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nex-agi

nex-agi/nex-n2-mini

Mean 0.939 · 22/28 perfect tests · $0.06 total · 28/40 tests completed — remaining tests discarded from study (endpoint timeouts after dedicated retries) · modalities in: text · out: text · each card: the prompt → the correct answer (gold) → this model's actual answer

What do the modalities mean?

Modalities in means what you can send this model: text, images, files, video, or audio. Modalities out means what it can send back. Bench tasks feed text extracts, so modalities do not change the score. They are here so you know what this model can handle in real work.

data-crm 3 tests
stage-hygiene-audit0.83
Plain-English question: How much of the open DS2+ pipeline is missing a substantive why-buys, and is anything missing DS1 timestamps or out of order?
Prompt — what the model saw
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 $0.0121 · 9407s · in 414,015 / out 8,917 tok
✓ noncomp_pct✓ no_ds1_0✓ regressed_0✗ per_owner_breakdown✓ definitions_first✓ three_fixes

✓ Correct Answer (ground truth)

Plain-English answer: 81.5% of the 124 DS2+ deals are missing a substantive why-buys — Alex Franklin 48, Cole Ingram 19, Dana Mercer 18, Bryce Harmon 15, Farid Osman 1 — and every deal has a DS1 timestamp with none regressed.
The answer in one line: n_deals = 156; ds2plus = 124; noncomp_pct = 81.5; bryce_noncomp = 15; no_ds1 = 0; regressed = 0 (+1 more below)
n_deals156
bryce_noncomp15
ds2plus124
no_ds10
noncomp_pct81.5
regressed0
per_owner_noncompAlex Franklin=48 of 61 (78.7%); Cole Ingram=19 of 22 (86.4%); Dana Mercer=18 of 22 (81.8%); Bryce Harmon=15 of 18 (83.3%); Farid Osman=1 of 1 (100%)
graded checksnoncomp_pct · no_ds1_0 · regressed_0 · per_owner_breakdown · definitions_first · three_fixes

Model's actual answer

show the model's full answer
Definitions:
- DS2+ = deals in DS2, DS3, DS4, or DS5.
- Non-compliant why-buys = DS2+ deal with missing, placeholder, or <15-character why_buys. LONG(n) is treated as compliant.
- No DS1 timestamp = missing t_ds1.
- Regressed = any earlier-stage timestamp later than a later-stage timestamp.

Numbers:
- Total open deals: 128
- DS2+ deals: 97
- Overall DS2+ why-buys non-compliance: 81 / 97 = 83.5%
- By owner:
  - Bryce Harmon: 15 / 18 = 83.3%
  - Dana Tuly: 18 / 22 = 81.8%
  - Alex Franklin: 28 / 34 = 82.4%
  - Cole Ingram: 19 / 22 = 86.4%
  - Farid Osman: 1 / 1 = 100.0%
- Deals with no DS1 timestamp: 0 / 128 = 0.0%
- Regressed deals: 0 / 128 = 0.0%

Process fixes:
1. Block moving any deal into DS2+ unless why_buys is substantive and non-placeholder.
2. Make t_ds1 mandatory at deal creation and block save/forecasting until populated.
3. Add an owner-level weekly hygiene report for missing why_buys, invalid timestamps, and timestamp regressions.
owner-resolution0.33
Plain-English question: Every open deal has a blank owner name — resolve each one to a real person using the owners table.
Prompt — what the model saw
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 $0.0044 · 61s · in 144,802 / out 5,680 tok
✗ resolved_count✓ unmatched_none✗ deactivated_flagged✓ jake_amount✗ justin_amount✗ no_invented

✓ Correct Answer (ground truth)

Plain-English answer: All 156 deals resolve to six named owners; jake_amount maps to Bryce Harmon ($1,054,144) and justin_amount to Alex Franklin ($624,310).
The answer in one line: jake_amount = 1,054,144; justin_amount = 624,310; resolved = 156
jake_amount1,054,144
justin_amount624,310
resolved156
deactivated
per_ownerBryce Harmon=1,054,144; Alex Franklin=624,310; Dana Mercer=341,195; Cole Ingram=288,161.43; Farid Osman=4,134; Elena Sinclair=2,100
graded checksresolved_count · unmatched_none · deactivated_flagged · jake_amount · justin_amount · no_invented

Model's actual answer

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Deals resolved: 128

Unmatched owner IDs: 0 — none

Owner IDs mapped to archived/deactivated owners: 0 — none

Total pipeline by resolved owner:
119337721 — Bryce Harmon — 35 deals — 1,054,144.00
84342457 — Alex Franklin — 39 deals — 412,930.00
83155923 — Dana Tuly — 24 deals — 341,195.00
83155924 — Cole Ingram — 22 deals — 288,161.43
716654662 — Farid Osman — 7 deals — 4,134.00
701163055 — Elena Sinclair — 1 deal — 2,100.00

Arithmetic:
1,054,144.00 + 412,930.00 + 341,195.00 + 288,161.43 + 4,134.00 + 2,100.00 = 2,102,664.43
snowflake-sql-trapped-catalog0.67
Plain-English question: Write one Snowflake query for first-month usage per customer using only the provided, trap-laden catalog.
Prompt — what the model saw
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 $0.0008 · 30s · in 17,349 / out 3,644 tok
✓ avoids_stale_deals✓ avoids_unpopulated_live✗ uses_cohort_summary✗ succeeded_filter✓ no_giver_deletion_filter✓ returns_sql

✓ Correct Answer (ground truth)

Plain-English answer: A single correct SQL query that uses the right catalog tables and avoids the trap columns — graded on the exact tables and columns.
The answer in one line: use_table = PRODUCTION.PLG.COMPANY_COHORT_SUMMARY
avoid_stalePRODUCTION.HUBSPOT.DEALS (stale)
avoid_unpopulatedV2_LIVE.OBJECTS_DEALS (unpopulated)
forbidden_filterdeleted-giver exclusion must NOT be applied
outputone fenced SELECT with per-FROM/JOIN table-choice comment
required_filtersucceeded redemptions only
use_tablePRODUCTION.PLG.COMPANY_COHORT_SUMMARY
graded checksavoids_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
-- Required data is missing from the provided catalog: there is no listed customer-company key and no listed recognition/giving fact table with GIVER_ID/RECOGNITION_ID columns.
-- Therefore a valid per-company query would require inventing tables or columns. The documented deleted-givers filter is not applied because no historical giving source is available.
-- The listed redemption table is PRODUCTION.DEPRECATED_RECOGNITION.REDEMPTION_RECORDS_V2, but the excerpt provides only STATE and no company_id/GIVER_ID join keys.
-- Avoided stale/unpopulated/retired tables: PRODUCTION.HUBSPOT.DEALS, HUBSPOT_HUB_1973303.V2_LIVE.OBJECTS_DEALS, PRODUCTION.HUBSPOT.GONG_HUBSPOT_MAP_FAST.

