-market" | **CONFIRMED** | [S02], [S04] |
| "$5/user/mo, annual billing (as of 2026-01)" | **SUPERSEDED** — was correct then; now $7/user/mo as of 2026-08-12 | [S03] → [S17] |
| "Rivally lacks a Slack integration" | **FALSE** — contradicted by data. Slack integration works out of box per mid-market reviewer (2026-02-02) | [S04] |
| "Rivally was acquired by WorkHuman in 2025" | **UNVERIFIED** — no snippet mentions any acquisition. S11 references hiring an ex-*Workday* (not WorkHuman) VP for EMEA; this is a hire, not an acquisition. Cannot confirm from provided data. | No supporting snippet |
| "Strong in EU enterprise with multi-language support" | **CONFIRMED** | [S12], [S11], [S15] |
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 $0.0169 · 200s · in 109 / out 2,536 tok
✓ tracking_error✓ overlap_pair✓ weakest_step✓ fix_priority

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
## Sequence Performance Analysis

**PER-SEQUENCE METRICS**

New Logo Nurture (total sent: 1,386)
  Step 1: 500 sent | 42.0% open | 8.4% reply | 2.4% mtg | weakest: step 3
  Step 2: 458 sent | 34.9% open | 6.6% reply | 2.0% mtg
  Step 3: 428 sent | 28.0% open | 4.2% reply | 1.4% mtg

Expansion Nurture (total sent: 875)
  Step 1: 300 sent | 43.3% open | 7.3% reply | 1.7% mtg | weakest: step 3
  Step 2: 300 sent | 113.3% open | 8.3% reply | 1.3% mtg  *** TRACKING ERROR ***
  Step 3: 275 sent | 34.5% open | 4.4% reply | 1.1% mtg

Cold Outbound - HR Leaders (total sent: 1,785)
  Step 1: 600 sent | 40.0% open | 0.83% reply | 0% mtg | weakest: all steps
  Step 2: 595 sent | 29.4% open | 0.34% reply | 0% mtg
  Step 3: 590 sent | 22.0% open | 0.17% reply | 0% mtg

Cold Outbound - People Ops (total sent: 1,163)
  Step 1: 400 sent | 37.5% open | 3.5% reply | 0.75% mtg | weakest: step 3
  Step 2: 386 sent | 28.5% open | 2.3% reply | 0.52% mtg
  Step 3: 377 sent | 21.2% open | 1.6% reply | 0.27% mtg

---

**TRACKING ERROR**

Expansion Nurture step 2: 340 opens > 300 sent (+13.3%). Likely cause: open pixel firing on forwards, replies, or a deduplication bug in the send log. Do not trust any Expansion funnel metrics until this is resolved.

**AUDIENCE OVERLAP**

8 contacts appear in multiple sequences:
- CT-001255, CT-001153, CT-001033, CT-001258, CT-001345, CT-001097, CT-000849 → appear in BOTH **Cold Outbound - HR Leaders** AND **Cold Outbound - People Ops**
- CT-000301 → appears in BOTH **Expansion Nurture** AND **New Logo Nurture**

The two cold outbound sequences are leaking into each other. This inflates sent counts and may cause double-messaging.

---

**FAILURE MODES UNDER 2% REPLY**

**Cold Outbound - HR Leaders (all steps):** Opens are healthy (40% step 1) but replies collapse (0.83% → 0.17%). The subject lines work — people open. The content doesn't. Audience either isn't the right decision-maker for this offer, or the ask/messaging misses their priorities entirely. Zero meetings across 1,785 sends confirms total miss.

**Cold Outbound - People Ops step 3 (1.6%):** Normal fatigue decay. Not broken — just a 3rd touch that's outlived its pull.

---

**ONE CHANGE PER WEAK SEQUENCE + PRIORITY ORDER**

1. FIX FIRST: **Cold Outbound - HR Leaders** — Rewrite step 1 body copy. Opens prove deliverability and subject-line interest are fine; the failure is post-open. Pivot the value prop: lead with a specific benchmark or cost-saving hook relevant to HR leaders (e.g., turnover cost per role, recognition-to-retention correlation) rather than a product pitch. Current state: 1,785 sends, 8 replies, $0 pipeline. Highest-impact fix.

2. **Cold Outbound - PeopleOps** — Drop step 3 or convert it to a value-only drop (send a case study or benchmark PDF with no CTA). Reply rate fell from 3.5% → 1.6%; a third "ask" email is burning audience with diminishing returns.

