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openai/gpt-5.6-terra

Mean 0.920 · 27/40 perfect tests · $3.67 total · each card: the question → the correct answer (gold) → this model's actual answer

data-crm
stage-hygiene-auditPASS 1.00

? The Question

Audit stage hygiene on the open pipeline below. Rules: a deal at DS2 or later must have a substantive why-buys (15+ characters, not a placeholder such as tbd, n/a, see notes). A why_buys value of the form LONG(n) means the real text was present but truncated for display; treat it as present and substantive. Every deal should have a DS1 entry timestamp. A deal whose stage entry timestamps are out of order regressed (an earlier-stage timestamp is later than a later-stage timestamp). Report: percent of DS2+ deals non-compliant on why-buys overall and by owner (use the owners table for names), count of deals with no DS1 timestamp, count that regressed, and three process fixes. State definitions before numbers.
cost $0.1289 · 40s · in 6 / out 4,663 tok
✓ noncomp_pct✓ no_ds1_0✓ regressed_0✓ per_owner_breakdown✓ definitions_first✓ three_fixes

✓ Correct Answer (ground truth)

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

Model's actual answer

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Definitions
- DS2+ population: open deals currently at DS2, DS3, DS4, or DS5.
- Why-buys non-compliant: blank, under 15 characters, or a placeholder (`tbd`, `n/a`, `see notes`). `LONG(n)` is compliant.
- Missing DS1: blank `t_ds1`.
- Regressed: any populated earlier-stage timestamp later than a populated subsequent-stage timestamp.

Why-buys compliance — DS2+
- Overall: 102 non-compliant / 125 DS2+ deals = 81.6%
- Bryce Harmon: 15 / 18 = 83.3%
- Dana Mercer: 19 / 23 = 82.6%
- Alex Franklin: 48 / 61 = 78.7%
- Cole Ingram: 19 / 22 = 86.4%
- Farid Osman: 1 / 1 = 100.0%
- Elena Sinclair: 0 / 0 = n/a

Stage-timestamp hygiene
- Deals with no DS1 timestamp: 0
- Deals with regressed stage timestamps: 0

Process fixes
1. Make a substantive why-buys field required before a deal can move from DS1 to DS2.
2. Add a validation rule that rejects placeholder entries and requires at least 15 characters.
3. Add a scheduled stage-history audit that flags missing DS1 timestamps and out-of-order stage entries for owner correction.
owner-resolution0.67

? The Question

The deal extract below has an owner name field that is blank on nearly every row and an owner id field that is populated. Using the owners table, resolve each open deal to an owner name. Report: number of deals resolved, deals whose owner id has no match in the owners table (list them), the owner ids that map to archived/deactivated owners (list their names), and the total pipeline amount per resolved owner. Do not guess a name for any unmatched id.
cost $0.0990 · 18s · in 6 / out 1,973 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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Deals resolved: 155 of 155 open deals.

Owner IDs with no match in `owners.csv`: None.

Owner IDs mapped to archived/deactivated owners among the open deals: None.  
`Gavin Porter` (`1520255671`) and `Hugo Lindqvist` (`77260721`) are archived in `owners.csv`, but neither owner ID appears in `deals_open.csv`.

Total pipeline amount per resolved owner:

- `Bryce Harmon` (`119337721`): 35 deals; $1,054,144.00
- `Alex Franklin` (`84342457`): 67 deals; $624,310.00
- `Dana Mercer` (`83155923`): 23 deals; $335,795.00
- `Cole Ingram` (`83155924`): 22 deals; $288,161.43
- `Farid Osman` (`716654662`): 7 deals; $4,134.00
- `Elena Sinclair` (`701163055`): 1 deal; $2,100.00

Arithmetic check:  
$1,054,144.00 + $624,310.00 + $335,795.00 + $288,161.43 + $4,134.00 + $2,100.00 = **$2,308,644.43** total resolved open-deal pipeline.
gong-hubspot-join-integrityPASS 1.00

? The Question

Check the integrity of the call-to-deal mapping below. The gong table lists calls in the last 90 days keyed to HubSpot deal ids; deals_open lists the current open default-pipeline deals. Report: (a) count of gong rows whose deal id has NO match in deals_open (orphans; list a few sample aliases), (b) duplicate conversation keys (rows where calls_90d exceeds distinct_conversation_keys), (c) among the open deals that ARE at stage DS3 or later, the share that have at least one logged call. Give sample aliases for the orphan finding, all taken from the data. Note: gong_owner_id is a Gong user id, not a HubSpot owner id.
cost $0.1382 · 38s · in 6 / out 4,810 tok
✓ orphan_count✓ dup_keys_0✓ ds3_coverage✓ sample_ids_present

✓ Correct Answer (ground truth)

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

Model's actual answer

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(a) Orphans: 30 of 67 Gong rows have no `hs_deal_id` match in `deals_open`.

Arithmetic: 67 Gong rows − 37 matched open-deal rows = 30 orphans.

Sample orphan aliases: `Deal-8FA85D`, `Deal-8FC3F9`, `Deal-3B7945`, `Deal-42B265`, `Deal-9CCC42`.

(b) Duplicate conversation keys: 0 rows.

Arithmetic: for every Gong row, `calls_90d = distinct_conversation_keys`; therefore no row has `calls_90d > distinct_conversation_keys`.

(c) DS3-or-later open-deal call coverage: 25 of 84 open deals at `DS3`, `DS4`, or `DS5` have at least one logged Gong call.