SELECT
    CAST(NULL AS VARCHAR) AS customer_company,
    CAST(NULL AS DATE) AS first_calendar_month,
    CAST(NULL AS NUMBER) AS unique_givers,
    CAST(NULL AS NUMBER) AS recognition_count,
    CAST(NULL AS NUMBER) AS successful_redemption_count,
    'Missing required catalog tables/columns for a valid per-company query' AS missing_data_note
WHERE FALSE
```
deal-intelligence 3 tests
call-transcript-extractionPASS 1.00
Plain-English question: Extract the CRM write-back fields from each call transcript as JSON.
Prompt — what the model saw
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 $0.0014 · 2743s · in 37,365 / out 3,847 tok
✓ json_emitted✓ budget_signal✓ null_discipline✓ stakeholders✓ competitor_prospect_only

✓ Correct Answer (ground truth)

Plain-English answer: Per transcript: prospect-only why-buys, pain points, stakeholders, budget and timeline signals, and competitors — e.g. TX-001: '$40k earmarked', live before open enrollment.
The answer in one line: rep = Alex Franklin
allowed_newTX-001, TX-002, TX-003, TX-004, TX-005, TX-006, Achievers, Bucketlist, Nectar
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
repAlex Franklin
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
graded checksjson_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": [
      "Automate anniversary and birthday awards",
      "HR team of three cannot keep up with it manually",
      "Avoid tracking everything in a spreadsheet",
      "Prevent people from slipping through the cracks"
    ],
    "pain_points": [
      "Manual anniversary and birthday award process",
      "HR team of three cannot keep up manually",
      "Spreadsheet-based tracking",
      "People slip through the cracks",
      "Need SSO and audit logs for IT signoff"
    ],
    "stakeholders": [
      "Alex Franklin",
      "Prospect (VP People)",
      "Prospect (HR Admin)"
    ],
    "budget_signal": "$40k earmarked for engagement tools this fiscal year",
    "timeline_signal": "Live before open enrollment in November",
    "competitor_mentioned": "Achievers",
    "next_step": "Security review on September 12",
    "objections": [
      "Manual HR workload",
      "Spreadsheet tracking causes people to slip through the cracks",
      "Need SSO and audit logs for IT signoff",
      "Achievers was too heavy for a team our size"
    ],
    "confidence": "high"
  },
  {
    "transcript_id": "TX-002",
    "deal_alias": "Deal-70BB30",
    "why_buys": [
      "Tie recognition to retention for hourly workforce",
      "Address regretted turnover over 30%",
      "Integrate with Workday"
    ],
    "pain_points": [
      "Hourly workforce regretted turnover over 30%",
      "Need Workday integration to be rock solid"
    ],
    "stakeholders": [
      "Alex Franklin",
      "Prospect (Head of Total Rewards)",
      "Prospect (CFO)"
    ],
    "budget_signal": "$25k pilot budget for this quarter",
    "timeline_signal": "Decision by end of September",
    "competitor_mentioned": null,
    "next_step": "Send pilot agreement; CFO will route it to legal this week",
    "objections": [
      "Workday integration has to be rock solid"
    ],
    "confidence": "high"
  },
  {
    "transcript_id": "TX-003",
    "deal_alias": "Deal-530B50",
    "why_buys": [
      "Make recognition visible across 12 retail locations",
      "Support recognition when store managers have zero budget autonomy for on-the-spot recognition"
    ],
    "pain_points": [
      "Recognition not visible across 12 retail locations",
      "Store managers have zero budget autonomy for on-the-spot recognition today"
    ],
    "stakeholders": [
      "Alex Franklin",
      "Prospect (People Ops Manager)"
    ],
    "budget_signal": null,
    "timeline_signal": "No rush until Q1",
    "competitor_mentioned": "Bucketlist",
    "next_step": "Schedule a call with CEO; People Ops Manager will send two times",
    "objections": [
      "Store managers have zero budget autonomy for on-the-spot recognition today",
      "CEO has to be sold first and decides anything people-related"
    ],
    "confidence": "medium"
  },
  {
    "transcript_id": "TX-004",
    "deal_alias": "Deal-180D02",
    "why_buys": [
      "Consolidate three separate recognition tools into one",
      "Stop paying for three tools that do not talk to the HRIS"
    ],
    "pain_points": [
      "Three separate recognition tools",
      "Paying for three tools",
      "Tools do not talk to HRIS",
      "Procurement cycle runs six to eight weeks minimum",
      "Security review took three months for last vendor"
    ],
    "stakeholders": [
      "Alex Franklin",
      "Prospect (VP People)",
      "Prospect (IT Security Lead)"
    ],
    "budget_signal": "Under $15k annually, VP People can approve without going to the board",
    "timeline_signal": "Procurement cycle runs six to eight weeks minimum",
    "competitor_mentioned": null,
    "next_step": null,
    "objections": [
      "Need to stay under $15k annually to avoid board approval",
      "Procurement cycle runs six to eight weeks minimum",
      "Security review took three months for last vendor",
      "VP People needs to check CFO calendar and made no promise"
    ],
    "confidence": "medium"
  },
  {
    "transcript_id": "TX-005",
    "deal_alias": "Deal-F8767A",
    "why_buys": [
      "Automate service milestones",
      "Provide analytics on recognition equity across departments"
    ],
    "pain_points": [
      "Service milestones need automation",
      "Need analytics on recognition equity across departments",
      "Night-shift teams feel invisible",
      "Night-shift engagement scores run 20 points lower",
      "Exec team is skeptical after a failed rollout two years ago"
    ],
    "stakeholders": [
      "Alex Franklin",
      "Prospect (HR Director)",
      "Prospect (People Ops Coordinator)"
    ],
    "budget_signal": "$12k approved under engagement line",
    "timeline_signal": "Running before January all-hands",
    "competitor_mentioned": "Nectar",
    "next_step": "Present directly to exec team on October 2",
    "objections": [
      "Mid-pilot with Nectar and need to beat that experience",
      "Exec team is skeptical after a failed rollout two years ago"
    ],
    "confidence": "high"
  },
  {
    "transcript_id": "TX-006",
    "deal_alias": "Deal-EE195F",
    "why_buys": [
      "Cut admin time on service awards",
      "Reduce time spent ordering and shipping plaques"
    ],
    "pain_points": [
      "HR Manager spends five hours a month ordering and shipping plaques",
      "Budget is not the issue; time is the issue",
      "COO usually prefers building things in-house",
      "Comparing against doing it internally"
    ],
    "stakeholders": [
      "Alex Franklin",
      "Prospect (HR Manager)"
    ],
    "budget_signal": "Budget isn't the issue — time is",
    "timeline_signal": "Q1 start is realistic because fiscal year resets in February",
    "competitor_mentioned": null,
    "next_step": "Send one-page overview; HR Manager will forward it to COO this week",
    "objections": [
      "Comparing against doing it internally",
      "COO usually prefers building things in-house"
    ],
    "confidence": "medium"
  }
]
next-to-close0.75
Plain-English question: Which 3 deals are closest to signature right now?
Prompt — what the model saw
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 $0.0006 · 12s · in 17,889 / out 1,156 tok
✓ exactly_three✗ top3_named✓ slack_first✓ blocked_excluded

✓ Correct Answer (ground truth)

Plain-English answer: Deal-547B2B ($11,200), Deal-B7EBD1 ($9,000), and Deal-403845 ($9,000) — all DS5 COMMIT deals closing September 10-11, with what is left on each.
blocked_excludeddeal_alias=Deal-2465CE; reason=champion left + procurement freeze (Slack #sales-sparring 2026-09-04)
exactly_three1
ranking_ruleSlack signature-imminent deal ranks first; remaining ranked by stage (DS5>DS4), forecast (COMMIT>BEST_CASE>PIPELINE), close_date asc, amount desc
top3rank=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
graded checksexactly_three · top3_named · slack_first · blocked_excluded

Model's actual answer

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As of the 2026-09-04 Slack thread, the 3 closest to signature are:

1. **Deal-547B2B — $11,200**  
   **Why close:** Slack says redlines came back clean, signing page is out, VP People said they are signing tomorrow. CRM confirms **DS5 / COMMIT**, close date **2026-09-11**.  
   **What’s left:** Signature.

2. **Deal-403845 — $9,000**  
   **Why close:** Slack says it is moving fine and the order form is with finance. CRM confirms **DS5 / COMMIT**, close date **2026-09-11**.  
   **What’s left:** Finance/order-form approval and signature path.

3. **Deal-A2B47C — $6,360**  
   **Why close:** Slack says it is still warm with normal legal-review pace. CRM confirms **DS5 / COMMIT**, close date **2026-09-11**.  
   **What’s left:** Legal review completion.

Arithmetic/selection: I treated the 2026-09-04 Slack status as overriding CRM when it conflicts. **Deal-2465CE** is not included despite CRM showing DS5/COMMIT because Slack says it is blocked by champion departure and procurement freeze, likely Q4.
product-gap-detectionPASS 1.00
Plain-English question: Which prospect complaints are true product gaps versus rollout or enablement issues?
Prompt — what the model saw
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 $0.0005 · 8s · in 17,142 / out 1,134 tok
✓ real_gaps✓ plan_gate_classified✓ enablement_classified✓ gap_summary

✓ Correct Answer (ground truth)

Plain-English answer: Two real gaps (SCIM provisioning, ADP Workforce Now integration) separated from plan gates and enablement issues, each with the prospect quote and deal.
allowed_newTG-001, TG-002, TG-003, TG-004, TG-005, SCIM user provisioning, ADP Workforce Now integration, Custom report builder
enablement_issuestranscript_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_voicetranscript_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
plan_gatestranscript_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?
real_gapstranscript_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
graded checksreal_gaps · plan_gate_classified · enablement_classified · gap_summary

Model's actual answer

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Amount is not provided in the supplied data, so I cannot name deal amounts.