3. **Expansion Nurture** — Pause step 2 sends until the 340/300 open tracking error is diagnosed. Any decisions made on current Expansion data are unreliable.
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 $0.0094 · 67s · in 261 / out 1,297 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 (Week ending ~Sep 6; Day 66 of 92)**

---

**METRIC-BY-METRIC**

| Metric | QTD Actual | Target | Delta | Pace Target (66/92) | Pace |
|---|---|---|---|---|---|
| SQMs | 230 | 300 | −70 | 215 | AHEAD (+7%) |
| SQOs | 84 | 120 | −36 | 86 | BEHIND (−2%) |
| DS2s | 40 | 75 | −35 | 54 | BEHIND (−26%) |
| Closed-Lost MIA Rate | 20.0% (5/25) | 10.0% | +10.0pp | ≤10.0% | BEHIND |
| Same-Qtr Closes | 10 | 20 | −10 | 14 | BEHIND (−29%) |
| Active Pipeline | $3.0M | $4.0M | −$1.0M | $2.87M | AHEAD (+4%) |
| Pipeline Coverage vs Target | **0.75x** | — | — | — | — |

Arithmetic:
- SQM pace: 300 × (66/92) = 215.2 → 230 actual = +6.9% ahead
- SQO pace: 120 × (66/92) = 86.1 → 84 actual = −2.4% behind
- DS2 pace: 75 × (66/92) = 53.8 → 40 actual = −25.6% behind
- MIA rate: 5 ÷ 25 = 0.200 → target ≤0.100, over by 10pp
- Close pace: 20 × (66/92) = 14.3 → 10 actual = −30.1% behind
- Pipeline pace: $4.0M × (66/92) = $2.87M → $3.0M actual = +4.5% ahead
- Coverage: $3.0M ÷ $4.0M = 0.75x

**WHAT MOVED THIS WEEK**

Two metrics are pacing ahead — SQMs (+7%) and active pipeline (+4%) — but both mask conversion leaks downstream. The SQM-to-SQO conversion rate sits at 36.5% (84/230); at that rate the 230 SQMs already banked can only yield ~84 SQOs (which is exactly where we are — no headroom). That's the binding constraint: top-of-funnel is outpacing its own conversion capacity, so incremental SQMs this week don't translate to incremental pipeline unless SQO conversion improves. DS2s are the deepest deficit at −26% off pace (40 vs. 54 expected), and same-quarter closes lag by −29% (10 vs. 14 expected). The MIA rate at 20% is double the 10% tolerance — 5 of 25 closed-lost deals went dark with no documented reason, which is a signal-gaps issue, not a volume issue. With 26 days remaining in quarter, the path to green requires: (a) recovering ~16 DS2s (from current 40-run-rate that would land at ~56 vs. 75 target), (b) cutting MIA losses in half, and (c) converting 10 more same-quarter wins from the $3.0M active pipe. Pipeline coverage at 0.75x is workable if close rates on late-stage deals execute, but the DS2 and close-count gaps are the real risk — they're both behind pace by roughly a full month of run-rate.
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 $0.0217 · 51s · in 16,599 / out 441 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 lands at $116K weighted ($45K commit plus $71K from best-case at 35%), but all 7 commit deals lack a documented why-buy and 32 deals worth $228K have already slipped past September close dates. Treat this as a directional estimate rather than a precise number until owners and buying reasons are populated.
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 $0.0058 · 28s · in 373 / out 370 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: Re: Pricing recap — Deal-0D2F7A

Hi [Name],

Following up on the pricing I sent over on August 5th after our July 28 demo. The People team had a strong reaction to the automated milestone awards and points catalog during that session — wanted to circle back before this falls off the radar.

Two quick questions:

1. Where does this sit in your priority stack for Q4?
2. Would a 15-minute sync next week work to address any outstanding questions on pricing or rollout timing?

Happy to loop in the VP People directly if that helps move things forward.

Best,
Alex Franklin

---

98 words. References the Aug 5 pricing email and Jul 28 demo specifically. One clear ask (15-min sync). Tone matches prior thread: direct, professional, light urgency.
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 $0.0065 · 36s · in 236 / out 587 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 — Week of Aug 31**

**Marketing** — We landed 46 SQMs against our 52-SQM target, putting us 6 short of goal. The webinar channel pulled its weight, contributing 18 of those SQMs — nearly 40% of the week's total from one channel. Paid search cost-per-SQM held steady at $150, so efficiency isn't the issue; we just need more top-of-funnel volume to close that gap next week.

**Sales** — Strong conversion rhythm this week: 14 SQOs moved forward and the team set 9 DS2 meetings. New pipeline created hit $310K, and we booked 3 same-quarter closes — concrete momentum heading into September. The DS2 meeting count is the number to watch: keeping that cadence above 8/week is what keeps the mid-funnel healthy.