Arithmetic: 25 ÷ 84 = 0.297619 = 29.76%.
snowflake-sql-trapped-catalog0.67

? The Question

Using ONLY the catalog provided, write one Snowflake SQL query returning, per customer company, for its first calendar month as a customer: unique givers, recognition count, successful redemption count. Read the catalog notes carefully: some tables are stale or unpopulated and must be avoided, and one documented filter (excluding deleted givers) must NOT be applied to historical giving counts. Put a comment above each FROM or JOIN explaining the table choice. Return only fenced SQL.
cost $0.0422 · 7s · in 3 / out 352 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
-- Cannot produce the requested per-customer results from the catalog:
-- no documented giving-events table, customer-company key/name mapping, or
-- redemption-to-company column is provided.
SELECT
  CAST(NULL AS VARCHAR) AS customer_company,
  CAST(NULL AS NUMBER) AS unique_givers,
  CAST(NULL AS NUMBER) AS recognition_count,
  CAST(NULL AS NUMBER) AS successful_redemption_count
WHERE FALSE;
```
crm-completeness-enrichment0.80

? 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 $0.1253 · 62s · in 9 / out 6,193 tok
✓ dup_clusters✗ invalid_emails✓ survivor_language✓ completeness_pct✓ no_invent

✓ Correct Answer (ground truth)

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

Model's actual answer

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Completeness

Deals: no CRM deal extract was provided. Completeness for owner, stage, amount, close date, and why-buys cannot be calculated. Pipeline amount at stake cannot be calculated.

Companies (34 records)
- industry: 34 / 34 × 100 = 100.00%
- employee_count: 24 / 34 × 100 = 70.59%
- hq_country: 28 / 34 × 100 = 82.35%
- Overall required company fields: 86 / 102 × 100 = 84.31%

Contacts (52 records)
- email, syntactically valid: 48 / 52 × 100 = 92.31%
- title: 41 / 52 × 100 = 78.85%
- persona: 39 / 52 × 100 = 75.00%
- Overall required contact fields, using valid email: 128 / 156 × 100 = 82.05%

Missing-field fixes supported by the enrichment export

- C-EC3025: fill employee_count = 400 from zoominfo_enrichment.csv.
- C-96039F: fill employee_count = 400 from zoominfo_enrichment.csv.
- C-44EA29: fill employee_count = 400 from zoominfo_enrichment.csv.
- C-D04904: fill employee_count = 400 from zoominfo_enrichment.csv.
- C-B23205: fill employee_count = 400 from zoominfo_enrichment.csv.
- C-60C75F: fill employee_count = 400 from zoominfo_enrichment.csv.
- C-7BBDFA: fill employee_count = 400 from zoominfo_enrichment.csv.
- C-50D386: fill employee_count = 400 from zoominfo_enrichment.csv.

No supported fill is available for these blank company fields because the matching enrichment row is also blank or absent:
- C-2D1F1B: hq_country
- C-D73B89: hq_country
- C-44EA29: hq_country
- C-D04904: hq_country
- C-2C60E5: hq_country
- C-EE9FFB: employee_count, hq_country
- C-93C8BF: employee_count
- C-2D1F1B and C-D73B89 also have blank hq_country in the enrichment export.

CRM versus enrichment disagreements

Recommendation: use zoominfo_enrichment.csv as the source for the conflicting fields below, then normalize country values consistently. This is a source recommendation only; the files do not establish which source is authoritative.

- C-66D1FC
  - industry: CRM `tech`; enrichment `Computer Software`
  - hq_country: CRM `US`; enrichment `United States`
- C-C6FE92
  - hq_country: CRM `United States`; enrichment `United States`
  - No disagreement.
- C-950043
  - hq_country: CRM `US`; enrichment `United States`
- C-EC3025
  - industry: CRM `Technology`; enrichment `Computer Software`
  - hq_country: CRM `USA`; enrichment `United States`
- C-96039F
  - hq_country: CRM `USA`; enrichment `United States`
- C-44EA29
  - industry: CRM `tech`; enrichment `Computer Software`
- C-92D97D
  - industry: CRM `Technology`; enrichment `Computer Software`
- C-D04904
  - industry: CRM `Technology`; enrichment `Computer Software`
- C-77A95A
  - industry: CRM `Technology`; enrichment `Computer Software`
  - hq_country: CRM `US`; enrichment `United States`
- C-AA8DDA
  - industry: CRM `Technology`; enrichment `Computer Software`
- C-B23205
  - hq_country: CRM `US`; enrichment `United States`
- C-E51FB7
  - hq_country: CRM `USA`; enrichment `United States`
- C-D0662E
  - hq_country: CRM `US`; enrichment `United States`
- C-B25F40
  - industry: CRM `Technology`; enrichment `Computer Software`
- C-60C75F
  - industry: CRM `tech`; enrichment `Computer Software`
- C-425E2A
  - industry: CRM `Tech `; enrichment `Computer Software`
  - hq_country: CRM `USA`; enrichment `United States`
- C-2D7423
  - hq_country: CRM `USA`; enrichment `United States`

Duplicate-company clusters

- Shared domain `acme-corp.com`: C-0A092931, C-0A092932
  - Survivor: C-0A092931
  - Reason: both share the same domain; C-0A092931 has the more complete non-conflicting record in the supplied extract.
- Shared domain `globex.io`: C-0A092933, C-0A092934
  - Survivor: C-0A092933
  - Reason: both share the same domain; fields conflict on industry (`SaaS` versus `Technology`). No source data establishes which industry is correct.

Invalid emails

- CT-0010: `user0@`
- CT-0080: `user0@`
- CT-0081: `user1@`
- CT-0192: `user2@`

Email-domain mismatch

- CT-0011: email `user1@other-domain.com`; contact domain `66d1fc.com`; company domain for C-66D1FC is `66d1fc.com`.

Missing contact fields requiring CRM completion

- Missing title: CT-0000, CT-0022, CT-0081, CT-0092, CT-0120, CT-0121, CT-0122, CT-0132, CT-0141, CT-0162, CT-0170.
- Missing persona: CT-0000, CT-0041, CT-0060, CT-0070, CT-0081, CT-0082, CT-0092, CT-0110, CT-0132, CT-0171, CT-0172, CT-0180, CT-0181.
- No supplied source supports filling these contact fields without inventing values.

Top 10 fixes by pipeline amount at stake

Cannot be produced: no deals, deal-to-company relationships, or pipeline amounts were provided. Therefore no fix can be ranked by pipeline amount at stake.
deal-intelligence
closed-lost-classification0.80

? The Question

Classify every deal lost in the last 6 months below into one primary category: pricing, competitor, no decision, timing, product gap, champion left, or other. Also assign a side (Bonusly, buyer, unknown). Then summarize: category counts, the side split, the number of deals where the structured closed_lost_tag clearly disagrees with the free-text reason, and the two patterns most worth acting on. Base everything only on the tag and free-text provided.
cost $0.1976 · 161s · in 18 / out 10,404 tok
✓ total_90✗ competitor_top✓ category_counts_present✓ side_split✓ two_patterns

✓ Correct Answer (ground truth)

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

Model's actual answer

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Classifications

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

Category counts

- No decision: 32
- Competitor: 22
- Timing: 21
- Product gap: 7
- Pricing: 6
- Other: 2
- Champion left: 0

Arithmetic: 32 + 22 + 21 + 7 + 6 + 2 + 0 = 90 deals.

Side split

- Buyer: 51
- Unknown: 31
- Bonusly: 8

Arithmetic: 51 + 31 + 8 = 90 deals.

Structured-tag versus free-text disagreement

19 deals clearly disagree with the structured `closed_lost_tag` based on the free-text reason.

Two patterns most worth acting on

1. No-decision losses are the largest category: 32 of 90 deals. Many reasons are `MIA`, `unresponsive`, no response, or generic statements that the buyer is not moving forward. This leaves the underlying loss cause unknown.

2. Timing is substantial: 21 of 90 deals explicitly cite a pause, future priorities, or reconnecting next year/2027. The free text frequently provides a future re-engagement point rather than a permanent rejection.
pipeline-tiering0.80

? The Question

Score the open pipeline below on these signals: forecast category, stage, meetings_30d, contact count, and engagement recency. Assign exactly one tier per deal: LOCK, ACTION, BUILD, REVIVE, WATCH, or RISKY (RISKY = the forecast category disagrees with the engagement evidence). Never tier a deal LOCK if it has zero meetings_30d. Do NOT list every deal. Return ONLY a compact JSON object with exactly these keys: - "tier_counts": object mapping each tier to the number of deals in it (must sum to the total deal count), - "tier_examples": object mapping each tier to up to 3 example deal aliases from the data, - "risky_deals": array of the deal aliases you tiered RISKY, - "lock_violations": number of deals tiered LOCK that have zero meetings_30d (should be 0), - "pipeline_shape": one short paragraph on the overall pipeline shape. Note: inbound_emails_30d is 0 for every row due to a data defect, so treat meetings_30d as the inbound signal.
cost $0.1732 · 69s · in 6 / out 7,339 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

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{"tier_counts":{"LOCK":3,"ACTION":11,"BUILD":37,"REVIVE":18,"WATCH":50,"RISKY":36},"tier_examples":{"LOCK":["Deal-D348E1","Deal-C26D20","Deal-403845"],"ACTION":["Deal-25F752","Deal-944310","Deal-3974EB"],"BUILD":["Deal-D73B89","Deal-523604","Deal-CA7DC0"],"REVIVE":["Deal-2D1F1B","Deal-66D1FC","Deal-950043"],"WATCH":["Deal-6787C2","Deal-C9C286","Deal-332637"],"RISKY":["Deal-E53952","Deal-5408B0","Deal-9AAE5F"]},"risky_deals":["Deal-E53952","Deal-5408B0","Deal-9AAE5F","Deal-C61CF7","Deal-62D607","Deal-C6D97A","Deal-7B3B0F","Deal-F9A08A","Deal-0660B4","Deal-BA571A","Deal-FC22A3","Deal-7BBDFA","Deal-60C2C2","Deal-4A13AD","Deal-8AD4A5","Deal-15D24F","Deal-9D0060","Deal-690476","Deal-635B8E","Deal-ED725A","Deal-55164C","Deal-3BA5EA","Deal-5FDCE4","Deal-F336B6","Deal-5EED42","Deal-BA3DDC","Deal-7599B8","Deal-F9A3C1","Deal-FA32A0","Deal-547B2B","Deal-B7EBD1","Deal-A2B47C","Deal-2465CE","Deal-A5E80A","Deal-FD9F4E","Deal-499BF6"],"lock_violations":0,"pipeline_shape":"The pipeline is engagement-weighted but forecast-fragile: 3 LOCK + 11 ACTION + 37 BUILD = 51 actively supported deals, while 18 REVIVE + 50 WATCH = 68 deals needing progression or monitoring; 36 RISKY deals have forecast categories that exceed their stage and/or meeting evidence. Arithmetic: 3 + 11 + 37 + 18 + 50 + 36 = 155 total deals."}
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 $0.0571 · 11s · in 3 / out 1,411 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

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[
  {
    "transcript_id": "TX-001",
    "deal_alias": "Deal-CFE7F4",
    "why_buys": [
      "automating anniversary and birthday awards",
      "our HR team of three cannot keep up with it manually"
    ],
    "pain_points": [
      "Right now we track everything in a spreadsheet, and people slip through the cracks.",
      "we need SSO and audit logs for IT to sign off"
    ],
    "stakeholders": [
      "Prospect (VP People)",
      "Prospect (HR Admin)"
    ],
    "budget_signal": "We have about $40k earmarked for engagement tools this fiscal year.",
    "timeline_signal": "Ideally we would have this live before open enrollment in November.",
    "competitor_mentioned": "Achievers",
    "next_step": "security review on September 12",
    "objections": [
      "we need SSO and audit logs for IT to sign off",
      "We looked at Achievers last year, but it was too heavy for a team our size."
    ],
    "confidence": "high"
  },
  {
    "transcript_id": "TX-002",
    "deal_alias": "Deal-70BB30",
    "why_buys": [
      "We want to tie recognition to retention for our hourly workforce"
    ],
    "pain_points": [
      "regretted turnover there is over 30%",
      "Integration with Workday has to be rock solid"
    ],
    "stakeholders": [
      "Prospect (Head of Total Rewards)",
      "Prospect (CFO)"
    ],
    "budget_signal": "Finance has approved a $25k pilot budget for this quarter.",
    "timeline_signal": "We want a decision by end of September.",
    "competitor_mentioned": null,
    "next_step": "send the pilot agreement and we'll route it to legal this week",
    "objections": [
      "Integration with Workday has to be rock solid — that's my one condition."
    ],
    "confidence": "high"
  },
  {
    "transcript_id": "TX-003",
    "deal_alias": "Deal-530B50",
    "why_buys": [
      "We need to make recognition visible across our 12 retail locations.",
      "Store managers have zero budget autonomy for on-the-spot recognition today."
    ],
    "pain_points": [
      "Store managers have zero budget autonomy for on-the-spot recognition today."
    ],
    "stakeholders": [
      "Prospect (People Ops Manager)"
    ],
    "budget_signal": null,
    "timeline_signal": "Honestly there's no rush on our side until Q1.",
    "competitor_mentioned": "Bucketlist",
    "next_step": "schedule a call with our CEO — I'll send two times",
    "objections": [
      "The CEO has to be sold first — she decides anything people-related."
    ],
    "confidence": "medium"
  },
  {
    "transcript_id": "TX-004",
    "deal_alias": "Deal-180D02",
    "why_buys": [
      "We want to consolidate three separate recognition tools into one."
    ],
    "pain_points": [
      "We're paying for three tools and none of them talk to our HRIS.",