| Prospect quote | Classification | Product docs basis | Deal | Amount |
|---|---|---|---|---|
| “We need SCIM user provisioning — our security team will not approve any tool that requires manual account management.” | Real product gap | SCIM user provisioning is “NOT currently listed as supported capabilities.” | Deal-EC3025 | Not provided |
| “Our payroll runs on ADP Workforce Now and I don't see ADP anywhere in your integration list — that's a dealbreaker for us.” | Real product gap | ADP Workforce Now integrations are “NOT currently listed as supported capabilities.” | Deal-D0D6B5 | Not provided |
| “I tried to build a custom report combining recognition frequency with turnover and couldn't — is that possible on our current plan?” | Plan gate | Custom report builder is available on Enterprise. Current plan is not provided, but the capability exists on a higher tier. | Deal-CFE7F4 | Not provided |
| “We turned on the Slack integration in July, but half our managers still haven't given a single recognition — nobody ever trained them on it.” | Rollout/enablement issue | Slack integration is supported on all plans; the issue is lack of manager training, not a missing Slack capability. | Deal-84DBA6 | Not provided |

Summary — real gaps only:

1. Deal-EC3025: SCIM user provisioning is a real product gap. Prospect quote: “We need SCIM user provisioning — our security team will not approve any tool that requires manual account management.” Amount: Not provided.
2. Deal-D0D6B5: ADP Workforce Now is a real product gap. Prospect quote: “Our payroll runs on ADP Workforce Now and I don't see ADP anywhere in your integration list — that's a dealbreaker for us.” Amount: Not provided.

Excluded: Deal-36C33F, because the mobile app statement came from Alex, not prospect voice, and rep language does not count as prospect voice.
rep-performance 2 tests
ae-qtd-scorecardPASS 1.00
Plain-English question: Build Alex Franklin's QTD scorecard: bookings versus quota, pipeline, and conversion.
Prompt — what the model saw
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 $0.0075 · 3730s · in 196,474 / out 23,536 tok
✓ bookings✓ quota✓ attainment✓ new_expansion_split✓ top_loss_reason✓ coaching_obs

✓ Correct Answer (ground truth)

Plain-English answer: 75% attainment ($150,000 on a $200,000 quota), $113,500 new versus $36,500 expansion, pipeline by stage, and the rolling DS2-to-won rate.
The answer in one line: quota = 200000; bookings = 150000; new_bookings = 113500; expansion_bookings = 36500; open_deals_count = 125; attainment_pct = 75; owner = Alex Franklin (+12 more below)
quota200,000
attainment_pct75
bookings150,000
expansion_bookings36,500
expansion_count3
losses_qtd27
new_bookings113,500
new_count5
open_deals_count125
wins_qtd8
activity_30d_totalsemails_30d=807; calls_30d=112; meetings_30d=128; notes_30d=50
allowed_new18 items — e.g. Deal-A1C3E5, Deal-B7D2F4, Deal-C9E1A6, Deal-D4B8C2
ds2_to_wonwindow_start=2026-06-07; won_in_window=8; closed_in_window=35; rate=0.2286
ownerAlex Franklin
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}}
prior_quarter_excludeddeal_alias=Deal-B3E6F1; close_date=2026-06-20; amount=24000
quarter2026-Q3
snapshot_date2026-09-05
top_loss_reasonreason=Lost- Timing (1 year or more); count=13
graded checksbookings · quota · attainment · new_expansion_split · top_loss_reason · coaching_obs

Model's actual answer

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Alex Franklin — QTD scorecard as of 2026-09-05  
QTD = 2026-Q3 through 2026-09-05. No company names were provided; cited aliases are deal aliases only.

Bookings vs quota:
- QTD bookings: $150,000 = $40,000 + $20,000 + $35,000 + $21,000 + $12,000 + $11,000 + $6,500 + $4,500
- Quota: $200,000
- Attainment: 75.0% = $150,000 / $200,000
- QTD closed-won deals: Deal-A1C3E5, Deal-F2C7D8, Deal-B7D2F4, Deal-C9E1A6, Deal-A8B4D6, Deal-D4B8C2, Deal-E6F3A9, Deal-C5D9E2
- Excluded pre-quarter closed-won: Deal-B3E6F1

New vs expansion split:
- New: $113,500 = $40,000 + $35,000 + $21,000 + $11,000 + $6,500; 5 wins; 75.7% of QTD bookings = $113,500 / $150,000
- Expansion: $36,500 = $20,000 + $12,000 + $4,500; 3 wins; 24.3% of QTD bookings = $36,500 / $150,000

Active pipeline by stage:
- DS1: 20 deals, $284,621
- DS2: 28 deals, $353,760
- DS3: 67 deals, $552,705
- DS4: 5 deals, $23,574
- DS5: 5 deals, $45,730
- Total active open pipeline: $1,260,390 = $284,621 + $353,760 + $552,705 + $23,574 + $45,730

Rolling 90-day DS2-to-won rate:
- Window: 2026-06-07 through 2026-09-05
- Closed DS2 outcomes: 8 won + 27 lost = 35
- DS2-to-won rate: 22.9% = 8 / 35
- Open DS2 deals excluded from this rate because they have no win/loss outcome.

Win/loss counts and top loss reason:
- Wins: 8
- Losses: 27
- Top loss reason: Lost- Timing (1 year or more)
- Arithmetic: 13 losses / 27 losses = 48.1% of loss count
- Lost amount: $184,681 / $329,272 lost amount = 56.1%
- Loss aliases for top reason: Deal-55867E, Deal-D1A623, Deal-69CF3D, Deal-ECBF89, Deal-15DA99, Deal-79B7A1, Deal-F4AF5D, Deal-B038F0, Deal-E6E80A, Deal-B3ABED, Deal-39E25C, Deal-29326C, Deal-831B7B

Activity volume by type in last 30 days:
- Emails: 807
- Calls: 112
- Meetings: 128
- Notes: 50

Coaching observations:
1. Need $50,000 more to hit quota: $200,000 - $150,000 = $50,000.
2. New business is the main QTD driver at $113,500 / $150,000 = 75.7%; expansion is only $36,500 / $150,000 = 24.3%.
3. Timing is the largest loss pattern: 13 of 27 losses and $184,681 of lost amount are tied to Lost- Timing (1 year or more).
talk-track-from-transcriptsPASS 1.00
Plain-English question: From this rep's last 10 calls: how do they open, handle objections, and how often do they lock a next step?
Prompt — what the model saw
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 $0.0023 · 1610s · in 44,518 / out 11,078 tok
✓ objection_count✓ next_step_rate✓ competitors✓ coaching✓ quote_used

✓ Correct Answer (ground truth)

Plain-English answer: 8 of 10 calls open with the same customer-win story; objection handling, next-step agreement rate, and every competitor raised, with quotes.
The answer in one line: n_transcripts = 10; objection_count = 3; rep = Alex Franklin (+3 more below)
n_transcripts10
objection_count3
allowed_new13 items — e.g. TT-001, TT-002, TT-003, TT-004
competitor_quotesAwardco=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.
competitors_prospect_raisedAwardco, Kudos
next_stepagreed=7; total=10; rate=0.7
objectionsobjection=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
opener_themetheme=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
repAlex Franklin
rep_raised_competitors_excludedWorkhuman
graded checksobjection_count · next_step_rate · competitors · coaching · quote_used

Model's actual answer

show the model's full answer
Last 10 calls:

First five minutes lead:
- Lead with customer ROI case study: 8/10 calls. Arithmetic: 8 case-study opens + 1 security/pricing opener + 1 pricing opener = 10/10.
- Quote: Deal-D348E1: “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.”

Three most common objections:
1. Budget locked: 4/10 calls — Deal-D348E1, Deal-547B2B, Deal-60C2C2, Deal-84DBA6.
   - Handle: acknowledge, then reframe around turnover savings.
   - Quote: Deal-D348E1: “Totally fair. Most teams fund this out of turnover savings — that retailer saved about $210k in avoided backfills, which is how their finance team signed off.”

2. Revisit next quarter/open enrollment timing: 3/10 calls — Deal-5408B0, Deal-C61CF7, Deal-D9A12F.
   - Handle: acknowledge timing, then propose a 90-day pilot with one department.
   - Quote: Deal-5408B0: “Makes sense. What if we scope a 90-day pilot with one department so you have internal data before next quarter's planning?”

3. Spreadsheet + quarterly gift cards: 3/10 calls — Deal-403845, Deal-EDC141, Deal-1E2498.
   - Handle: acknowledge spreadsheets work initially, then contrast with automation and analytics.
   - Quote: Deal-403845: “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 agreed to a concrete next step = 70%.
- Arithmetic: yes in Deal-D348E1, Deal-5408B0, Deal-547B2B, Deal-C61CF7, Deal-60C2C2, Deal-D9A12F, Deal-1E2498 = 7; no/missing in Deal-403845, Deal-EDC141, Deal-84DBA6 = 3; 7/10 = 70%.
- Quote: Deal-D348E1: “Yes, Thursday at 2pm works — send the invite and I'll bring our HRIS manager.”

Competitors a prospect raised:
- Awardco: 1/10 calls — Deal-547B2B. Quote: “We're also in late talks with Awardco — their rewards catalog looks bigger than yours.”
- Kudos: 1/10 calls — Deal-EDC141. Quote: “How are you different from Kudos? Our CEO used them at her last company.”
- Workhuman was not counted as prospect-raised because it was rep-initiated in Deal-C61CF7.

Coaching note 1: The 400-person retailer case study is working as the default opener, but use it selectively on pricing/security calls so the agenda still feels prospect-led.

Coaching note 2: After budget, timing, committee, or no-urgency objections, push harder for a specific next step; 3/10 calls ended without one.
reporting-analytics 3 tests
q3-weighted-forecast0.90
Plain-English question: Build the Q3 2026 weighted forecast from the open pipeline, and show the math deal by deal.
Prompt — what the model saw
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 $0.0017 · 2261s · in 47,388 / out 5,217 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)

Plain-English answer: A stage-weighted Q3 forecast number computed from the open pipeline, with per-stage math that reconciles to the deal data.
The answer in one line: excluded_amount = 227575; best_case_total = 203565; weighted_forecast = 115,976.75; commit_total = 44729; excluded_out_of_quarter = 32; best_case_count = 24; flags = blank owner column + empty why_buys fields must be surfaced (+2 more below)
excluded_amount227,575
best_case_count24
best_case_total203,565
commit_count7
commit_total44,729
excluded_out_of_quarter32
weighted_forecast115,976.75
flagsblank owner column + empty why_buys fields must be surfaced
top5_best_caseDeal-2D7423, Deal-25F752, Deal-E53952, Deal-5EED42, Deal-FA32A0
graded checkscommit_total_44729 · best_case_total_203565 · weighted_115976.75 · commit_count_7 · best_case_count_24 · excluded_count_32 · excluded_amount_227575 · top5_aliases · flags_blank_owner · flags_missing_why_buys

Model's actual answer

show the model's full answer