**CS** — Renewals team saved 2 accounts this week — always worth celebrating when retention work pays off in real dollars. Team NPS ticked up to 61, which is solid territory. That said, 3 open red-flag accounts are rolling into next week, so the CS team has their work cut out staying ahead of at-risk expansion.

**PLG** — Product-led motion added 412 new signups with activation holding at 31% (~128 activated users). The real bright spot: 38 companies reached the "10 recognition gives" aha moment — that's the inflection point where self-serve converts into expansion conversations. If even a fraction of those 38 convert to paid seats, PLG is feeding the Sales pipeline from below.
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 $0.0231 · 24s · in 16,506 / out 828 tok
✓ all_partners✓ pipeline_amount✓ quiet_one_line

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
# PARTNER DIGEST — 2026-08-24 to 2026-09-04

---

**Apex Rewards Co** — ACTIVE
Co-webinar locked for 09-15. 2 sourced opps this period, both DS1.
Pipeline: 2 deals / $275,000
  • Deal-DDAAF2 — $180,000
  • Deal-2CF33E — $95,000

**HRCloud Partners** — ACTIVE
Integration referral cleared security review; opp advanced to DS2. 1 sourced opp this period.
Pipeline: 1 deal / $140,000
  • Deal-F1CDA5 — $140,000

**CultureBridge** — ACTIVE
Lunch-and-learn drove 2 sourced opps; both early stage. No stage advancement noted this period.
Pipeline: 2 deals / $135,000
  • Deal-096E1D — $60,000
  • Deal-067213 — $75,000

**WorkWell Group** — ACTIVE
No sourced deals this period. Joint playbook restart targeted for Q4; planning call booked 09-09.
Pipeline: 0 deals / $0

---

**Recogniq** — Quiet. No activity this period.

**KudosWave** — Quiet. No activity this period.

**PeopleFirst Advisors** — Quiet. No activity this period.

**TotalPerk** — Quiet. No activity this period.

---

**PERIOD TOTALS**
Partner-sourced pipeline: 5 deals / $550,000
  • Apex Rewards Co:     2 deals / $275,000  (50% of pipeline $)
  • HRCloud Partners:    1 deal  / $140,000  (25%)
  • CultureBridge:       2 deals / $135,000  (25%)
  • WorkWell Group:      0 deals / $0
  • Quiet partners (4):  0 deals / $0
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 $0.0057 · 16s · in 423 / out 327 tok
✓ answered_count✓ unanswerable_flagged✓ citations✓ no_guess

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
Q1: Yes — AES-256 encryption at rest. [P1]

Q2: Yes — TLS 1.2 or higher. [P1]

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

Q4: Duration of the contract plus 90 days, after which data is deleted from production systems. [P3]

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

Q6: Yes, SAML 2.0 SSO is available on the Enterprise plan (alongside SCIM provisioning). [P5]

Q7: Daily backup of production databases; backups are encrypted and retained for 35 days. [P6]

Q8: Unanswerable from the provided excerpts. The excerpts mention SOC 2 Type II [P2] but never reference ISO 27001. An ISO certification statement or compliance matrix would be required.

Q9: Unanswerable from the provided excerpts. No uptime SLA percentage or availability commitment appears in any excerpt. The master services agreement (MSA) or service-level policy would contain this.

Q10: Unanswerable from the provided excerpts. No HIPAA, BAA, or protected health information (PHI) language appears in any excerpt. A HIPAA addendum or BAA request form would address this.
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 $0.1238 · 172s · in 81,725 / out 6,433 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

## Manifest Summary
- 14 manifest rows
- 14 SKILL.md files provided
- All 14 file names match manifest `declared_name` values

---

## Finding 1 — ALWAYS-Trigger Phrase Overlap

**Severity: WARNING | Action: TRIM_DESC**

**Skills:** `email-drafter` ↔ `comms-drafter`

**Overlapping trigger phrases (exact or near-exact matches):**

| Trigger Phrase | email-drafter | comms-drafter |
|---|---|---|
| "write me an email" | ✅ | ✅ |
| "draft a follow-up" | ✅ | ✅ |
| "what should I say" | ✅ | ✅ |
| "bump email" | ✅ | ✅ |
| "contract nudge" | ✅ | ✅ |
| "help me reply" / "reply to this" | ✅ | ✅ |

**Arithmetic:** 6 shared trigger phrases out of ~10 primary triggers each = ~60% overlap on always-triggers.

**Proposal:** `email-drafter` owns ALL email drafting (outbound, follow-up, contract, bump). `comms-drafter` retains non-email channels (Intercom, support tickets, partner notes, Slack messages, QBRs, onboarding check-ins) and removes the 6 overlapping email trigger phrases from its description. Both already have correct lane markers pointing to each other; the trigger boundary is what's fuzzy.