      "The security review took three months for our last vendor"
    ],
    "stakeholders": [
      "Prospect (VP People)",
      "Prospect (IT Security Lead)"
    ],
    "budget_signal": "If it's under $15k annually, I can approve it without going to the board.",
    "timeline_signal": "Our procurement cycle runs six to eight weeks minimum.",
    "competitor_mentioned": null,
    "next_step": null,
    "objections": [
      "The security review took three months for our last vendor — that's my hesitation.",
      "Maybe — I need to check her calendar, no promises."
    ],
    "confidence": "medium"
  },
  {
    "transcript_id": "TX-005",
    "deal_alias": "Deal-F8767A",
    "why_buys": [
      "automate service milestones",
      "give us analytics on recognition equity across departments"
    ],
    "pain_points": [
      "Our night-shift teams feel invisible — their engagement scores run 20 points lower.",
      "Our exec team is skeptical after a failed rollout two years ago."
    ],
    "stakeholders": [
      "Prospect (HR Director)",
      "Prospect (People Ops Coordinator)"
    ],
    "budget_signal": "We have $12k approved under our engagement line.",
    "timeline_signal": "We need this running before our January all-hands.",
    "competitor_mentioned": "Nectar",
    "next_step": "present to our exec team on October 2",
    "objections": [
      "We're mid-pilot with Nectar right now, so you'd need to beat that experience.",
      "Our exec team is skeptical after a failed rollout two years ago."
    ],
    "confidence": "high"
  },
  {
    "transcript_id": "TX-006",
    "deal_alias": "Deal-EE195F",
    "why_buys": [
      "I'd love to cut the admin time on service awards.",
      "Budget isn't the issue — time is."
    ],
    "pain_points": [
      "I personally spend five hours a month ordering and shipping plaques.",
      "our COO usually prefers building things in-house"
    ],
    "stakeholders": [
      "Prospect (HR Manager)"
    ],
    "budget_signal": null,
    "timeline_signal": "Our fiscal year resets in February, so a Q1 start is realistic.",
    "competitor_mentioned": null,
    "next_step": "send the one-page overview and I'll forward it to our COO this week",
    "objections": [
      "our COO usually prefers building things in-house"
    ],
    "confidence": "medium"
  }
]
next-to-close0.75

? The Question

Which 3 deals are closest to signature right now? Use the CRM data and the Slack thread; Slack often has the real status before the CRM does. For each: deal, amount, why it is close, what is left. Under 250 words.
cost $0.0437 · 7s · in 3 / out 437 tok
✓ exactly_three✗ top3_named✓ slack_first✓ blocked_excluded

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
1. Deal-547B2B — $11,200  
Why it is close: Redlines are clean, the signing page is out, and the VP People said they will sign tomorrow; Slack calls it “signature-imminent.”  
What is left: Customer signature.  
Arithmetic: $11,200; CRM close date is 2026-09-11, 7 days after the 2026-09-04 Slack update.

2. Deal-403845 — $9,000  
Why it is close: Slack says the order form is with the finance team; CRM is DS5/COMMIT.  
What is left: Finance approval/signature.  
Arithmetic: $9,000; CRM close date is 2026-09-11, 7 days after the Slack update.

3. Deal-A2B47C — $6,360  
Why it is close: Slack says it is “still warm,” with only normal legal-review pace; CRM is DS5/COMMIT.  
What is left: Completion of legal review and signature.  
Arithmetic: $6,360; CRM close date is 2026-09-11, 7 days after the Slack update.
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 $0.0435 · 7s · in 3 / out 506 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
Candidates

- Deal-EC3025 — “We need SCIM user provisioning — our security team will not approve any tool that requires manual account management.”
  - Classification: Real gap.
  - Evidence: Product docs state “SCIM user provisioning … [is] NOT currently listed as supported capabilities.”
  - Amount: Not provided.

- Deal-D0D6B5 — “Our payroll runs on ADP Workforce Now and I don't see ADP anywhere in your integration list — that's a dealbreaker for us.”
  - Classification: Real gap.
  - Evidence: Product docs state “ADP Workforce Now integrations are NOT currently listed as supported capabilities.”
  - Amount: Not provided.

- Deal-CFE7F4 — “I tried to build a custom report combining recognition frequency with turnover and couldn't — is that possible on our current plan?”
  - Classification: Plan gate.
  - Evidence: The custom report builder is available on Enterprise only.
  - Amount: Not provided.

- Deal-84DBA6 — “We turned on the Slack integration in July, but half our managers still haven't given a single recognition — nobody ever trained them on it.”
  - Classification: Rollout/enablement issue.
  - Evidence: Slack integration is supported on all plans; the stated issue is lack of manager training.
  - Amount: Not provided.

Summary — real product gaps only

- Deal-EC3025: SCIM user provisioning. Amount not provided.
- Deal-D0D6B5: ADP Workforce Now integration. Amount not provided.

Arithmetic: 1 SCIM gap + 1 ADP Workforce Now gap = 2 real product gaps.
rep-performance
stale-pipeline-by-rep0.83

? 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 $0.1823 · 57s · in 6 / out 7,531 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 = latest logged email/call/meeting is before 2026-08-29 (more than 7 days before 2026-09-05).

Bryce Harmon
- Deal-2D1F1B — DS1 — $240,000 — 2026-09-05 − 2026-06-16 = 81 days
- Deal-66D1FC — DS1 — $99,000 — 2026-09-05 − 2026-08-20 = 16 days
- Deal-950043 — DS1 — $70,000 — 2026-09-05 − 2026-08-17 = 19 days
- Deal-B23205 — DS1 — $45,000 — 2026-09-05 − 2026-08-20 = 16 days
- Deal-7BBDFA — DS3 — $37,440 — 2026-09-05 − 2026-07-21 = 46 days
- Deal-332637 — DS2 — $36,000 — 2026-09-05 − 2026-08-27 = 9 days
- Deal-1BEEBF — DS1 — $31,500 — 2026-09-05 − 2026-08-17 = 19 days
- Deal-C5658B — DS1 — $23,400 — 2026-09-05 − 2026-08-20 = 16 days
- Deal-40522D — DS3 — $21,000 — 2026-09-05 − 2026-08-17 = 19 days
- Deal-F0EBBB — DS3 — $11,400 — 2026-09-05 − 2026-08-12 = 24 days
- Deal-E25A09 — DS1 — $6,000 — 2026-09-05 − 2026-08-27 = 9 days
- Deal-C9C286 — DS2 — $5,502 — 2026-09-05 − 2026-08-27 = 9 days
- Deal-012CB1 — DS1 — $1 — 2026-09-05 − 2026-08-13 = 23 days

Bryce Harmon: 13 stale deals; $240,000 + $99,000 + $70,000 + $45,000 + $37,440 + $36,000 + $31,500 + $23,400 + $21,000 + $11,400 + $6,000 + $5,502 + $1 = $626,243.

Dana Mercer
- Deal-44EA29 — DS2 — $60,000 — 2026-09-05 − 2026-08-26 = 10 days
- Deal-E51FB7 — DS2 — $43,875 — 2026-09-05 − 2026-08-24 = 12 days
- Deal-B42F46 — DS1 — $27,000 — 2026-09-05 − 2026-08-17 = 19 days
- Deal-BA3DDC — DS3 — $23,400 — 2026-09-05 − 2026-08-21 = 15 days
- Deal-9DDE86 — DS2 — $20,000 — 2026-09-05 − 2026-08-21 = 15 days
- Deal-215CCA — DS3 — $18,900 — 2026-09-05 − 2026-08-19 = 17 days
- Deal-5EED42 — DS3 — $16,250 — 2026-09-05 − 2026-08-25 = 11 days
- Deal-57887A — DS2 — $15,000 — 2026-09-05 − 2026-08-28 = 8 days
- Deal-B7EBD1 — DS5 — $9,000 — 2026-09-05 − 2026-08-20 = 16 days
- Deal-3974EB — DS4 — $9,000 — 2026-09-05 − 2026-08-28 = 8 days
- Deal-F40F04 — DS2 — $8,100 — 2026-09-05 − 2026-08-21 = 15 days
- Deal-87DDD1 — DS1 — $5,000 — 2026-09-05 − 2026-08-17 = 19 days
- Deal-F336B6 — DS3 — $4,200 — 2026-09-05 − 2026-08-21 = 15 days
- Deal-0660B4 — DS4 — $1,920 — 2026-09-05 − 2026-08-20 = 16 days

Dana Mercer: 14 stale deals; $60,000 + $43,875 + $27,000 + $23,400 + $20,000 + $18,900 + $16,250 + $15,000 + $9,000 + $9,000 + $8,100 + $5,000 + $4,200 + $1,920 = $261,645.

Alex Franklin
- Deal-CC08D1 — DS1 — $24,000 — 2026-09-05 − 2026-08-20 = 16 days
- Deal-E73427 — DS3 — $18,000 — 2026-09-05 − 2026-08-26 = 10 days
- Deal-885F45 — DS2 — $9,300 — 2026-09-05 − 2026-08-24 = 12 days
- Deal-C2FF3C — DS1 — $8,316 — 2026-09-05 − 2026-08-26 = 10 days
- Deal-3EED2C — DS2 — $7,200 — no logged email, call, or meeting; days since last contact unavailable
- Deal-0D2F7A — DS3 — $5,100 — 2026-09-05 − 2026-08-24 = 12 days
- Deal-6C60D4 — DS3 — $4,800 — 2026-09-05 − 2026-08-24 = 12 days
- Deal-13FEBD — DS2 — $4,680 — 2026-09-05 − 2026-08-24 = 12 days
- Deal-9D0060 — DS3 — $3,840 — 2026-09-05 − 2026-08-24 = 12 days
- Deal-690476 — DS2 — $3,600 — 2026-09-05 − 2026-08-18 = 18 days
- Deal-C6D97A — DS4 — $3,240 — 2026-09-05 − 2026-08-28 = 8 days
- Deal-EE195F — DS3 — $3,120 — 2026-09-05 − 2026-08-28 = 8 days
- Deal-278DEC — DS3 — $2,700 — 2026-09-05 − 2026-08-28 = 8 days
- Deal-635B8E — DS3 — $2,600 — 2026-09-05 − 2026-08-18 = 18 days
- Deal-6883F3 — DS1 — $2,400 — 2026-09-05 − 2026-08-20 = 16 days
- Deal-4A13AD — DS3 — $2,160 — 2026-09-05 − 2026-08-10 = 26 days
- Deal-F67D31 — DS2 — $1,800 — 2026-09-05 − 2026-08-28 = 8 days
- Deal-5FDCE4 — DS3 — $1,600 — 2026-09-05 − 2026-08-24 = 12 days
- Deal-BA571A — DS4 — $1,080 — 2026-09-05 − 2026-08-18 = 18 days

Alex Franklin: 19 stale deals; $24,000 + $18,000 + $9,300 + $8,316 + $7,200 + $5,100 + $4,800 + $4,680 + $3,840 + $3,600 + $3,240 + $3,120 + $2,700 + $2,600 + $2,400 + $2,160 + $1,800 + $1,600 + $1,080 = $109,536.

Cole Ingram
- Deal-D04904 — DS2 — $58,529.25 — 2026-09-05 − 2026-08-25 = 11 days
- Deal-B25F40 — DS3 — $40,000 — 2026-09-05 − 2026-08-28 = 8 days
- Deal-813836 — DS2 — $32,175 — 2026-09-05 − 2026-08-25 = 11 days
- Deal-1BA595 — DS2 — $31,750 — 2026-09-05 − 2026-08-25 = 11 days
- Deal-CFE1E8 — DS3 — $18,000 — 2026-09-05 − 2026-08-25 = 11 days
- Deal-CD47A6 — DS2 — $12,168 — 2026-09-05 − 2026-08-25 = 11 days
- Deal-627646 — DS3 — $11,193 — 2026-09-05 − 2026-08-25 = 11 days
- Deal-FF809F — DS2 — $7,781.20 — 2026-09-05 − 2026-08-25 = 11 days
- Deal-AF932D — DS2 — $7,225.40 — 2026-09-05 − 2026-08-25 = 11 days
- Deal-A71728 — DS2 — $6,947.50 — 2026-09-05 − 2026-08-25 = 11 days
- Deal-8BC9F5 — DS2 — $5,616 — 2026-09-05 − 2026-08-26 = 10 days
- Deal-175395 — DS3 — $4,779.88 — 2026-09-05 − 2026-08-25 = 11 days
- Deal-481E24 — DS3 — $4,140 — 2026-09-05 − 2026-08-26 = 10 days
- Deal-C7F9BF — DS2 — $3,360 — 2026-09-05 − 2026-08-25 = 11 days
- Deal-2F3A66 — DS3 — $3,334.80 — 2026-09-05 − 2026-08-25 = 11 days
- Deal-342E96 — DS2 — $2,700 — 2026-09-05 − 2026-08-12 = 24 days
- Deal-E568D5 — DS3 — $1,875 — 2026-09-05 − 2026-08-25 = 11 days
- Deal-FD9F4E — DS5 — $1,330 — 2026-09-05 − 2026-08-26 = 10 days

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

Elena Sinclair
- Deal-57FF13 — DS1 — $2,100 — no logged email, call, or meeting; days since last contact unavailable

Elena Sinclair: 1 stale deal; $2,100 = $2,100.
activity-mix-vs-outcome0.80

? 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 $0.2213 · 93s · in 12 / out 9,835 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
30-day window: 2026-08-06 through 2026-09-05, inclusive.

Efficiency rank (lowest total activities per DS2 entry = most efficient):

1. Alex Franklin
   - Emails: 347; calls: 38; meetings: 42
   - Total activities: 347 + 38 + 42 = 427
   - Activity mix: emails 347 / 427 = 81.3%; calls 38 / 427 = 8.9%; meetings 42 / 427 = 9.8%
   - DS2 entries: 18 — Deal-403845, Deal-1FC049, Deal-3EED2C, Deal-7FA0C3, Deal-E531A6, Deal-5296C9, Deal-36C33F, Deal-F436DA, Deal-D1E6C2, Deal-CA5E44, Deal-4F775F, Deal-898FC5, Deal-46988D, Deal-E73427, Deal-92D97D, Deal-EE195F, Deal-D9A72E, Deal-1CCE5C
   - Activities per DS2 entry: 427 / 18 = 23.72

2. Bryce Harmon
   - Emails: 162; calls: 0; meetings: 43
   - Total activities: 162 + 0 + 43 = 205
   - Activity mix: emails 162 / 205 = 79.0%; calls 0 / 205 = 0.0%; meetings 43 / 205 = 21.0%
   - DS2 entries: 3 — Deal-25F752, Deal-D73B89, Deal-CA7DC0
   - Activities per DS2 entry: 205 / 3 = 68.33

3. Farid Osman
   - Emails: 38; calls: 0; meetings: 34
   - Total activities: 38 + 0 + 34 = 72
   - Activity mix: emails 38 / 72 = 52.8%; calls 0 / 72 = 0.0%; meetings 34 / 72 = 47.2%
   - DS2 entries: 1 — Deal-499BF6
   - Activities per DS2 entry: 72 / 1 = 72.00

4. Cole Ingram
   - Emails: 96; calls: 14; meetings: 1
   - Total activities: 96 + 14 + 1 = 111
   - Activity mix: emails 96 / 111 = 86.5%; calls 14 / 111 = 12.6%; meetings 1 / 111 = 0.9%
   - DS2 entries: 2 — Deal-1BA595, Deal-42326B
   - Activities per DS2 entry: 111 / 2 = 55.50

Correction to rank order: Cole Ingram ranks ahead of Farid Osman because 55.50 is lower than 72.00.

Final ranked order:
1. Alex Franklin — 23.72
2. Cole Ingram — 55.50
3. Bryce Harmon — 68.33
4. Farid Osman — 72.00
5. Dana Mercer — 113.00

Dana Mercer
- Emails: 84; calls: 18; meetings: 11
- Total activities: 84 + 18 + 11 = 113
- Activity mix: emails 84 / 113 = 74.3%; calls 18 / 113 = 15.9%; meetings 11 / 113 = 9.7%
- DS2 entries: 1 — Deal-F336B6
- Activities per DS2 entry: 113 / 1 = 113.00

Elena Sinclair
- Emails: 0; calls: 0; meetings: 0
- Total activities: 0
- Activity mix: not applicable; there were no activities.
- DS2 entries: 0
- Activities per DS2 entry: not applicable because there were no DS2 entries.
- Not ranked because the efficiency ratio is undefined.

Most efficient rep: Alex Franklin, at 23.72 activities per DS2 entry.

Highest-volume rep: Alex Franklin, with 427 total activities.

They do not differ.
ae-qtd-scorecardPASS 1.00

? The Question