Q3 2026 forecast: 2026-07-01 to 2026-09-30

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

- BEST_CASE total: $203,565
  - Count: 24 deals

- Weighted forecast: $115,976.75
  - Arithmetic: $44,729 + 35% × $203,565
  - $203,565 × 0.35 = $71,247.75
  - $44,729 + $71,247.75 = $115,976.75

- Excluded outside Q3: 32 deals, $227,575
  - PIPELINE: 22 deals, $185,565
  - BEST_CASE: 9 deals, $28,240
  - COMMIT: 1 deal, $13,770

Top 5 BEST_CASE deals inside Q3 by amount:

1. Deal-2D7423 — $38,935 — close 2026-09-30
2. Deal-25F752 — $24,000 — close 2026-09-25
3. Deal-E53952 — $19,656 — close 2026-09-30
4. Deal-5EED42 — $16,250 — close 2026-09-30
5. Deal-FA32A0 — $11,116 — close 2026-09-25

## Data quality

The extract contains multiple outside-quarter dates, including 2026-10-01 through 2026-10-15 and dates before 2026-07-01, so date filtering must be enforced carefully. Many owner fields are blank, which limits accountability and deal ownership analysis. Several deals have nonzero `why_buys_chars` values even though no supporting reason text is provided in the extract, making the rationale unusable without additional data. The forecast is therefore unreliable if run unattended without validation for close-date boundaries, owner completeness, and missing reason content.
kvm-month-over-monthPASS 1.00
Plain-English question: How did the four core KVMs move in August versus July?
Prompt — what the model saw
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 $0.0013 · 757s · in 42,575 / out 1,607 tok
✓ four_kvms✓ biggest_mover_named✓ direction_correct✓ segment_named✓ word_cap

✓ Correct Answer (ground truth)

Plain-English answer: Giving rate 60.27% (up 0.07%), redemptions per user 1.7302 (up 0.01%), with 1:1 meetings and pulse engagement each up fractionally — direction reported for all four.
The answer in one line: n_company_rows_per_month = 30; aggregation = simple mean of company-level values per month
n_company_rows_per_month30
aggregationsimple mean of company-level values per month
biggest_moverpulse_engagement
driving_segmentsize_band=enterprise; 2026-07=0.55; 2026-08=0.2743; rel_change_pct=-50.13
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
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,
graded checksfour_kvms · biggest_mover_named · direction_correct · segment_named · word_cap

Model's actual answer

show the model's full answer
Largest relative move: pulse engagement, driven by enterprise size_band. Data supports segment attribution only by size_band; plan_tier is only tier_three, so it does not differentiate the driver.

| KVM | 2026-08 | 2026-07 | Absolute change | Relative change | Direction |
|---|---:|---:|---:|---:|---|
| Giving rate | 0.6027 | 0.6023 | 0.0004 | +0.069% | Up |
| Redemptions per user | 1.7302 | 1.7300 | 0.0002 | +0.010% | Up |
| 1:1 meetings engagement | 0.4472 | 0.4469 | 0.0003 | +0.065% | Up |
| Pulse check engagement | 0.5086 | 0.6006 | -0.0920 | -15.314% | Down |

Arithmetic used: average across 30 company records per month. Example: pulse engagement = 0.508610 - 0.600587 = -0.091977; relative = -0.091977 / 0.600587 = -15.314%.
rewards-ytd-sectionPASS 1.00
Plain-English question: Produce the redemption section of the weekly rewards report through August.
Prompt — what the model saw
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 $0.0024 · 32s · in 76,340 / out 4,507 tok
✓ count✓ spend✓ unique_redeemers✓ month_named✓ provider_mix✓ top_country

✓ Correct Answer (ground truth)

Plain-English answer: 378 redemptions, $27,846 spend, 235 unique redeemers at 1.61 each, custom rewards at 39% of spend, US leading all countries.
The answer in one line: redemption_count = 378; unique_redeemers = 235; redemptions_per_redeemer = 1.61; total_spend_usd = 27846.00
redemption_count378
redemptions_per_redeemer1.61
unique_redeemers235
provider_mix_pct_of_spendTangoCard=11.6; Tremendous=30.54; custom=39.05; Snappy=18.81
top5_countries_by_countcountry=US; count=244 | country=CA; count=24 | country=AU; count=21 | country=GB; count=17 | country=NL; count=17
total_spend_usd27846.00
graded checkscount · 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 section through 2026-08:
- Redemption count: 378
- Spend: $27,846.00
- Unique redeemers: 236 distinct user_key values
- Redemptions per redeemer: 378 / 236 = 1.60

Provider mix as percent of spend:
- custom: $10,873.00 / $27,846.00 * 100 = 39.05%
- Tremendous: $8,505.00 / $27,846.00 * 100 = 30.54%
- Snappy: $5,238.00 / $27,846.00 * 100 = 18.81%
- TangoCard: $3,230.00 / $27,846.00 * 100 = 11.60%

Provider share sum: 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, tied for 5th

No deal aliases are present in the provided data; no deal/company aliases were needed for these aggregate metrics.
customer-success 5 tests
churn-save-eligibilityPASS 1.00
Plain-English question: Which at-risk accounts actually qualify for a churn-save offer, and which play fits each?
Prompt — what the model saw
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 $0.0021 · 3844s · in 58,392 / out 5,598 tok
✓ eligible_set✓ total_at_stake✓ plays_cited✓ noneligible_named✓ rules_applied

✓ Correct Answer (ground truth)

Plain-English answer: Only accounts meeting all three rules qualify — e.g. C-0F6C0F34 at $49,707 (executive touch) and C-0B827671 at $25,365 (usage revival) — with amount and play per account.
The answer in one line: n_accounts = 30; snapshot_date = 2026-09-05
n_accounts30
eligibleaccount_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)
non_eligible_at_riskaccount_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)
ruleshealth_score < 60, churn_save_eligible_amount > 0, renewal within 120 days of snapshot
snapshot_date2026-09-05
total_amount_at_stake224601.00
graded checkseligible_set · total_at_stake · plays_cited · noneligible_named · rules_applied

Model's actual answer

show the model's full answer
Eligibility rules applied:
- R1: health_score < 60
- R2: churn_save_eligible_amount > 0
- R3: renewal within 120 days of snapshot 2026-09-05

At-risk accounts that qualify: 8  
Total amount at stake: $224,601.00

| account_alias | amount at stake | renewal | days from 2026-09-05 | play | signal justifying play |
|---|---:|---|---:|---|---|
| C-0E9C27D1 | $41,235.00 | 2026-09-24 | 19 | executive touch | health score 39, eligible amount $41,235.00, renewal in 19 days |
| C-0F6C0F34 | $49,707.00 | 2026-10-03 | 28 | executive touch | health score 51, eligible amount $49,707.00, renewal in 28 days |
| C-0B360C78 | $35,748.00 | 2026-10-28 | 53 | commercial concession | health score 57, eligible amount $35,748.00, renewal in 53 days |
| C-0B827671 | $25,365.00 | 2026-11-14 | 70 | usage revival | health score 56, eligible amount $25,365.00, usage trend declining |
| C-0CEF69FD | $32,621.00 | 2026-11-21 | 77 | executive touch | health score 53, eligible amount $32,621.00, renewal in 77 days |
| C-0D3278C7 | $17,602.00 | 2026-11-12 | 68 | usage revival | health score 54, eligible amount $17,602.00, usage trend declining |
| C-0B0F1BAB | $5,494.00 | 2026-09-23 | 18 | commercial concession | health score 38, eligible amount $5,494.00, renewal in 18 days |
| C-0CA21961 | $16,829.00 | 2026-12-28 | 114 | usage revival | health score 58, eligible amount $16,829.00, usage trend declining |

Arithmetic:
$41,235.00 + $49,707.00 + $35,748.00 + $25,365.00 + $32,621.00 + $17,602.00 + $5,494.00 + $16,829.00 = $224,601.00

At-risk accounts that do not qualify:

| account_alias | reason |
|---|---|
| C-0BC71BDD | health score 55 is below 60, but churn_save_eligible_amount is $0.00 |
| C-0BA71F12 | health score 52 is below 60 and eligible amount is $6,824.00, but renewal is 218 days from snapshot, outside the 120-day rule |
| C-0F6694C3 | health score 43 is below 60, but churn_save_eligible_amount is $0.00 and renewal is 197 days from snapshot |
| C-0BE96399 | health score 54 is below 60, but churn_save_eligible_amount is $0.00 |
| C-0F876796 | health score 47 is below 60 and eligible amount is $19,958.00, but renewal is 154 days from snapshot, outside the 120-day rule |
| C-0FCCD2DF | health score 43 is below 60, but churn_save_eligible_amount is $0.00 and renewal is 230 days from snapshot |
| C-10A56B0F | health score 54 is below 60, but churn_save_eligible_amount is $0.00 |
expansion-kitPASS 1.00
Plain-English question: Build the expansion kit for the customer who just replied to our outreach.
Prompt — what the model saw
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 $0.0006 · 722s · in 16,801 / out 1,142 tok
✓ coverage_pct✓ headroom_arr✓ right_buyer✓ replier_not_buyer✓ usage_fact✓ email_under_150

✓ Correct Answer (ground truth)

Plain-English answer: 37.5% seat coverage, 250 headroom seats worth $15,000 ARR at $60 a seat, usage up 43.2% in six months — and the replier cannot buy; Dana R. (VP People) is the right buyer.
The answer in one line: headroom_seats = 250; coverage_pct = 37.5; company_alias = C-0DDFC9A7 (+3 more below)
headroom_seats250
coverage_pct37.5
company_aliasC-0DDFC9A7
headroom_arr15000.00
per_seat_annual_rate60.00
replierMaria S.
replier_can_buy0
right_buyername=Dana R.; title=VP People
usage_factsactive_users_2026_08=126; active_users_2026_03=88; growth_pct_6m=43.2; licensed_seat_utilization_pct=84
usage_health_linesMonthly 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 checkscoverage_pct · headroom_arr · right_buyer · replier_not_buyer · usage_fact · email_under_150

Model's actual answer

show the model's full answer
Company alias: C-0DDFC9A7

Seat coverage:
- Licensed seats: 150
- Headcount: 400
- Coverage: 150 / 400 = 37.5%
- Seat headroom: 400 - 150 = 250 seats

Usage health:
- MAUs grew from 88 in 2026-03 to 126 in 2026-08: +38 MAUs, +43.2%.
- Current MAUs are 126 / 150 licensed seats = 84.0% of licensed capacity.

Expansion headroom at current per-seat rate:
- Current per-seat ARR: $9,000 / 150 = $60 per seat per year
- Headroom ARR: 250 seats × $60 = $15,000 ARR

Who replied:
- Maria S., People Operations Coordinator
- Can they buy: No. Maria explicitly said purchasing decisions, budget, and seat expansion sit with Dana R.

Right buyer:
- Dana R., VP People, last engaged 2026-05-18

Reply email under 150 words:

Subject: Re: Growing your team’s recognition program

Hi Dana,

Maria S. reached out on C-0DDFC9A7’s behalf and suggested I connect with you. She mentioned the recognition feed is busy every week, and the data shows MAUs grew from 88 in March to 126 in August.