---

## Finding 2 — Circular Delegation Chain

**Severity: CRITICAL | Action: REVIEW**

**Chain name:** `email-drafter ↔ deal-strategy-coach`

**Evidence:**

- `email-drafter` §Lane marker: *"point them to the deal-strategy-coach skill"* for strategic coaching
- `deal-strategy-coach` §Manager-to-prospect email frameworks: *"use the `email-drafter` skill which automatically retrieves your Gmail signature and appends it"*

Each skill delegates to the other for different sub-tasks (strategy vs. execution), creating a cycle where either could invoke the other depending on user input phrasing.

**Proposal:** Break the cycle by having `deal-strategy-coach` produce draft content directly for manager emails (it already has full messaging guidance, tone rules, and persona data) rather than handing off to `email-drafter`. Keep the `email-drafter` → `deal-strategy-coach` direction (one-way escalation for strategy diagnosis). Remove the `email-drafter` invocation from `deal-strategy-coach`'s Manager Email Frameworks section.

---

## Finding 3 — Dangling Delegation Targets

**Severity: WARNING | Action: REVIEW**

**11 referenced skills/org-skills have no manifest row:**

| Dangling Target | Referenced By | Type |
|---|---|---|
| `bonusly-brand` | comms-drafter, email-drafter, sales-forecast, signalforge-claim-compressor | Org skill |
| `prospect-research-multithreading` | comms-drafter, deal-strategy-coach, email-drafter | Skill (missing) |
| `bonusly-data-questions` | analysis-validator §12.4 | Specialist skill (missing) |
| `bonusly-product-questions` | analysis-validator §12.4 | Specialist skill (missing) |
| `bonusly-business-reporting-questions` | analysis-validator §12.4 | Specialist skill (missing) |
| `bonusly-rewards-questions` | analysis-validator §12.4 | Specialist skill (missing) |
| `bonusly-ppp-questions` | analysis-validator §12.4 | Specialist skill (missing) |
| `bonusly-feature-flag-questions` | analysis-validator §12.4 | Specialist skill (missing) |
| `bonusly-deal-desk-questions` | analysis-validator §12.4 | Specialist skill (missing) |
| `bonusly-datadog-questions` | analysis-validator §12.4 | Specialist skill (missing) |
| `signalforge-reports` | pipeline-intelligence-report, weekly-pipeline-report | Org skill dir |

**Arithmetic:** 11 dangling targets across 7 manifest skills. `analysis-validator` alone accounts for 8 (all in §12.4 Specialist Skill Reference).

**Proposal:** Split into two actions:
- **`bonusly-brand` and `signalforge-reports`:** These are org-level assets (not pipeline-scoped skills). Either add them to this manifest with type=ORG, or accept as cross-profile references. No code change needed.
- **`prospect-research-multithreading` + 8 specialist skills:** These are invoked by `delegate_to`/`call` logic. Either (a) create stub manifest entries marking them as OUT_OF_SCOPE for this reconciliation, or (b) confirm they live under a different profile/skill directory and add a cross-reference note to the manifest. Without this, any skill invoking them will get silent failures at runtime.

---

## Finding 4 — Version Conflict

**Severity: INFO | Action: NONE**

**Result: No version conflicts found.**

Arithmetic: 14 skills scanned. Only `analysis-validator` declares an explicit version (`v3.6`). No other skill has a `version:` frontmatter field. Zero duplicate skill names. No same-skill-different-version pairs.

**Note:** `analysis-validator`'s changelog shows v3.0 through v3.6 all dated May 9, 2026 — same-day version churn, not a conflict. If this skill were ever duplicated (e.g., via sync), v3.6 would survive as the higher version.

---

## Finding 5 — Descriptions Exceeding 1,024 Characters

**Severity: INFO | Action: TRIM_DESC**

**Count: 0**

**Arithmetic from manifest `description_chars` column:**

```
Max value:   1,006  (pipeline-intelligence-report)
              1,006  (signalforge-claim-compressor)
Threshold:   1,024
Delta:        -18   (both are under)
```

Both max entries are 18 characters shy of the threshold. No action required today, but both are within 2% of the limit — flag for monitoring if descriptions are extended.