Build a QTD scorecard for the named AE as of 2026-09-05: bookings vs quota with attainment percent, new vs expansion split, active pipeline by stage with amount, rolling 90-day DS2-to-won rate, win and loss counts with top loss reason, activity volume by type in the last 30 days. Exclude any closed-won deal dated before the quarter. Close with three coaching observations grounded in the numbers.
cost $0.1140 · 33s · in 6 / out 3,913 tok
✓ bookings✓ quota✓ attainment✓ new_expansion_split✓ top_loss_reason✓ coaching_obs

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
QTD scorecard — Alex Franklin as of 2026-09-05
Quarter: 2026-Q3 (2026-07-01 through 2026-09-05)

Bookings vs. quota
- Bookings: $150,000
  - Arithmetic: $40,000 + $20,000 + $35,000 + $21,000 + $12,000 + $11,000 + $6,500 + $4,500 = $150,000
  - Excluded: Deal-B3E6F1 ($24,000), closed-won 2026-06-20, before the quarter.
- Quota: $200,000
- Attainment: 75.0%
  - Arithmetic: $150,000 ÷ $200,000 × 100 = 75.0%

New vs. expansion bookings
- New: $113,500
  - Arithmetic: $40,000 + $35,000 + $21,000 + $11,000 + $6,500 = $113,500
- Expansion: $36,500
  - Arithmetic: $20,000 + $12,000 + $4,500 = $36,500
- Mix: 75.7% new / 24.3% expansion
  - New: $113,500 ÷ $150,000 = 75.7%
  - Expansion: $36,500 ÷ $150,000 = 24.3%

Active pipeline
- DS1: 20 deals, $284,621
- DS2: 26 deals, $288,160
- DS3: 65 deals, $536,955
- DS4: 5 deals, $23,574
- DS5: 5 deals, $45,730
- Total active pipeline: 121 deals, $1,179,040
  - Arithmetic: $284,621 + $288,160 + $536,955 + $23,574 + $45,730 = $1,179,040

Rolling 90-day DS2-to-won rate
- 8 won / (8 won + 27 lost) = 22.9%
- Arithmetic: 8 ÷ 35 × 100 = 22.9%
- This uses closed outcomes whose entered_ds2 date falls within the rolling 90-day window ending 2026-09-05.

Closed outcomes
- Wins: 8
- Losses: 27
- Top loss reason: Lost- Timing (1 year or more), 13 losses
  - Arithmetic: 13 ÷ 27 = 48.1% of losses

Activity volume, last 30 days
- Emails: 799
- Calls: 112
- Meetings: 123
- Notes: 50
- Total activities: 1,084
  - Arithmetic: 799 + 112 + 123 + 50 = 1,084

Coaching observations
1. Attainment is 75.0%, leaving a $50,000 gap to quota ($200,000 − $150,000). The active DS5 pipeline is only $45,730, so the current late-stage amount alone is below the remaining quota gap by $4,270.

2. Timing is the primary closed-lost issue: 13 of 27 losses (48.1%) are coded Lost- Timing (1 year or more). Tighten timing qualification and establish future follow-up paths rather than advancing long-horizon opportunities.

3. The active pipeline is concentrated in DS3: $536,955 of $1,179,040 (45.5%) across 65 deals. Conversion from DS2 to won is 22.9%; prioritize moving qualified DS3 opportunities such as Deal-4F775F ($18,000) and Deal-5D8CEE ($7,200) forward while validating weaker opportunities before additional effort.
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 $0.0669 · 19s · in 6 / out 1,940 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
Active cutoff: 2026-07-07 (2026-09-05 − 60 days). Active contacts exclude former contacts.

- Deal-EC3025 (single-threaded)
  - Amount: not provided; Stage: not provided
  - Active-contact arithmetic: 1 = CT-047C54; CT-F2C1AE excluded (former)
  - Active contact count: 1
  - Personas present: champion
  - Personas missing: economic buyer, HR admin, IT security, finance
  - Most valuable persona to add given stage: cannot determine; stage not provided
  - On-file unengaged fit: CT-6827DB — Chief People Officer, economic buyer

- Deal-92D97D (single-threaded)
  - Amount: not provided; Stage: not provided
  - Active-contact arithmetic: 1 = CT-01F5B4; CT-A902AE excluded (last engaged 2026-06-01, before cutoff)
  - Active contact count: 1
  - Personas present: HR admin
  - Personas missing: economic buyer, champion, IT security, finance
  - Most valuable persona to add given stage: cannot determine; stage not provided
  - On-file unengaged fit: none on file

- Deal-50D386 (under-threaded)
  - Amount: not provided; Stage: not provided
  - Active-contact arithmetic: 2 = CT-AA41B2 + CT-B9C35B
  - Active contact count: 2
  - Personas present: champion, HR admin
  - Personas missing: economic buyer, IT security, finance
  - Most valuable persona to add given stage: cannot determine; stage not provided
  - On-file unengaged fit: CT-A1C4B3 — Chief People Officer, economic buyer

- Deal-D0D6B5 (under-threaded: all active contacts are one persona)
  - Amount: not provided; Stage: not provided
  - Active-contact arithmetic: 3 = CT-87CED4 + CT-DE6D7C + CT-FD70B2
  - Active contact count: 3
  - Personas present: champion
  - Personas missing: economic buyer, HR admin, IT security, finance
  - Most valuable persona to add given stage: cannot determine; stage not provided
  - On-file unengaged fit: CT-1FA4DB — Chief People Officer, economic buyer

- Deal-5BFE3B (under-threaded)
  - Amount: not provided; Stage: not provided
  - Active-contact arithmetic: 2 = CT-57123B + CT-5CE757
  - Active contact count: 2
  - Personas present: champion
  - Personas missing: economic buyer, HR admin, IT security, finance
  - Most valuable persona to add given stage: cannot determine; stage not provided
  - On-file unengaged fit: none on file

- Deal-36C33F (single-threaded)
  - Amount: not provided; Stage: not provided
  - Active-contact arithmetic: 1 = CT-4FE556; CT-405B45 and CT-86B22F excluded (former)
  - Active contact count: 1
  - Personas present: IT security
  - Personas missing: economic buyer, champion, HR admin, finance
  - Most valuable persona to add given stage: cannot determine; stage not provided
  - On-file unengaged fit: CT-1DB73E — Chief People Officer, economic buyer

- Deal-885F45 (under-threaded)
  - Amount: not provided; Stage: not provided
  - Active-contact arithmetic: 2 = CT-51C81E + CT-D9A0E8
  - Active contact count: 2
  - Personas present: economic buyer, champion
  - Personas missing: HR admin, IT security, finance
  - Most valuable persona to add given stage: cannot determine; stage not provided
  - On-file unengaged fit: CT-B3F25D — IT Security Lead, IT security

- Deal-FCBE5B (single-threaded)
  - Amount: not provided; Stage: not provided
  - Active-contact arithmetic: 1 = CT-4A5317
  - Active contact count: 1
  - Personas present: champion
  - Personas missing: economic buyer, HR admin, IT security, finance
  - Most valuable persona to add given stage: cannot determine; stage not provided
  - On-file unengaged fit: none on file

- Deal-5408B0 (under-threaded)
  - Amount: not provided; Stage: not provided
  - Active-contact arithmetic: 2 = CT-D33AE4 + CT-8742FD
  - Active contact count: 2
  - Personas present: champion, HR admin
  - Personas missing: economic buyer, IT security, finance
  - Most valuable persona to add given stage: cannot determine; stage not provided
  - On-file unengaged fit: CT-07FA76 — Chief People Officer, economic buyer

- Deal-C6D97A (under-threaded: all active contacts are one persona)
  - Amount: not provided; Stage: not provided
  - Active-contact arithmetic: 3 = CT-223DDC + CT-B03555 + CT-4E8A2B
  - Active contact count: 3
  - Personas present: champion
  - Personas missing: economic buyer, HR admin, IT security, finance
  - Most valuable persona to add given stage: cannot determine; stage not provided
  - On-file unengaged fit: none on file

- Deal-F9A08A (single-threaded)
  - Amount: not provided; Stage: not provided
  - Active-contact arithmetic: 1 = CT-931B10; CT-913581 excluded (last engaged 2026-06-20, before cutoff)
  - Active contact count: 1
  - Personas present: champion
  - Personas missing: economic buyer, HR admin, IT security, finance
  - Most valuable persona to add given stage: cannot determine; stage not provided
  - On-file unengaged fit: CT-697541 — Chief People Officer, economic buyer
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 $0.0604 · 14s · in 6 / out 1,163 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
First-five-minute lead:
- 8/10 calls lead with a quantified customer outcome/case study. “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.”
- 1/10 starts with an agenda in Deal-403845: “I put together a short agenda — security review first, then pricing.”
- 1/10 starts with pricing in Deal-1E2498: “You asked for straight pricing last time, so let's start there.”

Three most common objections and handling:
- Budget locked: 4 occurrences (Deal-D348E1, Deal-547B2B, Deal-60C2C2, Deal-84DBA6). He reframes funding around turnover savings: “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.”
- Revisit next quarter / workload: 3 occurrences (Deal-5408B0, Deal-C61CF7, Deal-D9A12F). He proposes a departmental pilot: “Makes sense. What if we scope a 90-day pilot with one department so you have internal data before next quarter's planning?”
- Existing spreadsheet and quarterly gift cards: 3 occurrences (Deal-403845, Deal-EDC141, Deal-1E2498). He contrasts manual work with automation and analytics: “Spreadsheets work until they scale — the difference is automation: milestones fire without HR lifting a finger, and you get analytics on who is being recognized.”

Concrete next step agreed:
- 7/10 calls = 70%.
- Arithmetic: 7 agreed next steps ÷ 10 calls = 0.70 = 70%.

Competitors raised by prospects:
- Awardco — Deal-547B2B. “We're also in late talks with Awardco — their rewards catalog looks bigger than yours.”
- Kudos — Deal-EDC141. “How are you different from Kudos? Our CEO used them at her last company.”

Coaching notes:
- The case-study opening is used in 8/10 calls and supports a 70% next-step rate; preserve it, but tailor the opening when the prospect has explicitly asked for pricing or an agenda.
- The rep converts budget and timing objections into next steps, but does not secure one after the committee/no-urgency responses in Deal-403845, Deal-EDC141, and Deal-84DBA6. Add a specific, low-commitment follow-up ask for those situations.
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 $0.1001 · 22s · in 6 / out 3,607 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

Only close dates from 2026-07-01 through 2026-09-30 are included.

- COMMIT: 7 deals, $44,729
  - Arithmetic: $11,200 + $9,000 + $9,000 + $6,360 + $5,400 + $2,520 + $1,249 = $44,729

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

- Weighted forecast: $115,976.75
  - Arithmetic: 100% × $44,729 + 35% × $203,565 + 0% × PIPELINE
  - $44,729 + $71,247.75 = $115,976.75

- PIPELINE inside Q3: 23 deals, $201,637.40
  - Included at $0 under the weighting rule.

## Excluded for being outside Q3

32 deals totaling $227,575, all with October close dates.

- Arithmetic: $43,875 + $18,000 + $17,000 + $13,770 + $10,800 + $9,000 + $9,000 + $7,920 + $7,690 + $7,500 + $7,200 + $5,700 + $5,400 + $5,400 + $5,400 + $5,400 + $5,160 + $4,800 + $4,400 + $4,300 + $4,000 + $3,600 + $3,600 + $3,600 + $3,300 + $2,400 + $1,800 + $1,800 + $1,680 + $1,600 + $1,400 + $1,080 = $227,575

## Top 5 BEST_CASE deals inside Q3

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

## Data quality

Most records have blank `owner` values, preventing reliable ownership-based forecast accountability. Most records have `why_buys_chars` equal to 0, leaving little evidence of documented buyer rationale or qualification. The extract contains a fractional amount (`Deal-42326B` at $2,480.40), which requires an explicit currency/rounding policy. The extract has no company/account field, so duplicate opportunities or multiple deals for the same company cannot be identified.
aha-moment-2x2PASS 1.00

? The Question

Test the activation hypothesis on the company cohort below: companies with 5+ unique givers (m1_users >= 5) AND 1+ successful redemption (m1_redemptions >= 1) in their first calendar month retain better at 24 months than those with one signal or neither. Every company in this extract is already 25+ months old, so current_status = 'active' means retained at 24 months. Report the full 2x2 (cohort size and 24-month retention rate for: both signals, givers-only, redemption-only, neither), state how many companies were excluded from the denominator and why (if none, say so), name the single signal with the largest retention lift, and state what this does and does not prove.
cost $0.1879 · 48s · in 15 / out 4,543 tok
✓ both_66✓ givers_47✓ redemp_31✓ neither_40✓ excluded_0✓ largest_lift_givers

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
| First-month signal cohort | Cohort size | Retained at 24 months (`active`) | 24-month retention rate |
|---|---:|---:|---:|
| Both: `m1_users >= 5` and `m1_redemptions >= 1` | 47 | 31 | 31 / 47 = **66.0%** |
| Givers-only: `m1_users >= 5` and `m1_redemptions = 0` | 49 | 23 | 23 / 49 = **46.9%** |
| Redemption-only: `m1_users < 5` and `m1_redemptions >= 1` | 29 | 9 | 9 / 29 = **31.0%** |
| Neither: `m1_users < 5` and `m1_redemptions = 0` | 95 | 38 | 38 / 95 = **40.0%** |

Denominator: 47 + 49 + 29 + 95 = **220 companies**.

Excluded: **0 companies**. All 220 companies have `m1_users`, `m1_redemptions`, and `current_status`; per the extract, all are 25+ months old. Only `active` is counted as retained; `cancelled` and `non_renewing` are not retained.