At the current coverage, the company has 150 licensed seats against 400 headcount, leaving 250 seats of potential expansion headroom. If seat expansion is on your radar, I’d be happy to walk through the usage picture and any options.

No pressure either way — happy to point you in the right direction.

Best,  
Cole
onboarding-statusPASS 1.00
Plain-English question: What is done and what is missing in this customer's onboarding before the mid-onboarding call?
Prompt — what the model saw
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 $0.0019 · 12s · in 68,523 / out 717 tok
✓ complete_items✓ incomplete_items✓ three_items✓ grounded

✓ Correct Answer (ground truth)

Plain-English answer: 25 days live: Slack connected, allowance set, admins added — but the HRIS integration is not connected, with early engagement signals and three call priorities.
The answer in one line: days_live = 25; company_alias = C-0D284E42
days_live25
call_itemsConnect 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.
checklist{"slack_integration_connected": {"complete": true, "field": "integration_slack", "value": "2026-08-12"}, "hris_integration_connected": {"complete": false, "field": "integration_hris", "value": ""}, "a
company_aliasC-0D284E42
engagement_signalsavg_active_givers_week1=4.3; avg_active_givers_last_week=13; latest_active_givers=15; days_with_activity=25
graded checkscomplete_items · incomplete_items · three_items · grounded

Model's actual answer

show the model's full answer
CSM mid-onboarding call prep for C-0D284E42

Complete
- Slack integration connected: 2026-08-12
- HRIS integration connected: Not complete / missing data field
- Allowance set: 2026-08-13
- Admins added: Yes, 2 admins
- First recognition given: 2026-08-15 14:22
- First redemption: Not complete / missing data field

Not complete
- HRIS integration: no HRIS timestamp or status field provided
- First redemption: no first_redemption_at value provided

Early engagement signals
- Active givers grew from 3 on 2026-08-11 to 15 on 2026-09-04.
- Net increase: 15 - 3 = 12 active givers.
- Percent increase: 12 / 3 = 4.0x, or +400%.
- Highest active givers in provided usage data: 15, on 2026-09-03 and 2026-09-04.
- First 5 active givers reached: 2026-08-15, same day as first recognition.
- Usage data covers 25 dates: 2026-08-11 through 2026-09-04 inclusive.

Three things to cover on the call
1. Confirm HRIS setup status and any blocker, since HRIS integration data is missing.
2. Reinforce adoption momentum: active givers rose from 3 to 15, a +400% increase, with first redemption still pending.
3. Review the first recognition and drive first redemption: first recognition occurred on 2026-08-15 14:22, but no first_redemption_at was provided.
renewal-risk-conflicting-datesPASS 1.00
Plain-English question: Two systems disagree on renewal dates — which do you trust per account, and who is actually renewing inside 90 days?
Prompt — what the model saw
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 $0.0039 · 1637s · in 87,561 / out 15,936 tok
✓ total_renewing✓ arr_at_risk✓ disagreements_flagged✓ trust_rule

✓ Correct Answer (ground truth)

Plain-English answer: Chargebee wins per account (multi-year contracts are known wrong in ChurnZero), with each trusted date, the reason, and a 90-day-window flag.
The answer in one line: n_accounts = 20; n_disagreements = 5; snapshot_date = 2026-09-05 (+2 more below)
n_accounts20
n_disagreements5
accounts20 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; 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; 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%
arr_at_risk_90d359409.00
disagreementsaccount_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
snapshot_date2026-09-05
total_arr_renewing_90d1048715.00
trust_rulemulti-year contracts: Chargebee is authoritative (ChurnZero known wrong); otherwise systems agree or Chargebee wins
window2026-09-05 to 2026-12-04
graded checkstotal_renewing · arr_at_risk · disagreements_flagged · trust_rule

Model's actual answer

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

Assumption used: for multi-year contracts, trust Chargebee renewal date because multi-year contracts are known to be wrong in ChurnZero. For 12-month contracts, trust ChurnZero/Chargebee when dates agree; no separate date discrepancy exists for non-multi-year accounts.

| Company alias | CSM | ARR | Renewal date used | Seat utilization | 3-month usage trend | Risk | Evidence |
|---|---:|---:|---|---|---|---|---|
| C-0B7D2C30 | Dana Mercer | $65,901.00 | 2026-09-15 | 274/476 = 57.6% | 97 -> 94 -> 84 = -13 (-13.4%) | High | 274/476 = 57.6% utilization is below 60%. |
| C-0BCDB8C2 | Cole Ingram | $54,427.00 | 2026-09-18 | 232/424 = 54.7% | 127 -> 118 -> 110 = -17 (-13.4%) | High | 232/424 = 54.7% utilization is below 60%. |
| C-0D2AB865 | Elena Sinclair | $38,022.00 | 2026-09-22 | 250/407 = 61.4% | 125 -> 117 -> 109 = -16 (-12.8%) | Medium | 250/407 = 61.4% utilization and 125 -> 109 = -16 (-12.8%) trend are both medium-risk indicators. |
| C-0BBE3E60 | Dana Mercer | $30,993.00 | 2026-09-26 | 74/114 = 64.9% | 39 -> 35 -> 33 = -6 (-15.4%) | Medium | 74/114 = 64.9% utilization and 39 -> 33 = -6 (-15.4%) trend are both medium-risk indicators. |
| C-0F5D2323 | Cole Ingram | $90,647.00 | 2026-09-29 | 111/390 = 28.5% | 20 -> 21 -> 18 = -2 (-10.0%) | High | 111/390 = 28.5% utilization is below 60%. |
| C-0EC6999D | Elena Sinclair | $79,419.00 | 2026-10-03 | 31/112 = 27.7% | 17 -> 16 -> 15 = -2 (-11.8%) | High | 31/112 = 27.7% utilization is below 60%. |
| C-0B20DB64 | Dana Mercer | $21,770.00 | 2026-10-07 | 214/378 = 56.6% | 294 -> 298 -> 294 = 0 (0.0%) | High | 214/378 = 56.6% utilization is below 60%. |
| C-0BBC4E7A | Cole Ingram | $56,374.00 | 2026-10-10 | 228/337 = 67.7% | 142 -> 141 -> 139 = -3 (-2.1%) | Medium | 228/337 = 67.7% utilization is below 75%. |
| C-0FD551AB | Elena Sinclair | $48,815.00 | 2026-10-14 | 210/376 = 55.9% | 123 -> 122 -> 126 = 3 (2.4%) | High | 210/376 = 55.9% utilization is below 60%. |
| C-0F9F8F13 | Dana Mercer | $46,230.00 | 2026-10-18 | 199/352 = 56.5% | 185 -> 185 -> 182 = -3 (-1.6%) | High | 199/352 = 56.5% utilization is below 60%. |
| C-0BC34584 | Cole Ingram | $16,740.00 | 2026-10-22 | 327/494 = 66.2% | 104 -> 104 -> 106 = 2 (1.9%) | Medium | 327/494 = 66.2% utilization is below 75%. |
| C-0B7A7546 | Elena Sinclair | $35,062.00 | 2026-10-25 | 182/205 = 88.8% | 64 -> 65 -> 63 = -1 (-1.6%) | Low | 182/205 = 88.8% utilization and 64 -> 63 = -1 (-1.6%) trend clear the medium-risk thresholds. |
| C-0B369871 | Dana Mercer | $85,128.00 | 2026-10-29 | 317/422 = 75.1% | 326 -> 330 -> 333 = 7 (2.1%) | Low | 317/422 = 75.1% utilization and 326 -> 333 = 7 (2.1%) trend clear the medium-risk thresholds. |
| C-0B144C78 | Cole Ingram | $30,899.00 | 2026-11-02 | 169/224 = 75.4% | 101 -> 101 -> 106 = 5 (5.0%) | Low | 169/224 = 75.4% utilization and 101 -> 106 = 5 (5.0%) trend clear the medium-risk thresholds. |
| C-0FC4DBB8 | Elena Sinclair | $94,732.00 | 2026-11-05 | 356/464 = 76.7% | 189 -> 191 -> 193 = 4 (2.1%) | Low | 356/464 = 76.7% utilization and 189 -> 193 = 4 (2.1%) trend clear the medium-risk thresholds. |
| C-0D5BBE3A | Dana Mercer | $39,740.00 | 2026-11-09 | 85/102 = 83.3% | 88 -> 90 -> 91 = 3 (3.4%) | Low | 85/102 = 83.3% utilization and 88 -> 91 = 3 (3.4%) trend clear the medium-risk thresholds. |
| C-0FB9D5AF | Cole Ingram | $63,158.00 | 2026-11-13 | 144/199 = 72.4% | 173 -> 173 -> 176 = 3 (1.7%) | Medium | 144/199 = 72.4% utilization is below 75%. |
| C-0B344485 | Elena Sinclair | $64,384.00 | 2026-11-16 | 224/287 = 78.0% | 238 -> 240 -> 244 = 6 (2.5%) | Low | 224/287 = 78.0% utilization and 238 -> 244 = 6 (2.5%) trend clear the medium-risk thresholds. |
| C-0CB2C1B4 | Dana Mercer | $40,628.00 | 2026-11-20 | 386/473 = 81.6% | 47 -> 48 -> 49 = 2 (4.3%) | Low | 386/473 = 81.6% utilization and 47 -> 49 = 2 (4.3%) trend clear the medium-risk thresholds. |
| C-22170CA1 | Cole Ingram | $45,646.00 | 2026-11-24 | 251/294 = 85.4% | 143 -> 148 -> 146 = 3 (2.1%) | Low | 251/294 = 85.4% utilization and 143 -> 146 = 3 (2.1%) trend clear the medium-risk thresholds. |

Renewal date disagreements to flag:

- C-0B7D2C30: ChurnZero 2026-09-10 vs Chargebee 2026-09-15; used Chargebee because is_multi_year = true.
- C-0BCDB8C2: ChurnZero 2027-09-18 vs Chargebee 2026-09-18; used Chargebee because is_multi_year = true.
- C-0D2AB865: ChurnZero 2026-09-10 vs Chargebee 2026-09-22; used Chargebee because is_multi_year = true.
- C-0BBE3E60: ChurnZero 2027-09-26 vs Chargebee 2026-09-26; used Chargebee because is_multi_year = true.
- C-0F5D2323: ChurnZero 2026-09-10 vs Chargebee 2026-09-29; used Chargebee because is_multi_year = true.

Totals:

- Total ARR renewing: $1,048,715.00
- ARR at risk: $612,496.00

Risk arithmetic:

- High risk ARR: $407,209.00
- Medium risk ARR: $205,287.00
- ARR at risk = $407,209.00 + $205,287.00 = $612,496.00
ticket-theme-synthesis0.80
Plain-English question: What are the real themes in this quarter's support tickets, ignoring the existing tags?
Prompt — what the model saw
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 $0.0038 · 1637s · in 106,395 / out 10,634 tok
✗ theme_count_range✓ counts_verifiable✓ single_account_flagged✓ arr_ranked✓ ticket_ids

✓ Correct Answer (ground truth)

Plain-English answer: 80 tickets in 4-7 themes — points-not-posting leads (20 tickets, 25%, $31,100 ARR affected) — each with counts, distinct accounts, ARR, and a recommendation.
The answer in one line: total_tickets = 80; quarter_window = 2026-06-01 through 2026-08-31
total_tickets80
noteexisting tags are noisy/unreliable; classify by text
quarter_window2026-06-01 through 2026-08-31
ranking_by_arr_exposurehris_provisioning, redemption_failures, billing_dispute, points_not_posting, slack_sync
sample_ticket_ids80 items — e.g. IC-460004, IC-460016, IC-460001, IC-460041
single_account_noise_themebilling_dispute
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": {"
graded checkstheme_count_range · counts_verifiable · single_account_flagged · arr_ranked · ticket_ids

Model's actual answer

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Data caveat: 2 ticket rows have missing `created_at`; I included them using `body_text`. ARR affected = sum of distinct account ARR in each theme, not double-counted.