---

## Finding 6 — Hardcoded Page IDs, Dates, and Person Names

**Severity: WARNING | Action: UPDATE_BODY**

**6A — Hardcoded HubSpot Stage IDs (5 distinct IDs, 5 skills)**

| ID | Value | Appears In |
|---|---|---|
| Stage IDs | `150582536`, `150582537`, `150582538`, `150582539`, `1175632767` | analysis-validator §12.2 & §13.3, next-to-close §Step 1, pipeline-intelligence-report §Phase 1, stale-pipeline-report §Phase 1, weekly-pipeline-report §Step 1 |

These are duplicated across 5 skills instead of being referenced from a single source of truth.

**6B — Hardcoded Dates (non-changelog/non-version)**

| Date | Skill | Context |
|---|---|---|
| `May 9, 2026` | analysis-validator | Header: "Last Updated" + 7 changelog entries |
| `May 4, 2026` | analysis-validator, closed-lost-analysis | Roster date, CALL_SPOTLIGHT_BRIEF removal |
| `March 28, 2023` | analysis-validator, pipeline-intelligence-report | DEALS table staleness |
| `May 2026` (general) | closed-lost-analysis | Sample cohort dates, multiple company examples |
| `2026-05-19` | model-selection | Registry last_checked |
| `2026-05-17` | partner-digest | Changelog dates |
| `2026-06-10` | stale-pipeline-report | Changelog dates |
| `April 27, 2026` | sales-forecast | Changelog |
| `April 1 – June 30, 2026` | weekly-pipeline-report | Q2 date window |

**6C — Hardcoded Person Names (non-roster/reference)**

| Name(s) | Skill | Context |
|---|---|---|
| Manish, Amani | analysis-validator §G1-K, §10 | Escalation targets for ARR hold |
| Full GTM roster (20 names + IDs) | analysis-validator §12.3 | Hardcoded name→ID map |
| Amani Phipps / Amani | partner-digest (6+ occurrences) | Owner attribution, Slack search |
| Kelli, Jen Lee, Hani, Bryce, Sara | partner-digest §Step 2C | Partner contact names |
| Alaina | sales-forecast | VP Sales reference (replaced Elena) |
| Ben Lavin | sales-forecast header, weekly-pipeline-report header | Report owner |
| Elena | sales-forecast changelog | Replaced by Alaina (stale ref in changelog only) |
| Company names as examples | closed-lost-analysis | Estee Lauder, MinIO, LIFTOFF, Nestlé, Ozinga, Aurora Innovation, GCash, Softheon, Ethos Cannabis, StickerYou |

**6D — Hardcoded System IDs**

| ID | Value | Skill | Purpose |
|---|---|---|---|
| HubSpot Org ID | `1973303` | pipeline-intelligence-report, next-to-close, stale-pipeline-report | Deal URL pattern |
| Confluence Cloud ID | `73fe98de-a4a3-4869-9f8a-bb1eeed4cf7f` | partner-digest, sales-forecast, signalforge-feedback | API target |
| Confluence Space ID | `1958248479` (RevOps), `2232811524` (SignalForg) | partner-digest, sales-forecast, signalforge-feedback | API target |
| Confluence Page IDs | `2286616609`, `2286321666`, `2295136266`, `2232582148`, `2247295002`, `2234417154` | partner-digest, signalforge-feedback, sales-forecast | Page targets |
| Google Sheet IDs | `1CLZe...`, `1ENua...` | weekly-pipeline-report | Data sources |
| Slack Channel ID | `C0561C1JCPJ` | stale-pipeline-report | Post target |
| User ID | `U03QLMBL7AR` | partner-digest | Slack search |
| Population anchors | `~452,000`, `~110,097` | analysis-validator §1-J | Calibration (noted as drift-prone) |

**Proposal:** This is the highest-volume finding. Prioritized actions:
1. **Stage IDs (5 skills):** Extract to a shared reference block or `deal-constants` include. All 5 skills copy-paste the same 5 IDs.
2. **Person names in analysis-validator §12.3:** Add `[LIVE QUERY]` tag — the roster already notes "Updated May 4, 2026" confirming it drifts.
3. **Confluence/Slack/Sheet IDs:** Accept as constants (these don't change often) but wrap in a marked `SYSTEM_CONSTANTS` section in each skill for auditability.
4. **Elena in sales-forecast changelog:** Stale — already replaced by Alaina in v1.1 body. Changelog is historically accurate but should be noted as such.

---

## Finding 7 — Manifest Drift (Both Directions)

**Severity: INFO | Action: NONE**

**Arithmetic:**

```
Manifest rows:     14
Files provided:    14
Rows with files:   14  (100%)
Files with rows:   14  (100%)
Orphan rows:        0
Orphan files:       0
```

**Result: No drift in either direction.** Every manifest row has a corresponding SKILL.md file, and every provided file has a corresponding manifest row. The manifest and filesystem are in sync.