The hypothesis is descriptively supported: the both-signals cohort has the highest observed 24-month retention rate, at **66.0%**, compared with **46.9%**, **31.0%**, and **40.0%** for the other three cohorts.

Single signal with the largest retention lift: **5+ unique givers (`m1_users >= 5`)**.

- Giver-signal companies: (31 + 23) / (47 + 49) = 54 / 96 = **56.3%**
- No-giver-signal companies: (9 + 38) / (29 + 95) = 47 / 124 = **37.9%**
- Giver-signal lift: 56.3% − 37.9% = **+18.3 percentage points**

For comparison:

- Redemption-signal companies: (31 + 9) / (47 + 29) = 40 / 76 = **52.6%**
- No-redemption-signal companies: (23 + 38) / (49 + 95) = 61 / 144 = **42.4%**
- Redemption-signal lift: 52.6% − 42.4% = **+10.3 percentage points**

This shows an association in this extract: both signals co-occur with the highest observed 24-month retention, and the giver signal has the larger standalone retention lift. It does not prove that either signal causes retention, that the thresholds are optimal, that the differences are statistically significant, or that the pattern generalizes beyond these 220 companies. Unmeasured differences between cohorts may explain some or all of the observed retention differences.
arr-rollforward-reconciliationPASS 1.00

? The Question

Reconcile ARR between CRM company records and billing subscriptions as of 2026-09-05. Billing ARR is MRR x 12. Report both totals, the variance, and a decomposition into buckets (status mismatch, rounding, missing records, other) that sums exactly to the variance. List each mismatched account with a suggested owner. Business rule: any subscription with a term other than 12 months must have cf_agreement_end_date populated; list violations.
cost $0.0851 · 24s · in 6 / out 3,085 tok
✓ billing_total✓ crm_total✓ variance✓ buckets_sum✓ violations_listed

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
CRM ARR total: $603,581.76

Billing ARR total: $604,739.28  
Arithmetic: $50,394.94 active billing MRR × 12 = $604,739.28

Variance (CRM − Billing): -$1,157.52  
Arithmetic: $603,581.76 − $604,739.28 = -$1,157.52

Variance decomposition (CRM − Billing):

| Bucket | Amount | Arithmetic |
|---|---:|---|
| Status mismatch | $13,158.48 | $4,905.24 + $8,253.24 |
| Rounding | $36.00 | $16.00 + $20.00 |
| Missing records | -$11,952.00 | $16,497.24 − $28,449.24 |
| Other | -$2,400.00 | $24,396.00 − $26,796.00 |
| Total variance | -$1,157.52 | $13,158.48 + $36.00 − $11,952.00 − $2,400.00 |

Mismatched accounts:

| Company alias | CRM ARR | Billing ARR | Difference (CRM − Billing) | Reconciliation issue | Suggested owner |
|---|---:|---:|---:|---|---|
| C-0C8323BF | $4,905.24 | $0.00 | $4,905.24 | Status mismatch: `SUB-000E` is `cancelled`; CRM retains ARR | Billing Operations |
| C-0DC4FB8C | $8,253.24 | $0.00 | $8,253.24 | Status mismatch: `SUB-000F` is `cancelled`; CRM retains ARR | Billing Operations |
| C-0D66DF9E | $23,200.00 | $23,184.00 | $16.00 | Rounding | RevOps |
| C-14D70CE0 | $18,200.00 | $18,180.00 | $20.00 | Rounding | RevOps |
| C-0D5BBE3A | $16,497.24 | $0.00 | $16,497.24 | Missing billing subscription record | Billing Operations |
| C-21629AA4 | $0.00 | $28,449.24 | -$28,449.24 | Missing CRM company ARR record; active `SUB-0004` | RevOps / CRM Administration |
| C-0F7269D7 | $24,396.00 | $26,796.00 | -$2,400.00 | Other: active `SUB-0006` billing ARR exceeds CRM ARR | RevOps |

Term-policy violations (`term_months` other than 12 with blank `cf_agreement_end_date`):

| Subscription ID | Company alias | Term months | Suggested owner |
|---|---|---:|---|
| SUB-0002 | C-1794A52C | 24 | Billing Operations |
| SUB-0019 | C-22170CA1 | 36 | Billing Operations |
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 $0.0872 · 18s · in 6 / out 2,932 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 | 2026-08 value | 2026-07 value | Absolute change | Relative change | Direction |
|---|---:|---:|---:|---:|---|
| Giving rate | 0.602713 | 0.602297 | +0.000417 | +0.0692% | Up |
| Redemptions per user | 1.730163 | 1.729983 | +0.000180 | +0.0104% | Up |
| 1:1 meetings engagement | 0.447177 | 0.446887 | +0.000290 | +0.0649% | Up |
| Pulse check engagement | 0.508610 | 0.600587 | -0.091977 | -15.3145% | Down |

Arithmetic: absolute change = 2026-08 value − 2026-07 value; relative change = absolute change ÷ 2026-07 value.

Pulse check engagement had the largest relative move: -15.3145%. The data supports enterprise as the driver: enterprise pulse engagement fell from 0.549980 to 0.274280 (-0.275700; -50.1291%). SMB declined -0.2247%, while mid_market increased +0.2108%.
rewards-ytd-sectionPASS 1.00

? The Question

Produce the redemption section of the weekly rewards report through the last completed month (2026-08): redemption count, spend, unique redeemers, redemptions per redeemer, provider mix as percent of spend, top 5 countries by redemptions. Say which month is the last completed month. Provider shares must sum to 100.
cost $0.1502 · 13s · in 6 / out 610 tok
✓ count✓ spend✓ unique_redeemers✓ month_named✓ provider_mix✓ top_country

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
Last completed month: 2026-08

- Redemption count: 378
- Spend: $27,846.00
- Unique redeemers: 235
- Redemptions per redeemer: 378 ÷ 235 = 1.6085 = 1.61

Provider mix by spend:
- custom: $10,873.00 ÷ $27,846.00 = 39.05%
- Tremendous: $8,505.00 ÷ $27,846.00 = 30.54%
- Snappy: $5,238.00 ÷ $27,846.00 = 18.81%
- TangoCard: $3,230.00 ÷ $27,846.00 = 11.60%
- Total: 39.05% + 30.54% + 18.81% + 11.60% = 100.00%

Top 5 countries by redemptions:
1. US: 244
2. CA: 24
3. AU: 21
4. GB: 17
5. NL: 17
customer-success
churn-save-eligibility0.60

? The Question

Which at-risk accounts qualify for a churn-save offer under the documented eligibility rules, what amount is at stake per account and in total, and which play fits each (usage revival, executive touch, commercial concession)? Cite the signal that justifies each play. List accounts that look at risk but do not qualify and why.
cost $0.0772 · 26s · in 6 / out 2,766 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
Qualifying churn-save accounts: all satisfy R1 (health score <60), R2 (eligible amount >$0), and R3 (renewal within 120 days of 2026-09-05).

| Account | Amount at stake | Arithmetic / eligibility | Play | Signal |
|---|---:|---|---|---|
| C-0F6C0F34 | $49,707.00 | Health 51 < 60; $49,707.00 > $0; 2026-10-03 is 28 days away | Executive touch | `champion_active=false` |
| C-0B827671 | $25,365.00 | Health 56 < 60; $25,365.00 > $0; 2026-11-14 is 70 days away | Usage revival | `usage_trend_3m=declining`; 113 / 202 seats used = 55.9% |
| C-0B360C78 | $35,748.00 | Health 57 < 60; $35,748.00 > $0; 2026-10-28 is 53 days away | Commercial concession | `usage_trend_3m=growing`, `champion_active=true`, and 246 / 327 seats used = 75.2%; the account is at risk by health score despite these adoption signals |
| C-0B0F1BAB | $5,494.00 | Health 38 < 60; $5,494.00 > $0; 2026-09-23 is 18 days away | Executive touch | `champion_active=false` |
| C-0CA21961 | $16,829.00 | Health 58 < 60; $16,829.00 > $0; 2026-12-28 is 114 days away | Usage revival | `usage_trend_3m=flat`; 84 / 325 seats used = 25.8% |
| C-0E9C27D1 | $41,235.00 | Health 39 < 60; $41,235.00 > $0; 2026-09-24 is 19 days away | Commercial concession | `usage_trend_3m=flat`, `champion_active=true`, and 134 / 157 seats used = 85.4%; low health remains the at-risk signal |
| C-0D3278C7 | $17,602.00 | Health 54 < 60; $17,602.00 > $0; 2026-11-12 is 68 days away | Usage revival | `usage_trend_3m=declining`; 126 / 380 seats used = 33.2% |

Total amount at stake:

$49,707.00 + $25,365.00 + $35,748.00 + $5,494.00 + $16,829.00 + $41,235.00 + $17,602.00 = **$191,980.00**

At-risk accounts that do not qualify:

| Account | Why it looks at risk | Why it does not qualify |
|---|---|---|
| C-0BA71F12 | Health 52 < 60; `usage_trend_3m=declining`; 23 / 98 seats used = 23.5% | Fails R3: 2027-04-11 is 218 days from the snapshot, exceeding 120 days |
| C-0F6694C3 | Health 43 < 60; `usage_trend_3m=declining`; 39 / 96 seats used = 40.6% | Fails R2: `churn_save_eligible_amount=$0.00`; fails R3: renewal is 197 days away |
| C-0BC71BDD | Health 55 < 60; `usage_trend_3m=flat`; 59 / 197 seats used = 29.9%; `champion_active=false` | Fails R2: `churn_save_eligible_amount=$0.00` |
| C-0BE96399 | Health 54 < 60; `usage_trend_3m=declining`; 43 / 154 seats used = 27.9% | Fails R2: `churn_save_eligible_amount=$0.00` |
| C-0F876796 | Health 47 < 60; `usage_trend_3m=declining`; 22 / 95 seats used = 23.2%; `champion_active=false` | Fails R3: 2027-02-06 is 154 days from the snapshot, exceeding 120 days |
| C-0FCCD2DF | Health 43 < 60; `usage_trend_3m=flat`; 27 / 63 seats used = 42.9%; `champion_active=false` | Fails R2: `churn_save_eligible_amount=$0.00`; fails R3: renewal is 230 days away |
| C-10A56B0F | Health 54 < 60; `usage_trend_3m=declining`; 85 / 176 seats used = 48.3%; `champion_active=false` | Fails R2: `churn_save_eligible_amount=$0.00` |

The eligibility rules document qualification only; they do not document a formal mapping from account signals to play type.
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 $0.0436 · 9s · in 3 / out 585 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
Seat coverage: 150 licensed seats / 400 headcount = 37.5% coverage.

Usage health:
- Monthly active users increased from 88 in 2026-03 to 126 in 2026-08: +38 users (+43.2%).
- Usage grew every month: +7, +7, +8, +8, and +8 monthly active users from 2026-03 through 2026-08.

Headroom:
- Licensed-seat utilization headroom: 150 licensed seats − 126 users in 2026-08 = 24 seats. Current per-seat ARR: $9,000.00 / 150 = $60.00 per seat. ARR headroom within current licenses: 24 × $60.00 = $1,440.00.
- Headcount expansion headroom: 400 headcount − 150 licensed seats = 250 seats. ARR at the current per-seat rate: 250 × $60.00 = $15,000.00.

Reply and buying authority: Maria S., People Operations Coordinator, replied. She explicitly stated she is not the right person for purchasing decisions.

Right buyer: Dana R., VP People. Maria S. stated that budget and seat expansion sit with Dana R.

Reply email (104 words):

Subject: RE: Growing your team's recognition program

Hi Maria,

Thank you for the context—and great to hear the team is getting value from Bonusly. I also noticed monthly active users grew from 88 in March to 126 in August, so it makes sense that Dana has been asking about usage.

If you’re comfortable, I’d appreciate an introduction to Dana R. I can share a concise view of current adoption, the remaining 24 licensed-seat headroom, and how coverage could align with the broader team—without assuming any change is needed.

Would you be open to connecting us? I’m happy to keep the conversation focused on the usage information Dana is looking for.

Best,  
Cole
onboarding-statusPASS 1.00

? The Question

Prep the CSM for the mid-onboarding call: what is complete (integrations connected, allowance set, admins added, first recognition given, first redemption), what is not, early engagement signals, and the three things to cover on the call. Mark nothing complete without a data field that shows it.
cost $0.0490 · 10s · in 6 / out 707 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
C-0D284E42 — mid-onboarding status

Complete
- Slack integration connected: complete. `integration_slack = 2026-08-12`.
- Allowance set: complete. `allowance_set = 2026-08-13`.
- Admins added: complete. `admins_added = 2`.
- First recognition given: complete. `first_recognition_at = 2026-08-15 14:22`.

Not complete / not evidenced
- HRIS integration: not complete based on the provided data. `integration_hris` is blank.
- First redemption: not complete based on the provided data. `first_redemption_at` is blank.

Early engagement signals
- Active givers increased from 3 on 2026-08-11 to 15 on 2026-09-04.
  - Arithmetic: 15 − 3 = 12 additional active givers.
  - Percentage growth: (15 − 3) ÷ 3 × 100 = 400%.
- The peak active-giver count is 15, reached on 2026-09-03 and 2026-09-04.
- Recent engagement is higher than initial engagement:
  - First 7 days average: (3 + 3 + 4 + 4 + 5 + 4 + 7) ÷ 7 = 30 ÷ 7 = 4.29 active givers.
  - Last 7 days average: (11 + 13 + 11 + 13 + 13 + 15 + 15) ÷ 7 = 91 ÷ 7 = 13.00 active givers.
  - Increase: 13.00 − 4.29 = 8.71 average active givers.

Three things to cover on the call
1. HRIS integration: confirm the plan, owner, and timing to connect it; no `integration_hris` date is present.
2. First redemption: identify why no redemption is recorded and agree on the steps to drive the first one; `first_redemption_at` is blank.
3. Engagement momentum: review what is driving growth from 3 to 15 active givers and how the two admins will sustain or expand participation.
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 $0.1025 · 31s · in 6 / out 3,952 tok
✓ total_renewing✓ arr_at_risk✓ disagreements_flagged✓ trust_rule

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
90-day renewal risk brief

Renewal-date reconciliation: Chargebee is used for every account. For multi-year contracts, Chargebee is used because ChurnZero is known to hold incorrect dates; for 12-month contracts, the two systems agree.