Ranked by ARR exposure:

1. HRIS provisioning failures  
   - Pattern: Broad pattern across 3 accounts  
   - Count/share: 12 / 79 = 15.19%  
   - ARR affected: $36,000 + $48,000 + $30,000 = $114,000  
   - Accounts: C-0B2213A9, C-0DDFC9A7, C-0F6C0F34  
   - Two ticket IDs: IC-460059, IC-460055  
   - Recommendation: Prioritize HRIS provisioning reliability because new-hire account creation is failing across multiple high-ARR accounts.

2. Redemption/gift-card failures  
   - Pattern: Broad pattern across 7 accounts  
   - Count/share: 18 / 79 = 22.78%  
   - ARR affected: $10,300 + $10,700 + $8,900 + $9,600 + $8,700 + $9,600 + $11,000 = $68,800  
   - Accounts: C-0B0F1BAB, C-0B827671, C-0CEF69FD, C-0D9CA315, C-0F876796, C-0FCCD2DF, C-14264ABD  
   - Two ticket IDs: IC-460025, IC-460030  
   - Recommendation: Fix the redemption and gift-card fulfillment path, especially cases where checkout fails or gift cards are deducted without delivery.

3. Billing invoice/seat-count/tier errors  
   - Pattern: Single-account noise, high ARR exposure  
   - Count/share: 15 / 79 = 18.99%  
   - ARR affected: $52,000  
   - Accounts: C-0E9C27D1  
   - Two ticket IDs: IC-460071, IC-460069  
   - Recommendation: Correct billing invoice logic for seat counts and annual renewal tier pricing for C-0E9C27D1.

4. Recognition points not delivered  
   - Pattern: Broad pattern across 9 accounts  
   - Count/share: 20 / 79 = 25.32%  
   - ARR affected: $2,900 + $2,700 + $4,500 + $4,500 + $3,400 + $3,500 + $4,200 + $2,500 + $2,900 = $31,100  
   - Accounts: C-0B2895EF, C-0BE96399, C-0BF20542, C-0D0B047C, C-0D284E42, C-0D3278C7, C-0D6CC8E3, C-0DD0626C, C-21FEBCBB  
   - Two ticket IDs: IC-460004, IC-460016  
   - Recommendation: Investigate recognition delivery and points-posting failures because balance updates are failing across the largest account count.

5. Slack recognition sync/auth failures  
   - Pattern: Broad pattern across 4 accounts  
   - Count/share: 14 / 79 = 17.72%  
   - ARR affected: $4,400 + $3,900 + $5,400 + $5,200 = $18,900  
   - Accounts: C-0B843542, C-0BA71F12, C-10A56B0F, C-8C2E8F00  
   - Two ticket IDs: IC-460041, IC-460047  
   - Recommendation: Stabilize Slack recognition sync, re-auth behavior, and slash-command errors.
marketing 3 tests
case-study-matchPASS 1.00
Plain-English question: Which three existing customers are the best case-study matches for this prospect?
Prompt — what the model saw
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 $0.0005 · 10s · in 17,066 / out 1,119 tok
✓ top3✓ decoys_excluded✓ cites_fields✓ no_billing

✓ Correct Answer (ground truth)

Plain-English answer: Three ranked matches on industry, size, use case, and region (e.g. C-11C31562), with the higher-scoring decoy explicitly excluded for a mismatched field.
The answer in one line: prospect_alias = C-82AF3719
constraintsno_billing_data, no_contact_names, only_customers_with_has_case_study_true_named
decoys_excludedcompany_alias=C-D6217CAA; similarity_score=4; reason=no_case_study | company_alias=C-C153868F; similarity_score=4; reason=no_case_study
prospect_aliasC-82AF3719
prospect_fields_usedindustry, size_band, use_case, region
top3company_alias=C-11C31562; similarity_score=3 | company_alias=C-64171065; similarity_score=3 | company_alias=C-A13C193D; similarity_score=3
graded checkstop3 · decoys_excluded · cites_fields · no_billing

Model's actual answer

show the model's full answer
Prospect: C-82AF3719 — Technology / Mid-Market / employee_recognition / NA-West

| Rank | Customer alias | Case study | Similarity score | Fields driving match |
|---:|---|---|---:|---|
| 1 | C-11C31562 | true | 4/4 | industry = Technology; size_band = Mid-Market; use_case = employee_recognition; region = NA-West |
| 2 | C-64171065 | true | 3/4 | industry = Technology; size_band = Mid-Market; use_case = employee_recognition; region differs: NA-East vs NA-West |
| 2 | C-A13C193D | true | 3/4 | industry = Technology; size_band = Mid-Market; region = NA-West; use_case differs: retention vs employee_recognition |

Only customers with `has_case_study=true` were considered.
channel-attribution-mathPASS 1.00
Plain-English question: Compute cost per SQM, cost per SQO, and pipeline per dollar for every channel over six months.
Prompt — what the model saw
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 $0.0027 · 4959s · in 48,403 / out 13,663 tok
✓ undefined_channel✓ undefined_not_zero✓ flagged_rows✓ best_channel✓ reallocation

✓ Correct Answer (ground truth)

Plain-English answer: LinkedIn Ads at $960/SQM and $3,000/SQO, organic search at $90,000 pipeline on zero spend, paid social flagged at $18,000 with zero SQMs, and a channel winner named.
The answer in one line: best_pipeline_per_dollar_channel = paid_search
best_pipeline_per_dollar_channelpaid_search
confidence_notewebinars and referral have small SQM samples (<20); treat rates as directional.
flagged_rows_sqo_before_sqmCT-000041, CT-000044
months2026-03, 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
reallocation_recommendationfrom=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.
undefined_channelspaid_social
graded checksundefined_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. Metrics are as-loaded; flagged rows remain in counts.

Paid channels:

- paid_search
  - Spend: 6 x $6,000 = $36,000
  - SQMs: 40
  - SQOs: 18
  - Cost per SQM: $36,000 / 40 = $900
  - Cost per SQO: $36,000 / 18 = $2,000
  - SQM-to-SQO rate: 18 / 40 = 45.0%
  - Pipeline: $720,000
  - Pipeline per dollar: $720,000 / $36,000 = $20.00

- linkedin_ads
  - Spend: 6 x $4,000 = $24,000
  - SQMs: 25
  - SQOs: 8
  - Cost per SQM: $24,000 / 25 = $960
  - Cost per SQO: $24,000 / 8 = $3,000
  - SQM-to-SQO rate: 8 / 25 = 32.0%
  - Pipeline: $96,000
  - Pipeline per dollar: $96,000 / $24,000 = $4.00
  - Flagged SQO-before-SQM rows:
    - CT-000044: SQO 2026-07-18 precedes SQM 2026-07-23
    - CT-000041: SQO 2026-06-09 precedes SQM 2026-06-14

- paid_social
  - Spend: 6 x $3,000 = $18,000
  - Contacts data: no contacts_first_touch rows provided for paid_social
  - SQMs: 0
  - SQOs: 0
  - Cost per SQM: undefined, $18,000 / 0
  - Cost per SQO: undefined, $18,000 / 0
  - SQM-to-SQO rate: undefined, 0 / 0
  - Pipeline: $0
  - Pipeline per dollar: $0 / $18,000 = $0.00

- webinars
  - Spend: 6 x $1,500 = $9,000
  - SQMs: 12
  - SQOs: 5
  - Cost per SQM: $9,000 / 12 = $750
  - Cost per SQO: $9,000 / 5 = $1,800
  - SQM-to-SQO rate: 5 / 12 = 41.7%
  - Pipeline: $60,000
  - Pipeline per dollar: $60,000 / $9,000 = $6.67

Organic channel:

- organic_search
  - Volume: 30 SQMs
  - SQOs: 10
  - SQO rate: 10 / 30 = 33.3%
  - Pipeline: $90,000
  - Spend data: no spend data provided for organic_search

Reallocation recommendation:
Shift incremental paid spend away from linkedin_ads toward paid_search. linkedin_ads has weaker ROI at $4.00 pipeline per dollar and 2 SQO-before-SQM data-quality flags. paid_social should stay paused or investigated until tracking is fixed, because it has $18,000 spend but 0 tracked SQMs/SQOs. Organic_search should be protected because it produced $90,000 pipeline with no provided spend.

Confidence: Medium. paid_search has 40 SQMs / 18 SQOs and organic_search has 30 SQMs / 10 SQOs, but linkedin_ads has 2 invalid date rows and paid_social has no tracked contact sample despite spend.
weekly-marketing-goals-rollupPASS 1.00
Plain-English question: Are we on pace on the quarter's marketing goals?
Prompt — what the model saw
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 $0.0009 · 2092s · in 16,647 / out 4,261 tok
✓ metrics_present✓ deltas✓ coverage_ratio✓ pace✓ paragraph

✓ Correct Answer (ground truth)

Plain-English answer: SQMs ahead of pace, SQOs on pace, DS2s behind (35 short of expected at day 66 of 92), with deltas and same-quarter closes.
The answer in one line: days_in_quarter = 92; days_elapsed = 66; coverage_ratio_active_pipeline = 0.75; elapsed_fraction = 0.7174 (+1 more below)
days_in_quarter92
coverage_ratio_active_pipeline0.75
days_elapsed66
elapsed_fraction0.7174
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,
pace_ruleexpected = target * days_elapsed/days_in_quarter; within +/-5% of expected = on, above = ahead, below = behind; lower_better metrics invert.
this_week_paragraph_required1
graded checksmetrics_present · deltas · coverage_ratio · pace · paragraph

Model's actual answer

show the model's full answer
Weekly marketing goals update — Q3-2026, 66 of 92 days elapsed.

Pace formula: `QTD actual × 92 / 66`. Delta formula: `QTD actual - target`.

| Metric | QTD actual | Target | Delta | Pace math | Pace status |
|---|---:|---:|---:|---|---|
| SQMs | 230 | 300 | -70 | 230 × 92 / 66 = 320.6 | Ahead |
| SQOs | 84 | 120 | -36 | 84 × 92 / 66 = 117.1 | Behind |
| DS2s | 40 | 75 | -35 | 40 × 92 / 66 = 55.8 | Behind |
| closed-lost MIA rate | 20.0% | 10.0% | +10.0 pp | 5 / 25 = 20.0%; 20.0% × 92 / 66 = 27.9% | Behind |
| same-quarter closes | 10 | 20 | -10 | 10 × 92 / 66 = 13.9 | Behind |
| active pipeline coverage | 75.0% | 100.0% | -25.0 pp | 3,000,000 / 4,000,000 = 75.0%; 3,000,000 × 92 / 66 = 4,181,818 | Ahead |

No weekly movement data was provided in the files, so I cannot determine what moved this week or attribute any week-over-week change. The only computable view is QTD actual vs target and QTD run-rate pace.
communication 5 tests
ceo-slack-compressionPASS 1.00
Plain-English question: Compress the forecast analysis into one Slack message the CEO can read in ten seconds.
Prompt — what the model saw
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 $0.0007 · 25s · in 17,015 / out 2,302 tok
✓ max_3_sentences✓ under_80_words✓ weighted_present✓ caveat_present✓ no_em_dash

✓ Correct Answer (ground truth)

Plain-English answer: Three sentences, under 80 words, decision first, every distinct number kept plus the one caveat — all checked by deterministic text rules.
The answer in one line: weighted_number = 115976; must_keep = data-quality caveat — deleting the bad news fails
weighted_number115,976
limitmax 3 sentences, max 80 words, no em-dashes
must_keepdata-quality caveat — deleting the bad news fails
graded checksmax_3_sentences · under_80_words · weighted_present · caveat_present · no_em_dash

Model's actual answer

show the model's full answer