---

## Summary Table

| # | Finding | Severity | Action | Affected Skills |
|---|---------|----------|--------|-----------------|
| 1 | ALWAYS-trigger overlap: email-drafter ↔ comms-drafter (6 shared phrases) | WARNING | TRIM_DESC | 2 |
| 2 | Circular delegation: email-drafter ↔ deal-strategy-coach | CRITICAL | REVIEW | 2 |
| 3 | Dangling delegation targets (11 missing skills/org-skills) | WARNING | REVIEW | 7 |
| 4 | Version conflict | INFO | NONE | 0 |
| 5 | Descriptions >1,024 chars | INFO | TRIM_DESC | 0 (but 2 at 98%+) |
| 6 | Hardcoded IDs, dates, person names (across 6 sub-categories) | WARNING | UPDATE_BODY | 11 of 14 |
| 7 | Manifest drift (either direction) | INFO | NONE | 0 |

**CRITICAL: 1 | WARNING: 4 | INFO: 3**
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 $0.0110 · 73s · in 586 / out 1,618 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 & take IC

| Field | Value |
|---|---|
| **Who** | Bryce Harmon |
| **Action** | Acknowledged PagerDuty alert for reward-worker queue depth > 10k; took Incident Commander role |
| **Command / Action** | PagerDuty acknowledgement (UI action) |
| **Verification** | Implicit — IC role assumed per [M01] |
| **Rollback** | N/A (acknowledgement only) |
| **Thread ref** | [M01] 14:02:10Z |

---

## Step 2: Measure queue depth

| Field | Value |
|---|---|
| **Who** | Farid Osman |
| **Action** | Check Sidekiq reward-queue depth |
| **Command** | `bundle exec rake sidekiq:queue_depth` |
| **Result** | Reward queue at **48,213** pending jobs (baseline < 500) |
| **Verification** | Command output: 48,213 |
| **Rollback** | N/A (read-only) |
| **Thread ref** | [M02] 14:04:33Z |

---

## Step 3: Inspect dead set for error pattern

| Field | Value |
|---|---|
| **Who** | Farid Osman |
| **Action** | Inspect Sidekiq dead set for failed-job root cause |
| **Command** | Not explicitly stated (Sidekiq console or dead-set inspection) |
| **Result** | **112 jobs** in dead set; all `Redis::TimeoutError`; errors started ~13:58Z |
| **Verification** | Dead-set count + error class inspection |
| **Rollback** | N/A (read-only) |
| **Thread ref** | [M03] 14:06:02Z |

---

## Step 4: Pause enqueue (feature flag)

| Field | Value |
|---|---|
| **Who** | Farid Osman |
| **Action** | Disable auto_recognition_enqueue feature flag to stop new job ingress |
| **Command** | `bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'` |
| **Verification** | Implicit — intended to stop queue growth; confirmed indirectly by [M07] showing depth falling |
| **Rollback** | `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'` |
| **Thread ref** | [M04] 14:08:45Z |

---

## Step 5: Clear dead set

| Field | Value |
|---|---|
| **Who** | Elena Sinclair |
| **Action** | Clear all jobs from the Sidekiq dead set (retry-dead / clear) |
| **Command** | Not explicitly stated (Sidekiq Web UI or Rails console) |
| **Result** | Dead set cleared of 112 Redis::TimeoutError jobs |
| **Verification** | Implicit — performed "while in the console"; no post-clear count given |
| **Rollback** | N/A once cleared (jobs are gone) — **NEEDS CONFIRMATION**: whether a backup/export was taken before clear |
| **Thread ref** | [M05] 14:15:20Z |

---

## Step 6: Scale workers up

| Field | Value |
|---|---|
| **Who** | Bryce Harmon |
| **Action** | Scale reward-worker deployment from 3 → 6 replicas to increase drain throughput |
| **Command** | `kubectl scale deployment/reward-worker --replicas=6` |
| **Result** | Replicas increased from **3 → 6** |
| **Verification** | Indirect — [M07] shows queue draining at ~1,200/min after this |
| **Rollback** | `kubectl scale deployment/reward-worker --replicas=3` |
| **Thread ref** | [M06] 14:21:07Z |

---

## Step 7: Confirm drain progress

| Field | Value |
|---|---|
| **Who** | Farid Osman |
| **Action** | Re-check queue depth to confirm drain is progressing |
| **Command** | Implied re-run of queue-depth check (exact command not restated) |
| **Result** | Queue at **9,400**, falling at **~1,200 jobs/min** |
| **Verification** | Queue depth measurement |
| **Rollback** | N/A (read-only check) |
| **Thread ref** | [M07] 14:33:41Z |