| Company | CSM | ARR | Date used | Seat utilization | 3-month usage trend | Risk |
|---|---|---:|---|---|---|---|
| C-0B7D2C30 | Dana Mercer | $65,901 | 2026-09-15 (Chargebee) | 274 / 476 = 57.6% | 97 → 94 → 84 = -13 (-13.4%) | High — Usage fell 13.4% over the last three months and only 57.6% of seats are used. |
| C-0BCDB8C2 | Cole Ingram | $54,427 | 2026-09-18 (both systems agree; Chargebee used) | 232 / 424 = 54.7% | 127 → 118 → 110 = -17 (-13.4%) | High — Usage fell 13.4% over the last three months and seat utilization is 54.7%. |
| C-0D2AB865 | Elena Sinclair | $38,022 | 2026-09-22 (Chargebee) | 250 / 407 = 61.4% | 125 → 117 → 109 = -16 (-12.8%) | High — Usage fell 12.8% over the last three months despite 61.4% seat utilization. |
| C-0BBE3E60 | Dana Mercer | $30,993 | 2026-09-26 (Chargebee) | 74 / 114 = 64.9% | 39 → 35 → 33 = -6 (-15.4%) | High — Usage fell 15.4% over the last three months, the largest percentage decline in the renewal cohort. |
| C-0F5D2323 | Cole Ingram | $90,647 | 2026-09-29 (Chargebee) | 111 / 390 = 28.5% | 20 → 21 → 18 = -2 (-10.0%) | High — Only 28.5% of seats are used and usage declined 10.0% over the last three months. |
| C-0EC6999D | Elena Sinclair | $79,419 | 2026-10-03 (both systems agree; Chargebee used) | 31 / 112 = 27.7% | 17 → 16 → 15 = -2 (-11.8%) | High — Seat utilization is only 27.7% and usage fell 11.8% over the last three months. |
| C-0B20DB64 | Dana Mercer | $21,770 | 2026-10-07 (both systems agree; Chargebee used) | 214 / 378 = 56.6% | 294 → 298 → 294 = 0 (0.0%) | Low — Usage is flat over the last three months, although seat utilization is 56.6%. |
| C-0BBC4E7A | Cole Ingram | $56,374 | 2026-10-10 (both systems agree; Chargebee used) | 228 / 337 = 67.7% | 142 → 141 → 139 = -3 (-2.1%) | Low — Usage is nearly flat, declining only 2.1%, with 67.7% seat utilization. |
| C-0FD551AB | Elena Sinclair | $48,815 | 2026-10-14 (both systems agree; Chargebee used) | 210 / 376 = 55.9% | 123 → 122 → 126 = +3 (+2.4%) | Medium — Usage increased 2.4%, but seat utilization remains 55.9%. |
| C-0F9F8F13 | Dana Mercer | $46,230 | 2026-10-18 (both systems agree; Chargebee used) | 199 / 352 = 56.5% | 185 → 185 → 182 = -3 (-1.6%) | Medium — Usage is essentially flat but seat utilization is 56.5%. |
| C-0BC34584 | Cole Ingram | $16,740 | 2026-10-22 (both systems agree; Chargebee used) | 327 / 494 = 66.2% | 104 → 104 → 106 = +2 (+1.9%) | Low — Usage increased 1.9% over the last three months with 66.2% seat utilization. |
| C-0B7A7546 | Elena Sinclair | $35,062 | 2026-10-25 (both systems agree; Chargebee used) | 182 / 205 = 88.8% | 64 → 65 → 63 = -1 (-1.6%) | Low — Seat utilization is 88.8% and usage is effectively stable. |
| C-0B369871 | Dana Mercer | $85,128 | 2026-10-29 (both systems agree; Chargebee used) | 317 / 422 = 75.1% | 326 → 330 → 333 = +7 (+2.1%) | Low — Usage increased 2.1% over the last three months and seat utilization is 75.1%. |
| C-0B144C78 | Cole Ingram | $30,899 | 2026-11-02 (both systems agree; Chargebee used) | 169 / 224 = 75.4% | 101 → 101 → 106 = +5 (+5.0%) | Low — Usage increased 5.0% over the last three months with 75.4% seat utilization. |
| C-0FC4DBB8 | Elena Sinclair | $94,732 | 2026-11-05 (both systems agree; Chargebee used) | 356 / 464 = 76.7% | 189 → 191 → 193 = +4 (+2.1%) | Low — Usage increased 2.1% over the last three months and seat utilization is 76.7%. |
| C-0D5BBE3A | Dana Mercer | $39,740 | 2026-11-09 (both systems agree; Chargebee used) | 85 / 102 = 83.3% | 88 → 90 → 91 = +3 (+3.4%) | Low — Usage increased 3.4% over the last three months and seat utilization is 83.3%. |
| C-0FB9D5AF | Cole Ingram | $63,158 | 2026-11-13 (both systems agree; Chargebee used) | 144 / 199 = 72.4% | 173 → 173 → 176 = +3 (+1.7%) | Low — Usage increased 1.7% over the last three months with 72.4% seat utilization. |
| C-0B344485 | Elena Sinclair | $64,384 | 2026-11-16 (both systems agree; Chargebee used) | 224 / 287 = 78.0% | 238 → 240 → 244 = +6 (+2.5%) | Low — Usage increased 2.5% over the last three months and seat utilization is 78.0%. |
| C-0CB2C1B4 | Dana Mercer | $40,628 | 2026-11-20 (both systems agree; Chargebee used) | 386 / 473 = 81.6% | 47 → 48 → 49 = +2 (+4.3%) | Low — Usage increased 4.3% over the last three months and seat utilization is 81.6%. |
| C-22170CA1 | Cole Ingram | $45,646 | 2026-11-24 (both systems agree; Chargebee used) | 251 / 294 = 85.4% | 143 → 148 → 146 = +3 (+2.1%) | Low — Usage increased 2.1% over the last three months and seat utilization is 85.4%. |

Date disagreements flagged

- C-0B7D2C30: ChurnZero = 2026-09-10; Chargebee = 2026-09-15. Used Chargebee because this is a 36-month contract and ChurnZero is known to be incorrect for multi-year contracts.
- C-0D2AB865: ChurnZero = 2026-09-10; Chargebee = 2026-09-22. Used Chargebee because this is a 24-month contract and ChurnZero is known to be incorrect for multi-year contracts.
- C-0BBE3E60: ChurnZero = 2027-09-26; Chargebee = 2026-09-26. Used Chargebee because this is a 24-month contract and ChurnZero is known to be incorrect for multi-year contracts.
- C-0F5D2323: ChurnZero = 2026-09-10; Chargebee = 2026-09-29. Used Chargebee because this is a 24-month contract and ChurnZero is known to be incorrect for multi-year contracts.

Total ARR renewing: $1,048,715  
Arithmetic: $359,409 High + $95,045 Medium + $594,261 Low = $1,048,715.

ARR at risk (High): $359,409  
Arithmetic: $65,901 + $54,427 + $38,022 + $30,993 + $90,647 + $79,419 = $359,409.
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 $0.0723 · 17s · in 6 / out 1,777 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
Ranked by ARR exposure (76 tickets total)

1. HRIS provisioning and sync failures — Broad pattern
- Count/share: 11 / 76 = 14.5%
- Distinct accounts: 3
- ARR affected: $36,000 + $30,000 + $48,000 = $114,000
- Tickets: IC-460059, IC-460064
- Recommendation: Prioritize an HRIS sync/provisioning incident review and add reconciliation alerts for missing new-hire account creation.

2. Redemption and gift-card fulfillment failures — Broad pattern
- Count/share: 17 / 76 = 22.4%
- Distinct accounts: 7
- ARR affected: $8,900 + $10,700 + $9,600 + $8,700 + $11,000 + $9,600 + $10,300 = $68,800
- Tickets: IC-460025, IC-460034
- Recommendation: Trace checkout, points deduction, gift-card-code delivery, and email fulfillment as one end-to-end redemption flow.

3. Billing, seat-count, and renewal-tier discrepancies — Single-account noise, high ARR concentration
- Count/share: 16 / 76 = 21.1%
- Distinct accounts: 1
- ARR affected: $52,000
- Tickets: IC-460071, IC-460075
- Recommendation: Assign a named owner to reconcile C-0E9C27D1’s seat counts and renewal-tier pricing, then determine whether the issue is isolated before treating it as a product-wide pattern.

4. Points posting and balance-update failures — Broad pattern
- Count/share: 20 / 76 = 26.3%
- Distinct accounts: 9
- ARR affected: $3,500 + $4,500 + $4,500 + $2,700 + $3,400 + $4,200 + $2,500 + $2,900 + $2,900 = $31,100
- Tickets: IC-460004, IC-460007
- Recommendation: Investigate delayed or failed points-ledger posting, especially after weekends and for recognitions marked delivered.

5. Slack recognition sync, authentication, and slash-command failures — Broad pattern
- Count/share: 12 / 76 = 15.8%
- Distinct accounts: 4
- ARR affected: $4,400 + $5,400 + $3,900 + $5,200 = $18,900
- Tickets: IC-460041, IC-460044
- Recommendation: Audit Slack token persistence, sync-toggle state, and slash-command error handling; add monitoring for disconnects and failed re-authentication.
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 $0.0414 · 6s · in 3 / out 343 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
1. C-64171065 — 3/4 matching fields
   - Industry: Technology = Technology
   - Size band: Mid-Market = Mid-Market
   - Use case: employee_recognition = employee_recognition
   - Region: NA-East ≠ NA-West

2. C-A13C193D — 3/4 matching fields
   - Industry: Technology = Technology
   - Size band: Mid-Market = Mid-Market
   - Use case: retention ≠ employee_recognition
   - Region: NA-West = NA-West

3. C-11C31562 — 3/4 matching fields
   - Industry: Manufacturing ≠ Technology
   - Size band: Mid-Market = Mid-Market
   - Use case: employee_recognition = employee_recognition
   - Region: NA-West = NA-West
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 $0.0752 · 23s · in 6 / out 2,261 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
Trailing 6 months: 2026-03 through 2026-08.

| Paid channel | Spend | SQMs | SQOs | Cost/SQM | Cost/SQO | SQM→SQO rate | Pipeline amount | Pipeline/$ |
|---|---:|---:|---:|---:|---:|---:|---:|---:|
| paid_search | $36,000 | 40 | 18 | $900 | $2,000 | 45.0% | $720,000 | $20.00 |
| linkedin_ads | $24,000 | 25 | 8 | $960 | $3,000 | 32.0% | $96,000 | $4.00 |
| paid_social | $18,000 | 0 | 0 | undefined | undefined | undefined | $0 | undefined |
| webinars | $9,000 | 12 | 5 | $750 | $1,800 | 41.7% | $60,000 | $6.67 |

Arithmetic:
- paid_search: spend = 6 × $6,000 = $36,000; cost/SQM = $36,000 ÷ 40 = $900; cost/SQO = $36,000 ÷ 18 = $2,000; rate = 18 ÷ 40 = 45.0%; pipeline/$ = $720,000 ÷ $36,000 = $20.00.
- linkedin_ads: spend = 6 × $4,000 = $24,000; cost/SQM = $24,000 ÷ 25 = $960; cost/SQO = $24,000 ÷ 8 = $3,000; rate = 8 ÷ 25 = 32.0%; pipeline/$ = $96,000 ÷ $24,000 = $4.00.
- paid_social: spend = 6 × $3,000 = $18,000; zero SQMs means cost/SQM, cost/SQO, conversion rate, and pipeline/$ are undefined.
- webinars: spend = 6 × $1,500 = $9,000; cost/SQM = $9,000 ÷ 12 = $750; cost/SQO = $9,000 ÷ 5 = $1,800; rate = 5 ÷ 12 = 41.7%; pipeline/$ = $60,000 ÷ $9,000 = $6.67.

| Organic channel | Volume | SQOs | SQO rate | Pipeline |
|---|---:|---:|---:|---:|
| organic_search | 30 | 10 | 33.3% | $90,000 |

Arithmetic:
- organic_search: SQO rate = 10 ÷ 30 = 33.3%; pipeline = 10 × $9,000 = $90,000.

SQO-date quality flags:
- CT-000044: SQO date 2026-07-18 precedes SQM date 2026-07-23.
- CT-000041: SQO date 2026-06-09 precedes SQM date 2026-06-14.

Recommendation: Reallocate paid_social’s $18,000 spend first. paid_search is the primary destination: it has the highest recorded SQM-to-SQO rate (45.0%) and pipeline per dollar ($20.00). webinars is the secondary destination if lower cost per SQO is prioritized ($1,800 versus $2,000 for paid_search), but it produces materially less pipeline per dollar ($6.67 versus $20.00).

Confidence: moderate for paid_search (40 SQMs, 18 SQOs); low-to-moderate for webinars (12 SQMs, 5 SQOs) and linkedin_ads (25 SQMs, 8 SQOs). Confidence is reduced for linkedin_ads because 2 of its 8 recorded SQOs have SQO dates before SQM dates. paid_social’s result is definitive only for this six-month sample: $18,000 spend produced zero recorded SQMs.
competitor-battlecard-update0.80

? The Question

Update the battlecard for the competitor. Sections: one-line positioning, pricing with source and date (newer source wins, note the conflict), where they win, where we win, objections and responses, recent changes, our 12-month win/loss record against them. Cite a snippet id for every factual claim. Rep opinion on a call is not a fact about the competitor. Anything from the old card you cannot re-source gets marked unverified.
cost $0.0596 · 16s · in 3 / out 1,721 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
# Rivally Battlecard

## One-line positioning
Rivally is a points-based employee-recognition platform with an engaging recognition feed, Slack integration, and a lightweight engagement-survey add-on. [S02] [S04] [S06]

## Pricing
- Current public price: **Recognition Starter is $7 per user/month; annual billing is required** (pricing page, 2026-08-12). [S17]
- Pricing conflict: Rivally’s earlier public price was **$5 per user/month** with annual billing (pricing page, 2026-01-20; still shown 2026-04-01). The newer $7 public price wins. [S03] [S08] [S17]
- Deal-level quotes differ from public pricing: Rivally quoted **$6.50/user/month** for a 500-seat prospect on an annual term (2026-06-02), and a prospect reported a **$7/user/month list price with a 15% discount for a three-year term** (2026-08-14). [S13] [S18]

## Where they win
- Distributed EU teams: an EU enterprise reviewer praised Rivally’s multi-language support. [S12]
- EU data residency: Rivally announced general availability of EU data residency after opening its Dublin office. [S15]
- Fast setup and Slack integration: a mid-market reviewer reported setup in under a week and that Slack integration worked out of the box. [S04]
- Recognition engagement: reviewers describe Rivally’s recognition feed as points-based and engaging. [S02] [S16]
- Support responsiveness: a reviewer praised support response times of under four hours. [S22]

## Where we win
- Analytics depth: an 800-seat prospect selected Bonusly over Rivally, citing analytics depth. [S25]
- Enterprise administration: an enterprise reviewer reported Rivally lacks SCIM provisioning and that manual user management is painful. [S10]
- Admin tooling: reviewers report that Rivally’s admin tooling lags peers and that its admin console lacks bulk recognition editing. [S16] [S24]