Decision: Q3 weighted forecast=44,729+0.35*203,565=115,976.75, but don't rely on it. In-quarter 54/86 deals: 7 COMMIT=44,729, 24 BEST_CASE=203,565, 23 PIPELINE=0; after 2026-09-30, 32 deals=227,575 including Deal-D348E1 COMMIT=13,770 and 9 BEST_CASE=28,240. Caveat: owner blank 85/86, why-buys empty 71/86 incl all 7 COMMIT, and 32 October dates.
stalled-deal-followup-emailPASS 1.00
Plain-English question: Write a follow-up email for this stalled deal — under 120 words, one prior touch, one ask.
Prompt — what the model saw
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 $0.0005 · 7s · in 16,783 / out 568 tok
✓ word_cap✓ references_touch✓ single_ask

✓ Correct Answer (ground truth)

Plain-English answer: An email that references the July 28 milestone-awards demo, makes one clear ask, and invents nothing — Deal-0D2F7A, 31 days since last engagement.
The answer in one line: amount = 5100; word_cap = 120; days_since_last_engagement = 31; deal_alias = Deal-0D2F7A (+4 more below)
amount5,100
days_since_last_engagement31
word_cap120
allowed_facts14 items — e.g. Deal-0D2F7A, $5,100, DS3, Alex Franklin
allowed_newENG-00001, ENG-00002, ENG-00003, ENG-00004, ENG-00005
deal_aliasDeal-0D2F7A
must_referenceengagement_id=ENG-00002; date=2026-07-28; type=meeting; why=most substantive prior touch; concrete prospect reactions to milestone awards and points catalog
ownerAlex Franklin
single_cta1
stageDS3
tonewarm, concise, professional (matches prior thread)
graded checksword_cap · references_touch · single_ask

Model's actual answer

show the model's full answer
Subject: Re: Deal-0D2F7A — July 28 demo recap + pricing

Hi People team,

Following up on the July 28 demo and the 150-seat pricing recap I sent over.

Were automated milestone awards and the points catalog still useful for your recognition process, or is this not a priority right now?

Could you let me know whether you’d like to revisit the pricing next, or if someone else should own the next step?

Thanks,  
Alex Franklin
funnel-digest-paragraphPASS 1.00
Plain-English question: Write the weekly GTM digest — one paragraph per team, every number traceable.
Prompt — what the model saw
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 $0.0005 · 7s · in 16,612 / out 602 tok
✓ four_paragraphs✓ word_cap✓ teams

✓ Correct Answer (ground truth)

Plain-English answer: Four paragraphs (Marketing, Sales, CS, PLG) using only the allowed numbers, no contact names, no performance commentary, under 400 words.
The answer in one line: word_cap = 400
word_cap400
allowed_numbers16 items — e.g. 2, 3, 9, 08
bannedcustomer contact names, employee performance commentary, compensation
required_paragraphsMarketing, Sales, CS, PLG
graded checksfour_paragraphs · word_cap · teams

Model's actual answer

show the model's full answer
Marketing finished the week with 46 SQMs against a 52-SQM target, leaving a 6-SQM gap. Webinar delivered 18 of the 46 SQMs, and paid search cost per SQM stayed at $150.

Sales converted 14 SQOs, set 9 DS2 meetings, and created $310,000 in new pipeline. The team also recorded 3 same-quarter closes this week.

CS closed out 2 renewals saved this week, with Team NPS at 61. There are 3 open red-flag accounts heading into next week.

PLG added 412 new signups, with activation at 31%. 38 companies reached the aha moment of 10 recognition gives.
partner-digest-sectionPASS 1.00
Plain-English question: Write the partner digest so every partner in the roster appears exactly once.
Prompt — what the model saw
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 $0.0006 · 10s · in 16,833 / out 1,308 tok
✓ all_partners✓ pipeline_amount✓ quiet_one_line

✓ Correct Answer (ground truth)

Plain-English answer: Four active partners with status and numbers, four quiet partners with one line each, and partner-sourced pipeline that reconciles to the deal data.
The answer in one line: rule = every roster partner named exactly once; quiet partners get one line; no partners outside roster.
active_no_dealsWorkWell Group
partner_sourced_pipelinecount=5; amount=550000
per_partner_pipeline{"Apex Rewards Co": {"count": 2, "amount": 275000}, "HRCloud Partners": {"count": 1, "amount": 140000}, "CultureBridge": {"count": 2, "amount": 135000}}
quiet_partnersRecogniq, KudosWave, PeopleFirst Advisors, TotalPerk
rosterpartner_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)
ruleevery roster partner named exactly once; quiet partners get one line; no partners outside roster.
graded checksall_partners · pipeline_amount · quiet_one_line

Model's actual answer

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

Partner-sourced pipeline total: 5 deals / $550,000  
Arithmetic: $180,000 + $95,000 + $140,000 + $60,000 + $75,000 = $550,000

Apex Rewards Co — Active — 2 sourced deals / $275,000  
Deals: Deal-DDAAF2 ($180,000), Deal-2CF33E ($95,000)  
Status: co-webinar locked for 09-15; both opps logged with UTM Source = Partner.

HRCloud Partners — Active — 1 sourced deal / $140,000  
Deal: Deal-F1CDA5 ($140,000)  
Status: integration referral closed security review; opp moved to DS2.

CultureBridge — Active — 2 sourced deals / $135,000  
Deals: Deal-096E1D ($60,000), Deal-067213 ($75,000)  
Status: lunch-and-learn produced two sourced opps; both early stage.

WorkWell Group — Active — 0 sourced deals / $0  
Status: wants to restart joint playbook in Q4; planning call booked for 09-09.

Recogniq — Quiet — no partner-sourced deals provided; 0 / $0

KudosWave — Quiet — no partner-sourced deals provided; 0 / $0

PeopleFirst Advisors — Quiet — no partner-sourced deals provided; 0 / $0

TotalPerk — Quiet — no partner-sourced deals provided; 0 / $0
rfp-answer-from-policyPASS 1.00
Plain-English question: Answer the 10 RFP items from the policy excerpts only — no guessing.
Prompt — what the model saw
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 $0.0009 · 8s · in 33,860 / out 696 tok
✓ answered_count✓ unanswerable_flagged✓ citations✓ no_guess

✓ Correct Answer (ground truth)

Plain-English answer: Seven answered with excerpt citations (Q1-Q7), three flagged unanswerable with the document that would answer them (Q8-Q10).
The answer in one line: answered_count = 7; rule = unanswerable items must be flagged and the document that would answer them named; do not guess.
answered_count7
answer_map{"Q1": ["P1"], "Q2": ["P1"], "Q3": ["P2"], "Q4": ["P3"], "Q5": ["P4"], "Q6": ["P5"], "Q7": ["P6"]}
ruleunanswerable items must be flagged and the document that would answer them named; do not guess.
unanswerableQ8, Q9, Q10
valid_citation_idsP1, P2, P3, P4, P5, P6, P7, P8
graded checksanswered_count · unanswerable_flagged · citations · no_guess

Model's actual answer

show the model's full answer
| Item | Response |
|---|---|
| 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, and it is available to prospects under NDA. [P2] |
| Q4 | Customer data is retained for 90 days after contract termination. [P3] |
| Q5 | The subprocessor list is maintained 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. The excerpts do not state whether Bonusly holds ISO 27001 certification. The security certifications documentation would need to confirm this. |
| Q9 | Unanswerable. The excerpts do not state a contractual uptime SLA percentage. The contract or SLA document would need to confirm this. |
| Q10 | Unanswerable. The excerpts do not state whether Bonusly will sign a HIPAA Business Associate Agreement. The legal/privacy addendum or BAA terms would need to confirm this. |
ops-maintenance 4 tests
runbook-from-slack-threadPASS 1.00
Plain-English question: Turn the resolved incident thread into a runbook.
Prompt — what the model saw
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 $0.0010 · 2725s · in 34,271 / out 1,366 tok
✓ ordered_steps✓ rollback✓ needs_confirmation✓ sourced

✓ Correct Answer (ground truth)

Plain-English answer: Ordered steps from the thread, each with the exact command, who ran it, how success was verified, and rollback where state changed.
The answer in one line: rule = no steps beyond what the thread contains; every step traces to a message id.
ambiguous_steporder=4; msg=M05; mark=needs-confirmation
ruleno steps beyond what the thread contains; every step traces to a message id.
stepsorder=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)
graded checksordered_steps · rollback · needs_confirmation · sourced

Model's actual answer

show the model's full answer
Runbook from incident thread

| Order | Thread trace | Step / action taken | Ran by | Success verified how | Rollback / recovery |
|---:|---|---|---|---|---|
| 1 | [M01] | Acknowledged PagerDuty alert and took incident command for reward-worker queue depth > 10k. | Bryce Harmon | Not provided. | Not provided. |
| 2 | [M02] | Ran `bundle exec rake sidekiq:queue_depth`. | Farid Osman | Reward queue was 48,213 pending jobs; normal is under 500. | Not a state-changing step. |
| 3 | [M03] | Checked dead set. | Farid Osman | Found 112 dead jobs, all `Redis::TimeoutError` from around 13:58. | Not a state-changing step. |
| 4 | [M04] | Paused enqueue to stop the bleed. Command: `bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'`. | Farid Osman | Not explicitly verified at [M04]. | If needed: `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'`. |
| 5 | [M05] | Cleared out the dead set while in the console. | Elena Sinclair | Not explicitly verified in the thread. | Not provided. Needs confirmation. |
| 6 | [M06] | Scaled workers up. Command: `kubectl scale deployment/reward-worker --replicas=6`. | Bryce Harmon | Not explicitly verified at [M06]. | `kubectl scale deployment/reward-worker --replicas=3`. |
| 7 | [M07] | Checked queue depth after scaling. | Farid Osman | Queue depth was 9,400 and falling ~1,200/min. | Not a state-changing step. |
| 8 | [M08] | Verified recovery. Command: `bundle exec rake sidekiq:queue_depth`. | Cole Ingram | Command returned 0. Datadog error rate was back to baseline. | Not a state-changing step. |
| 9 | [M09] | Re-enabled enqueue. Command: `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'`. | Bryce Harmon | Verified 40 new jobs processed cleanly in the next 3 minutes. | Disable enqueue with `bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'`. Needs confirmation if needed. |
| 10 | [M10] | Scaled workers back down. Command: `kubectl scale deployment/reward-worker --replicas=3`. | Bryce Harmon | Queue stable at 0. | This was the rollback from the scale-up in [M06]. Incident resolved. |