---

## Step 8: Full resolution verification

| Field | Value |
|---|---|
| **Who** | Cole Ingram |
| **Action** | Confirm queue fully drained and error rate normalized |
| **Command** | `bundle exec rake sidekiq:queue_depth` + Datadog error-rate dashboard check |
| **Result** | Queue = **0**; Datadog error rate back to **baseline** |
| **Verification** | Command output = 0; Datadog visual confirmation |
| **Rollback** | N/A (verification only) |
| **Thread ref** | [M08] 14:47:55Z |

---

## Step 9: Re-enable enqueue

| Field | Value |
|---|---|
| **Who** | Bryce Harmon |
| **Action** | Re-enable auto_recognition_enqueue feature flag now that queue is empty |
| **Command** | `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'` |
| **Verification** | Watched job processing for **3 minutes**: **40 new jobs processed cleanly** |
| **Rollback** | `bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'` (re-pause) |
| **Thread ref** | [M09] 14:49:10Z |

---

## Step 10: Scale workers back down

| Field | Value |
|---|---|
| **Who** | Bryce Harmon |
| **Action** | Return reward-worker replicas to pre-incident level |
| **Command** | `kubectl scale deployment/reward-worker --replicas=3` |
| **Result** | Replicas **6 → 3**; queue stable at **0** |
| **Verification** | Queue remains stable at 0 post-scale-down |
| **Rollback** | `kubectl scale deployment/reward-worker --replicas=6` (re-scale-up) |
| **Thread ref** | [M10] 14:55:00Z |

---

## Items needing confirmation

1. **[M03]** Exact command used to inspect the dead set (count + error class) — not provided.
2. **[M05]** Whether Elena exported or backed up the 112 dead jobs before clearing them. If not, those payloads are unrecoverable.
3. **[M05]** Exact command used to clear the dead set (e.g., `Sidekiq::DeadSet.new.clear`, Web UI click).
4. **[M07]** Exact command for the queue-depth recheck — assumed same as [M02] but not explicitly restated.

---

**Total elapsed time:** 14:02:10Z → 14:55:00Z = **52 minutes 50 seconds** (alert ack → resolved).
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 $0.0256 · 35s · in 17,702 / out 1,098 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 CASCADE SEQUENCE

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 (in log order)

  Time            Service          Event
  ──────────────  ───────────────  ────────────────────────────────────────────────────
  14:01:12Z       reward-service   FIRST ERROR — Redis connection timeout (root cause)
  14:01:20Z       reward-service   Retry exhausted for RewardGiveJob
  14:01:30Z       reward-service   Retry exhausted for RewardGiveJob
  14:01:40Z       reward-service   Retry exhausted for RewardGiveJob (3rd)
  14:01:40Z       sidekiq          RewardGiveJob failed; retrying in 60s
  14:02:28Z       sidekiq          RewardGiveJob failed; retrying
  14:02:30Z       sidekiq          WARN — Queue reward depth above 10,000 (backlog signal)
  14:03:05Z       api-gateway      502 upstream timeout calling reward-service /gives
                                    ↑ failure crosses into API layer
  14:03:30Z       web-app          Give form submission failed: upstream 502 from api-gateway
                                    ↑ user-facing impact begins
  14:03:31Z       sidekiq          RewardGiveJob failed; retrying
  14:03:48Z       api-gateway      502 upstream timeout calling reward-service
  14:04:13Z       api-gateway      502 upstream timeout calling reward-service
  14:04:22Z       sidekiq          RewardGiveJob failed; retrying
  14:04:45Z       web-app          Give form submission failed: upstream 502
  14:05:16Z       api-gateway      502 upstream timeout calling reward-service
  14:05:26Z       sidekiq          RewardGiveJob failed; retrying
  14:05:42Z       web-app          Give form submission failed: upstream 502
  14:06:47Z       sidekiq          RewardGiveJob failed; retrying
  14:06:49Z       web-app          Give form submission failed: upstream 502
  14:06:52Z       api-gateway      502 upstream timeout calling reward-service
  ...             postgres         Checkpoints continue unaffected (not in call path)
  22:10Z          reward-service   Redis connection restored; resuming job processing
  24:45Z          sidekiq          Queue reward depth below 500 (drain confirmed)

JOB INVOLVED
  Primary:  RewardGiveJob (sidekiq async worker)
  Collateral: RecognitionDigestJob (4 failures in sidekiq_jobs.csv, not explicitly called out in the log stream but failed in the same window)

DATADOG QUERY TO CONFIRM FIRST ERROR
  service:reward-service "Redis::TimeoutError" OR "timed out after 5s"