- Reporting and data portability: reviewers describe Rivally reporting dashboards as basic versus enterprise tools and analytics exports as CSV-only. [S07] [S20]
- EMEA rewards catalog: a reviewer reported Rivally’s EMEA catalog is thinner than its US catalog. [S14]

## Objections and responses
- **“Rivally is stronger for EU teams.”**  
  Response: Acknowledge Rivally’s praised multi-language support and generally available EU data residency; then validate whether the buyer also needs enterprise-grade analytics, SCIM provisioning, bulk admin workflows, or a deeper EMEA rewards catalog. [S12] [S15] [S10] [S14] [S24]

- **“Rivally is easier to launch and integrates with Slack.”**  
  Response: Acknowledge the reported under-one-week setup and out-of-the-box Slack integration; then assess ongoing administration, since reviewers cite lagging admin tooling, painful manual user management, and no bulk recognition editing. [S04] [S10] [S16] [S24]

- **“Rivally’s recognition experience is engaging.”**  
  Response: Acknowledge the points-based, engaging recognition feed; differentiate on analytics depth and enterprise reporting requirements. [S02] [S16] [S07] [S25]

- **“Rivally’s price is $5/user/month.”**  
  Response: The newest public pricing source lists $7/user/month with annual billing, superseding the earlier $5 listing. Deal quotes may vary by term and discount. [S03] [S08] [S13] [S17] [S18]

## Recent changes
- 2025-11-04: Rivally announced a $40M Series C led by Northgate Ventures. [S01]
- 2026-03-05: Rivally launched Rivally Pulse, a lightweight engagement-survey add-on. [S06]
- 2026-05-09: Rivally hired an ex-Workday VP EMEA to lead European expansion. [S11]
- 2026-07-01: Rivally opened a Dublin office and announced EU data residency generally available. [S15]
- 2026-08-12: Public Recognition Starter pricing changed to $7/user/month with annual billing required. [S17]
- 2026-08-19: Rivally announced Microsoft Teams app v2 in public preview. [S19]
- 2026-09-01: Rivally Pulse exited beta and is priced as an add-on rather than bundled. [S23]

## 12-month win/loss record against Rivally
Period represented in the deal data: **2025-09 through 2026-08**.

- **Wins (12):** Deal-A9FD43, Deal-7AA785, Deal-44C524, Deal-0D0CD6, Deal-D5B790, Deal-5C636E, Deal-67BE14, Deal-1B6969, Deal-F03E7B, Deal-072E31, Deal-F65C8F, Deal-E46EAB.
- **Losses (6):** Deal-7767F5, Deal-5645A5, Deal-C6FFAA, Deal-D263E0, Deal-935746, Deal-9066A6, Deal-72A02F.

Arithmetic: **12 wins + 7 losses = 19 total deals**.  
Win rate: **12 / 19 = 63.2%**.  
Loss rate: **7 / 19 = 36.8%**.

## Old-card claims requiring correction or verification
- **“Rivally lacks a Slack integration” — unverified and contradicted by a reviewer reporting that Slack integration worked out of the box.** [S04]
- **“Rivally was acquired by WorkHuman in 2025” — unverified; no provided source supports this claim.**
- **“Strong in EU enterprise with multi-language support” — supported only for multi-language support from one EU enterprise reviewer; retain with that qualification.** [S12]
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 $0.1115 · 30s · in 9 / out 2,788 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 review (rates = total metric ÷ total sent):

- New Logo Nurture: sent 1,386; open rate = 490 ÷ 1,386 = 35.35%; reply rate = 90 ÷ 1,386 = 6.49%; meeting rate = 27 ÷ 1,386 = 1.95%. Weakest step: 3 (reply rate = 18 ÷ 428 = 4.21%; meeting rate = 6 ÷ 428 = 1.40%). Change: revise step 3’s CTA to focus on booking a meeting.

- Expansion Nurture: sent 875; open rate = 565 ÷ 875 = 64.57%; reply rate = 59 ÷ 875 = 6.74%; meeting rate = 12 ÷ 875 = 1.37%. Weakest step: 3 (reply rate = 12 ÷ 275 = 4.36%; meeting rate = 3 ÷ 275 = 1.09%). Change: revise step 3’s CTA to focus on booking a meeting.

- Cold Outbound - HR Leaders: sent 1,785; open rate = 545 ÷ 1,785 = 30.53%; reply rate = 8 ÷ 1,785 = 0.45%; meeting rate = 0 ÷ 1,785 = 0.00%. Weakest step: 3 (reply rate = 1 ÷ 590 = 0.17%; meetings = 0). Failure mode: replies are under 2% in every step—step 1 = 0.83%, step 2 = 0.34%, step 3 = 0.17%—so the sequence is failing to generate responses, not merely convert replies to meetings. Change: replace the HR Leaders messaging/offer starting with step 1.

- Cold Outbound - People Ops: sent 1,163; open rate = 340 ÷ 1,163 = 29.23%; reply rate = 29 ÷ 1,163 = 2.49%; meeting rate = 6 ÷ 1,163 = 0.52%. Weakest step: 3 (reply rate = 6 ÷ 377 = 1.59%; meeting rate = 1 ÷ 377 = 0.27%). Failure mode: step 3 reply rate is under 2%, indicating late-sequence response decline. Change: replace step 3.

Tracking error: Expansion Nurture step 2 reports 340 opened ÷ 300 sent = 113.33%; opened exceeds sent.

Audience overlap:
- Expansion Nurture / New Logo Nurture: CT-000301, CT-000624.
- Cold Outbound - HR Leaders / Cold Outbound - People Ops: 19 contacts, including CT-000849, CT-000884, CT-000890, CT-001033, CT-001097, CT-001101, CT-001103, CT-001105, CT-001153, CT-001159, CT-001217, CT-001227, CT-001236, CT-001255, CT-001258, CT-001277, CT-001285, CT-001311, CT-001345.

Fix first: Cold Outbound - HR Leaders, because its aggregate reply rate is 0.45% and it produced zero meetings.
weekly-marketing-goals-rollupPASS 1.00

? The Question

Produce the weekly marketing goals update for the active quarter: SQMs, SQOs, DS2s, closed-lost MIA rate, same-quarter close count, active pipeline coverage against target. For each: QTD actual, target, delta, pace (on/behind/ahead given days elapsed). One paragraph on what moved this week.
cost $0.0523 · 12s · in 6 / out 973 tok
✓ metrics_present✓ deltas✓ coverage_ratio✓ pace✓ paragraph

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
Q3-2026: 66 of 92 days elapsed (71.7%).

| Goal | QTD actual | Target | Delta | Pace |
|---|---:|---:|---:|---|
| SQMs | 230 | 300 | -70 | Ahead: expected pace = 300 × 66/92 = 215.2; actual is 14.8 above pace |
| SQOs | 84 | 120 | -36 | Behind: expected pace = 120 × 66/92 = 86.1; actual is 2.1 below pace |
| DS2s | 40 | 75 | -35 | Behind: expected pace = 75 × 66/92 = 53.8; actual is 13.8 below pace |
| Closed-lost MIA rate | 20.0% (5/25) | 10.0% | +10.0 percentage points | Behind: 20.0% exceeds the lower-is-better target by 10.0 percentage points |
| Same-quarter close count | 10 | 20 | -10 | Behind: expected pace = 20 × 66/92 = 14.3; actual is 4.3 below pace |
| Active pipeline coverage against target | 75.0% ($3,000,000/$4,000,000) | 100.0% ($4,000,000) | -25.0 percentage points / -$1,000,000 | Ahead on elapsed-quarter pace: expected pace = $4,000,000 × 66/92 = $2,869,565; actual is $130,435 above pace |

This week’s movement cannot be determined from the provided data because no prior-week actuals or weekly changes are included. QTD, SQMs and active pipeline are ahead of elapsed-quarter pace; SQOs are slightly behind; DS2s and same-quarter closes are materially behind; and the closed-lost MIA rate is above its lower-is-better target.
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 $0.0397 · 6s · in 3 / out 224 tok
✓ max_3_sentences✓ under_80_words✓ weighted_present✗ caveat_present✓ no_em_dash

✓ Correct Answer (ground truth)

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

Model's actual answer

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Plan Q3 against a $115,976.75 weighted forecast: $44,729 COMMIT + 35% × $203,565 BEST_CASE ($71,247.75); PIPELINE contributes $0. Exclude 32 post-quarter deals totaling $227,575, including COMMIT Deal-D348E1 ($13,770, 2026-10-15). Caveat: all 7 in-quarter COMMIT deals lack a documented why-buy, so the committed forecast is not substantiated.
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 $0.0375 · 4s · in 3 / out 77 tok
✓ word_cap✓ references_touch✓ single_ask

✓ Correct Answer (ground truth)

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

Model's actual answer

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

Hi [Name],

Following up on the July 28 demo with the People team, where we discussed automated milestone awards and the points catalog. I also sent pricing for 150 seats on August 5.

Would you be open to a brief conversation to confirm whether this is still a priority?

Best,  
Alex
funnel-digest-paragraphPASS 1.00

? The Question

Write the weekly GTM digest: one paragraph per team (Marketing, Sales, CS, PLG) in Bonusly brand voice. Every number must trace to the extracts. No customer contact names, no employee performance commentary, no compensation. Four paragraphs, under 400 words total.
cost $0.0411 · 7s · in 3 / out 402 tok
✓ four_paragraphs✓ word_cap✓ teams

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
Marketing: The team generated 46 SQMs against a weekly target of 52, a gap of 52 − 46 = 6 SQMs. The webinar channel contributed 18 SQMs, and paid-search cost per SQM held at $150. Clear signal: recognize the webinar momentum while keeping focus on closing the six-SQM weekly gap.

Sales: The team converted 14 SQOs, set 9 DS2 meetings, and created $310,000 in new pipeline. Same-quarter close count was 3. That is meaningful progress across conversion, meeting creation, and pipeline building—keep the momentum visible and shared.

CS: The team saved 2 renewals, and team NPS moved to 61. There are 3 open red-flag accounts heading into next week. Celebrate the renewals saved while keeping the three open red-flag accounts front and center.

PLG: PLG added 412 new signups with 31% activation. A total of 38 companies reached the aha moment of 10 recognition gives. The next opportunity is to build on signup volume and activation while recognizing the companies reaching that meaningful recognition-giving milestone.
partner-digest-section0.67

? The Question

Write the partner digest for the period. Every partner in the roster appears once: active plays get status with numbers, quiet partners get one line. Partner-sourced pipeline count and amount must match the deal data. Nothing omitted, nothing added.
cost $0.0460 · 8s · in 6 / out 454 tok
✓ all_partners✗ pipeline_amount✓ quiet_one_line

✓ Correct Answer (ground truth)

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

Model's actual answer

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Apex Rewards Co — Active: Co-webinar locked for 09-15. 2 partner-sourced DS1 opps: Deal-DDAAF2 ($180,000) + Deal-2CF33E ($95,000) = $275,000.

HRCloud Partners — Active: Integration referral cleared security review and moved to DS2. 1 partner-sourced opp: Deal-F1CDA5 ($140,000) = $140,000.

CultureBridge — Active: Lunch-and-learn produced 2 early-stage partner-sourced opps: Deal-096E1D ($60,000) + Deal-067213 ($75,000) = $135,000.

WorkWell Group — Active: No partner-sourced deals this period. Joint playbook restart is targeted for Q4; planning call booked for 09-09.

Recogniq — Quiet.

KudosWave — Quiet.

PeopleFirst Advisors — Quiet.

TotalPerk — Quiet.
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 $0.0404 · 5s · in 3 / out 310 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. Customer data is encrypted at rest using AES-256. [P1]

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

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

Q4. Customer data is retained for the contract duration plus 90 days, then deleted from production systems. [P3]

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

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

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

Q8. Unanswerable from the provided excerpts. An ISO 27001 certificate or applicable compliance documentation would answer this question.

Q9. Unanswerable from the provided excerpts. The contractual SLA or service agreement would answer this question.

Q10. Unanswerable from the provided excerpts. The HIPAA BAA policy or a signed Business Associate Agreement template would answer this question.
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 $0.2935 · 72s · in 6 / out 6,570 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
CRITICAL | MERGE  
Finding: `comms-drafter` and `email-drafter` have duplicate ALWAYS-trigger coverage for email drafting and review. Overlapping phrases include: `"write me an email"`, `"draft a follow-up"`, `"what should I say"`, `"bump email"`, and `"contract nudge"`.  
Proposal: Merge `email-drafter` into `comms-drafter`. Keep `comms-drafter` because it covers the broader communications lane; retain `email-drafter`’s Gmail-signature logic in the survivor.

WARNING | UPDATE_BODY  
Finding: `pipeline-intelligence-report` and `weekly-pipeline-report` overlap on pipeline-update requests. `pipeline-intelligence-report` includes `"pipeline update"` and `"what's the pipeline look like"`; `weekly-pipeline-report` includes `"run the pipeline update"`, `"update the pipeline"`, and `"what does pipeline look like"`.  
Proposal: Make the routing boundary explicit: `pipeline-intelligence-report` owns full deal-level scoring/tiering; `weekly-pipeline-report` owns weekly performance metrics, funnel performance, and bookings pacing.

WARNING | UPDATE_BODY  
Finding: `model-selection`’s ALWAYS trigger, `"at the start of every task, without exception"`, overlaps every other ALWAYS-trigger skill. Its “before any … skill invocation” requirement also conflicts with skill-specific trigger routing.  
Proposal: Change `model-selection` from a universal trigger to an orchestration-only preflight invoked after task routing and before execution.

WARNING | UPDATE_BODY  
Finding: 10 unique delegation targets are absent from both the manifest and supplied skill files: `bonusly-brand`, `prospect-research-multithreading`, `bonusly-data-questions`, `bonusly-product-questions`, `bonusly-business-reporting-questions`, `bonusly-rewards-questions`, `bonusly-ppp-questions`, `bonusly-feature-flag-questions`, `bonusly-deal-desk-questions`, and `bonusly-datadog-questions`.  
Proposal: Add manifest rows and skill files for the intended targets, or replace/remove each delegation reference with an existing skill. Do not leave unresolved handoffs.

INFO | REVIEW  
Finding: No circular delegation chain is present in the supplied skills. The apparent relationship `pipeline-intelligence-report` → `closed-lost-analysis` is one-way.  
Proposal: Retain the one-way handoff and re-check after dangling targets are added or rewired.

CRITICAL | UPDATE_BODY  
Finding: `analysis-validator` has an internal version conflict. The body declares `analysis-validator v3.6`, but its Validation Trail template states `Validator: analysis-validator v3.2`.  
Proposal: `v3.6` should survive because it is the declared current version and includes G2-F, which v3.2 predates. Update the trail template to `v3.6`.

CRITICAL | UPDATE_BODY  
Finding: `closed-lost-analysis` queries `t.SNIPPET` from `GONG_TRANSCRIPTS_AGG`, while `analysis-validator` and `stale-pipeline-report` state the approved transcript field is `GONG_TRANSCRIPTS_AGG.TRANSCRIPT`; `stale-pipeline-report` explicitly says no `SNIPPET` field exists.  
Proposal: `analysis-validator` should survive as the final QA authority. Update `closed-lost-analysis` to use `t.TRANSCRIPT`.

WARNING | UPDATE_BODY  
Finding: AE-roster conflict. `analysis-validator` defines the “Core 6 AEs” as Bryce Harmon, Hugo Lindqvist, Dana Mercer, Alex Franklin, Cole Ingram, and Gavin Porter. `pipeline-intelligence-report` hardcodes only five AEs and omits Hugo Lindqvist.  
Proposal: `analysis-validator` should survive because it explicitly defines the full-team rule. Update `pipeline-intelligence-report` to resolve owners dynamically or include all six aliases exactly as defined.

WARNING | REVIEW  
Finding: Hardcoded page/folder identifiers occur in:
- `partner-digest`: `2286616609`, `2286321666`, `2265382925`, `2236940297`, `2237825028`, `2239365136`, `2238283777`
- `sales-forecast`: `2232811524`, `2232582148`
- `signalforge-feedback`: `2295136266`, `2232811524`, `2234417154`

Proposal: Move page and folder IDs to manifest-backed configuration or reference files. Retain only a stable logical destination alias in each body.

WARNING | REVIEW  
Finding: Hardcoded person names occur in:
- `analysis-validator`: `Manish`, `Amani`, `Dana Mercer`, `Gavin Porter`, `Alaina Loori`, `Shealagh Coughlin`, `Bryce Harmon`, `Hugo Lindqvist`, `Alex Franklin`, `Cole Ingram`, `Colleen Perry`, `Ellie Barton`, `Ashley Reyer`, `Ashley Le`, `Megan Franz`, `Elena Sinclair`, `Tracy`, `Youssef Elkhateeb`, `Amanda Czenkus`, `Ben Castelli`, `John Thomas`, `Yasmin Wahid`
- `partner-digest`: `Amani Phipps`, `Kelli`, `Jen Lee`, `Hani`, `Bryce`, `Sara`
- `pipeline-intelligence-report`: `Bryce Harmon`, `Dana Mercer`, `Cole Ingram`, `Alex Franklin`, `Gavin Porter`
- `sales-forecast`: `Alaina`, `Elena`
- `weekly-pipeline-report`: `Ben Lavin`

Proposal: Move roster and ownership details into a single dynamically maintained roster source. Keep only role-based references in skill bodies unless a named escalation owner is required.

WARNING | REVIEW  
Finding: Hardcoded dates occur in:
- `analysis-validator`: April 26, 2026; May 4, 2026; May 9, 2026; March 28, 2023; Q1 2026; Q2 2026; and May 2026 reference ranges.
- `closed-lost-analysis`: May 2026; May 4–12; 4/13; and fixed trailing-period examples.
- `deal-strategy-coach`: April 2026 and 2026 pricing.
- `model-selection`: April 14, 2026; May 19, 2026; and model-registry `last_checked: 2026-05-19`.
- `partner-digest`: May 16, 2026; May 17, 2026; May 19, 2026; June 2, 2026; and Q2/Q3 2026.
- `pipeline-intelligence-report`: March 2023 and May 2026.
- `sales-forecast`: Q2 2026; April 1–June 30, 2026; and dated title examples.
- `signalforge-claim-compressor`: 2026-05-09.
- `stale-pipeline-report`: 2026-06-10 and dated outreach examples.
- `weekly-pipeline-report`: Q1 2026 and Q2 2026.

Proposal: Preserve changelog dates, but replace operational dates, pricing vintages, quarter windows, and “current” examples with dynamic date logic or versioned reference data.

INFO | REVIEW  
Finding: Manifest description-length check: 0 descriptions exceed 1,024 characters.

Arithmetic: 14 manifest rows checked.  
`count(description_chars > 1,024) = 0`.  
Largest listed value: `1,006` for `pipeline-intelligence-report` and `signalforge-claim-compressor`.

Proposal: No trim required.

INFO | REVIEW  
Finding: No manifest drift in either direction.

Arithmetic:
- Manifest rows: 14
- Supplied skill files: 14
- Files with no manifest row: 0
- Manifest rows with no file: 0

Proposal: Retain the current one-to-one manifest/file coverage.
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 $0.0480 · 10s · in 3 / out 902 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
1. Acknowledge incident and take IC
   - Action: Acknowledged PagerDuty alert for reward-worker queue depth > 10k; took IC.
   - Who: Bryce Harmon.
   - Success verification: Not stated; needs confirmation.
   - Rollback: Not applicable; this did not change system state.
   - Trace: [M01]

2. Measure reward queue depth
   - Command: `bundle exec rake sidekiq:queue_depth`
   - Who: Farid Osman.
   - Result / verification: Reward queue was 48,213 pending jobs. Normal is under 500.
   - Rollback: Not applicable; this was a read-only command.
   - Trace: [M02]

3. Inspect dead set
   - Action: Checked the dead set.
   - Who: Farid Osman.
   - Result / verification: Found 112 jobs, all `Redis::TimeoutError` from around 13:58.
   - Rollback: Not applicable; inspection did not change system state.
   - Trace: [M03]

4. Disable automatic recognition enqueue
   - Command: `bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'`
   - Who: Farid Osman.
   - Success verification: Not stated immediately after the command; needs confirmation.
   - Rollback: `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'`
   - Trace: [M04]

5. Clear the dead set
   - Action: Cleared out the dead set while in the console.
   - Who: Elena Sinclair.
   - Success verification: Not stated; needs confirmation.
   - Rollback: Not stated; needs confirmation.
   - Trace: [M05]

6. Scale `deployment/reward-worker` from 3 to 6 replicas
   - Command: `kubectl scale deployment/reward-worker --replicas=6`
   - Who: Bryce Harmon.
   - Success verification: Queue depth later decreased to 9,400 and was falling by approximately 1,200 jobs per minute.
   - Rollback: `kubectl scale deployment/reward-worker --replicas=3`
   - Trace: [M06], [M07]

7. Verify queue recovery
   - Command: `bundle exec rake sidekiq:queue_depth`
   - Who: Cole Ingram.
   - Success verification: Command returned 0; Datadog error rate was back to baseline.
   - Rollback: Not applicable; this was a read-only verification command.
   - Trace: [M08]

8. Re-enable automatic recognition enqueue
   - Command: `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'`
   - Who: Bryce Harmon.
   - Success verification: 40 new jobs processed cleanly in the next 3 minutes.
   - Rollback: The thread does not explicitly state a rollback for this re-enable action. The previously used disable command was `bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'`; using it as rollback needs confirmation.
   - Trace: [M09], [M04]

9. Scale `deployment/reward-worker` from 6 back to 3 replicas
   - Command: `kubectl scale deployment/reward-worker --replicas=3`
   - Who: Bryce Harmon.
   - Success verification: Queue was stable at 0; incident resolved.
   - Rollback: `kubectl scale deployment/reward-worker --replicas=6`
   - Trace: [M10], [M06]
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 $0.0478 · 9s · in 3 / out 671 tok
✓ first_error_ts✓ service_named✓ cascade_ordered✓ query✓ gaps_stated

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
First error:
- 2026-09-03T14:01:12Z — `reward-service`: `Redis::TimeoutError: Connection to Redis at redis-primary:6379 timed out after 5s`

Cascade, in order:
1. 14:01:12Z — `reward-service` times out connecting to `redis-primary:6379`.
2. 14:01:20Z, 14:01:30Z, 14:01:40Z — `reward-service` reports retries exhausted for `RewardGiveJob`.
3. 14:01:40Z onward — `sidekiq` reports `RewardGiveJob` failures and retries; the job table’s earliest recorded failure is `J-00005` at 14:01:46Z.
4. 14:02:30Z — `sidekiq` reports `Queue reward depth above 10,000`.
5. 14:03:05Z onward — `api-gateway` reports 502 upstream timeouts calling `reward-service`.
6. 14:03:30Z onward — `web-app` reports failed Give form submissions due to upstream 502s.
7. 14:22:10Z — `reward-service` reports Redis connection restored and resumes job processing.
8. 14:24:45Z — `sidekiq` reports `Queue reward depth below 500`.

Service and jobs involved:
- Initial failing service: `reward-service`
- Dependency implicated by the error: `redis-primary:6379`
- Queue processor: `sidekiq`
- Primary failed job: `RewardGiveJob`
- Additional affected job: `RecognitionDigestJob` (first recorded failure: 14:02:36Z)

Datadog query to confirm the first error:
```text
service:reward-service status:error "Redis::TimeoutError" "redis-primary:6379"
```
Filter the result timestamp to start at `2026-09-03T14:01:12Z`.

The logs do not show:
- The root cause of Redis unavailability or timeout.
- Redis server health, resource utilization, network status, configuration changes, or Redis-side logs.
- The specific requests, users, or records affected by failed Give submissions.
- Whether any `RewardGiveJob` or `RecognitionDigestJob` ultimately succeeded after retries.
- The total number of failed jobs or submissions; only the listed job failures and a queue-depth threshold are shown.
- A direct causal proof that the Redis issue caused every 502, though the timestamps and messages show a consistent sequence.
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 $0.0473 · 11s · in 6 / out 592 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
- recognition_streaks_v2 — On for `segment:beta_companies` (42 companies). Controls recording a streak when a give occurs via `StreakTracker.record(give)`.

- points_budget_guardrails — On for `all_companies` (220 companies). Controls enforcement of a company’s points budget via `BudgetService.new(company).enforce!(giver, points)`.

- slack_dm_nudges — On for `segment:region_na` (87 companies). Controls whether `SlackDm.send_nudge(user)` runs.

- redeem_flow_redesign — Off for `targeted_list` (12 companies). Controls redemption UI selection: enabled renders `RedeemV2Component`; otherwise it renders `RedeemV1Component`.

- analytics_dashboard_v3 — On for `segment:tier_three` (65 companies). Controls use of `AnalyticsV3.new(company)` for the dashboard.

- ms_teams_app_v2 — Off for `targeted_list` (9 companies). Controls installation of `TeamsAppV2`.

- legacy_give_modal — Off for `segment:legacy_plan` (14 companies). No code reference was provided.

- survey_boosters_q3 — On for `segment:legacy_plan` (7 companies). No code reference was provided.

- paused_offboard_cleanup — Off with no targeting rules and 0 companies. No code reference or targets were provided.

Export company-count arithmetic: `42 + 220 + 87 + 12 + 65 + 9 + 14 + 7 + 0 = 456` flag-company targets. This is a sum of export counts; unique-company count cannot be determined because segment/list overlap is not provided.
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 $0.0411 · 6s · in 3 / out 348 tok
✓ green✓ yellow✓ red✓ non_solicit_caught✓ clause_quoted✓ correct_labels

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
NDA-1 — GREEN: Standard mutual NDA; standard carve-outs including “independently developed” and “must be disclosed by law or court order,” Delaware law, and no restrictive covenants. Term arithmetic: 2-year agreement term + 3-year post-termination confidentiality survival.

NDA-2 — YELLOW: Counsel review required for embedded non-solicit: “During the term of this Agreement and for eighteen (18) months thereafter, neither party shall solicit for employment or hire any employee of the other party with whom it came into contact under this Agreement.” Term arithmetic: 3-year NDA term + 18-month post-term non-solicit = 4 years 6 months of restriction from the Effective Date.

NDA-3 — RED: Full legal review required because it contains a 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.”—has no stated confidentiality carve-outs, is one-way in favor of [PARTY C], and uses unusual governing law/jurisdiction: “the laws of the Republic of Ireland” with “exclusive jurisdiction of its courts.”