  Narrowed to the window:
  service:reward-service "Redis::TimeoutError" @14:00:00Z @14:02:00Z

WHAT THE LOGS DO NOT SHOW

  - Root cause of the Redis timeout itself (network partition? Redis OOM? maxclients reached? CPU starve on redis-primary?). Only the symptom (connection timeout after 5s) is recorded.
  - Any Redis-server-side logs or metrics (connected clients, memory usage, rejection log). The redis-primary service does not appear as a logging `service` in this dataset.
  - Whether the timeout was transient (single dropped packet) vs. sustained (Redis unresponsive). The ~21-minute outage window (14:01 → 14:22) suggests sustained, but logs don't say why.
  - Downstream user impact volume: how many unique end-users hit the 502. We see 4 explicit web-app errors but the sidekiq_jobs.csv shows 16 total job failures (12 RewardGiveJob + 4 RecognitionDigestJob), implying broader affect than the log sample captures.
  - Whether RecognitionDigestJob failures caused missed digest emails / notifications. The log stream never mentions RecognitionDigestJob by name — it only appears in sidekiq_jobs.csv.
  - Any auto-scaling, failover, or retry-with-backoff configuration details. We see "retrying in 60s" once and "retrying" thereafter, but the backoff schedule is not logged.
  - Post-incident replay: whether the 10,000+ queued jobs succeeded, were partially lost, or produced duplicates after the 14:22 recovery. The final log line says depth <500 but not whether jobs completed OK.
  - Any alerting or paging action (PagerDuty, Slack incident). No on-call response appears in the log slice.
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 $0.0065 · 23s · in 450 / out 517 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

**recognition_streaks_v2** — ON | 42 companies
  Controls: Records a recognition streak via StreakTracker when a "give" event occurs (recognition.rb)
  Targeting: segment:beta_companies

**points_budget_guardrails** — ON | 220 companies
  Controls: Enforces point-spending limits per giver (budget_service.rb)
  Targeting: all_companies (global)

**slack_dm_nudges** — ON | 87 companies
  Controls: Sends Slack DM nudge to users (nudge_job.rb)
  Targeting: segment:region_na

**redeem_flow_redesign** — OFF | 12 companies
  Controls: Switches redeem flow between V2 (new) and V1 (legacy) component rendering (redeem_controller.rb)
  Targeting: targeted_list

**analytics_dashboard_v3** — ON | 65 companies
  Controls: Instantiates AnalyticsV3 dashboard instead of default (analytics_controller.rb)
  Targeting: segment:tier_three

**ms_teams_app_v2** — OFF | 9 companies
  Controls: Installs TeamsAppV2 for MS Teams integration (teams_installer.rb)
  Targeting: targeted_list

**legacy_give_modal** — OFF | 14 companies
  Controls: NO CODE REFERENCE FOUND in the provided excerpt
  Targeting: segment:legacy_plan

**survey_boosters_q3** — ON | 7 companies
  Controls: NO CODE REFERENCE FOUND in the provided excerpt
  Targeting: segment:legacy_plan

**paused_offboard_cleanup** — OFF | 0 companies
  Controls: NO CODE REFERENCE FOUND in the provided excerpt
  Targeting: NONE (empty), zero companies

---

**Flags with no code reference:** legacy_give_modal, survey_boosters_q3, paused_offboard_cleanup (3 of 9)
**Flags with no targets (0 companies):** paused_offboard_cleanup only
**Total company counts sum to 456 across all non-zero flags; individual counts match each row as listed.**

**Shared targeting note:** Both legacy_give_modal and survey_boosters_q3 target segment:legacy_plan but have different company counts (14 vs 7) — meaning the segment is not being applied uniformly, or these are sub-segmented further within that plan.
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 $0.0063 · 21s · in 503 / out 448 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 — GREEN (standard approval)
  Reasoning: Clean mutual NDA with all five standard carve-outs, Delaware governing law, no embedded non-solicit or non-compete, and standard term/survival. Nothing unusual.

NDA-2 — YELLOW (counsel review)
  Reasoning: Embedded non-solicitation with 18-month post-termination tail exceeds typical inbound NDA bounds — "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." Rest is standard; carve-outs and governing law are fine.

NDA-3 — RED (full legal review)
  Reasoning: Three compounding issues: (1) broad non-compete — "For a period of three (3) years following the Effective Date, Recipient shall not, directly or indirectly, engage in or provide services to any business that competes with Discloser's business"; (2) zero confidentiality carve-outs stated; (3) Irish governing law / exclusive jurisdiction outside US — "governed by and construed in accordance with the laws of the Republic of Ireland, and the parties submit to the exclusive jurisdiction of its courts."