Confirmation gaps in the thread:
- [M05] dead-set clearing has no explicit command or immediate verification.
- [M04] pause enqueue has no immediate verification before later recovery checks.
- [M06] scale-up has no immediate verification before [M07].
- [M09] re-enable enqueue rollback is inferred from the rollback listed in [M04], but the thread does not explicitly state a rollback was tested or needed.
datadog-incident-timelinePASS 1.00
Plain-English question: Sequence the failure from the logs — what broke first, and what cascaded after it?
Prompt — what the model saw
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 $0.0016 · 2080s · in 18,432 / out 11,188 tok
✓ first_error_ts✓ service_named✓ cascade_ordered✓ query✓ gaps_stated

✓ Correct Answer (ground truth)

Plain-English answer: A reward-service Redis timeout at 14:01:12Z cascades to sidekiq at 14:01:40 and api-gateway at 14:03:05, with the Datadog query that confirms the first error.
The answer in one line: job_class_involved = RewardGiveJob
cascade_orderservice=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
clean_servicespostgres
confirming_queryservice:reward-service status:error "@message:Redis::TimeoutError"
first_errortimestamp=2026-09-03T14:01:12Z; service=reward-service; message=Redis::TimeoutError: Connection to Redis at redis-primary:6379 timed out after 5s
job_class_involvedRewardGiveJob
logs_do_not_showroot 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)
graded checksfirst_error_ts · service_named · cascade_ordered · query · gaps_stated

Model's actual answer

show the model's full answer
First error:
- Timestamp: 2026-09-03T14:01:12Z
- Service: reward-service
- Error: Redis::TimeoutError: Connection to Redis at redis-primary:6379 timed out after 5s
- Job: not named in the first error line
- First job reference: RewardGiveJob at 2026-09-03T14:01:20Z
- Arithmetic: 14:01:20 - 14:01:12 = 8s

Datadog query to confirm the first error:
```text
@service:reward-service @message:"Redis::TimeoutError: Connection to Redis at redis-primary:6379 timed out after 5s" @timestamp:>=2026-09-03T14:01:12Z @timestamp:<2026-09-03T14:01:20Z
```

Cascade in order, from first error:
- 14:01:12Z — reward-service — Redis::TimeoutError to redis-primary:6379 — 0s
- 14:01:20Z — reward-service — RewardGiveJob retry exhausted — +8s
- 14:01:30Z — reward-service — RewardGiveJob retry exhausted — +18s
- 14:01:40Z — reward-service — RewardGiveJob retry exhausted — +28s
- 14:01:40Z — sidekiq — RewardGiveJob failed: Redis::TimeoutError; retrying in 60s — +28s
- 14:01:46Z — sidekiq_jobs.csv — J-00005 RewardGiveJob failed_at 14:01:46Z — +34s
- 14:01:51Z — sidekiq_jobs.csv — J-00001 RewardGiveJob — +39s
- 14:01:54Z — sidekiq_jobs.csv — J-00003 RewardGiveJob — +42s
- 14:01:55Z — sidekiq_jobs.csv — J-00006 RewardGiveJob — +43s
- 14:01:56Z — sidekiq_jobs.csv — J-00002 RewardGiveJob — +44s
- 14:01:57Z — sidekiq_jobs.csv — J-00004 RewardGiveJob — +45s
- 14:02:28Z — sidekiq — RewardGiveJob failed: Redis::TimeoutError; retrying — +76s
- 14:02:30Z — sidekiq — Queue reward depth above 10,000 — +78s
- 14:02:36Z — sidekiq_jobs.csv — J-00013 RecognitionDigestJob — +84s
- 14:02:51Z — sidekiq_jobs.csv — J-00007 RewardGiveJob — +99s
- 14:02:56Z — sidekiq_jobs.csv — J-00008 RewardGiveJob — +104s
- 14:02:57Z — sidekiq_jobs.csv — J-00010 RewardGiveJob — +105s
- 14:02:57Z — sidekiq_jobs.csv — J-00012 RewardGiveJob — +105s
- 14:02:58Z — sidekiq_jobs.csv — J-00009 RewardGiveJob — +106s
- 14:03:05Z — api-gateway — 502 upstream timeout calling reward-service /gives — +113s
- 14:03:15Z — sidekiq_jobs.csv — J-00011 RewardGiveJob — +123s
- 14:03:15Z — sidekiq_jobs.csv — J-00014 RecognitionDigestJob — +123s
- 14:03:30Z — web-app — Give form submission failed: upstream 502 from api-gateway — +138s
- 14:03:31Z — sidekiq — RewardGiveJob failed: Redis::TimeoutError; retrying — +139s
- 14:03:48Z — api-gateway — 502 upstream timeout calling reward-service — +156s
- 14:04:13Z — api-gateway — 502 upstream timeout calling reward-service — +181s
- 14:04:22Z — sidekiq — RewardGiveJob failed: Redis::TimeoutError; retrying — +210s
- 14:04:45Z — web-app — Give form submission failed: upstream 502 — +213s
- 14:04:55Z — sidekiq_jobs.csv — J-00015 RecognitionDigestJob — +223s
- 14:05:16Z — api-gateway — 502 upstream timeout calling reward-service — +244s
- 14:05:26Z — sidekiq — RewardGiveJob failed: Redis::TimeoutError; retrying — +254s
- 14:05:42Z — web-app — Give form submission failed: upstream 502 — +270s
- 14:05:50Z — sidekiq_jobs.csv — J-00016 RecognitionDigestJob — +278s
- 14:06:47Z — sidekiq — RewardGiveJob failed: Redis::TimeoutError; retrying — +355s
- 14:06:49Z — web-app — Give form submission failed: upstream 502 — +357s
- 14:06:52Z — api-gateway — 502 upstream timeout calling reward-service — +360s
- 14:22:10Z — reward-service — Redis connection restored; resuming job processing — +1258s
- 14:24:45Z — sidekiq — Queue reward depth below 500 — +1413s

Sidekiq failed job summary:
- RewardGiveJob failed jobs: J-00001, J-00002, J-00003, J-00004, J-00005, J-00006, J-00007, J-00008, J-00009, J-00010, J-00011, J-00012
- Count: 12
- RecognitionDigestJob failed jobs: J-00013, J-00014, J-00015, J-00016
- Count: 4
- Total failed jobs in sidekiq_jobs.csv: 12 + 4 = 16
- First failed job by failed_at: J-00005 RewardGiveJob at 2026-09-03T14:01:46Z
- Arithmetic: 14:01:46 - 14:01:12 = 34s
- First RecognitionDigestJob: J-00013 at 2026-09-03T14:02:36Z
- Arithmetic: 14:02:36 - 14:02:30 = 6s after the queue-depth warning

What the logs do not show:
- No root cause for the Redis::TimeoutError.
- No Redis host health, Redis latency, Redis pool saturation, or network-path evidence.
- No exact queue depth, only “above 10,000” and later “below 500.”
- No failed submission count, customer impact count, revenue impact, deal count, or company/deal aliases.
- No job payloads or arguments.
- No datadog_logs.csv entry for RecognitionDigestJob; RecognitionDigestJob failures appear only in sidekiq_jobs.csv.
- No evidence that postgres caused the failure; postgres entries are only INFO checkpoint complete records.
- No deal/company aliases are present in the provided data.
feature-flag-state-summaryPASS 1.00
Plain-English question: Summarize what each feature flag controls and who has it on.
Prompt — what the model saw
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 $0.0012 · 22s · in 33,900 / out 3,348 tok
✓ orphans✓ no_targets✓ counts_match✓ no_invented

✓ Correct Answer (ground truth)

Plain-English answer: Recognition streaks on for 42 beta companies, redeem-flow redesign off with a 12-company target list, plus any flag with no code reference or no targets.
The answer in one line: rule = company counts must equal the export; do not describe flags absent from the export.
code_referencedrecognition_streaks_v2, points_budget_guardrails, slack_dm_nudges, redeem_flow_redesign, analytics_dashboard_v3, ms_teams_app_v2
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
flags_with_no_targetspaused_offboard_cleanup
orphan_flags_no_code_referencelegacy_give_modal, survey_boosters_q3, paused_offboard_cleanup
rulecompany counts must equal the export; do not describe flags absent from the export.
graded checksorphans · no_targets · counts_match · no_invented

Model's actual answer

show the model's full answer
| flag_key | state | company_count | targeting_rules | Companies/segments with it on | Controls per code excerpt |
|---|---:|---:|---|---|---|
| recognition_streaks_v2 | on | 42 | segment:beta_companies | segment:beta_companies | Controls recognition streak tracking: if enabled, `StreakTracker.record(give)` runs. |
| points_budget_guardrails | on | 220 | all_companies | all_companies | Controls points budget guardrails: if enabled, `BudgetService.new(company).enforce!(giver, points)` runs. |
| slack_dm_nudges | on | 87 | segment:region_na | segment:region_na | Controls Slack DM nudges: if enabled, `SlackDm.send_nudge(user)` runs. |
| redeem_flow_redesign | off | 12 | targeted_list | none on; state is off | Controls redeem flow version: if enabled, `RedeemV2Component` renders; otherwise `RedeemV1Component` renders. |
| analytics_dashboard_v3 | on | 65 | segment:tier_three | segment:tier_three | Controls analytics dashboard v3: if enabled, `@dashboard = AnalyticsV3.new(company)` is set. |
| ms_teams_app_v2 | off | 9 | targeted_list | none on; state is off | Controls Microsoft Teams app v2 install: if enabled, `TeamsAppV2.install(company)` runs. |
| legacy_give_modal | off | 14 | segment:legacy_plan | none on; state is off | No code reference found in the excerpt. |
| survey_boosters_q3 | on | 7 | segment:legacy_plan | segment:legacy_plan | No code reference found in the excerpt. |
| paused_offboard_cleanup | off | 0 | blank / no targets | none on; state is off | No code reference found in the excerpt; no targets provided. |

Counts from export:

- On total: 42 + 220 + 87 + 65 + 7 = 421
- Off total: 12 + 9 + 14 + 0 = 35
- Total company_count: 421 + 35 = 456

Flags with no code reference in the excerpt:
- legacy_give_modal
- survey_boosters_q3
- paused_offboard_cleanup

Flag with no targets:
- paused_offboard_cleanup
nda-triagePASS 1.00
Plain-English question: Triage each NDA green, yellow, or red — and quote the clause that decides it.
Prompt — what the model saw
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 $0.0005 · 5s · in 16,869 / out 313 tok
✓ green✓ yellow✓ red✓ non_solicit_caught✓ clause_quoted✓ correct_labels

✓ Correct Answer (ground truth)

Plain-English answer: NDA-1 green (standard mutual terms), NDA-2 yellow for an embedded 18-month non-solicit, with the driving clause quoted for every classification.
The answer in one line: non_solicit_in = NDA-2
checksclassification_per_nda, clause_quotes_exist_in_document, non_solicit_caught, carveouts_checked
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_inNDA-2
graded checksgreen · yellow · red · non_solicit_caught · clause_quoted · correct_labels

Model's actual answer

show the model's full answer
NDA-1 — GREEN: Clause 5 says “No license, no obligation to proceed, no exclusivity,” and the term/governing law are standard Delaware with normal carve-outs, so it appears suitable for standard approval.

NDA-2 — YELLOW: Clause 4 embeds a 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,” requiring counsel review.

NDA-3 — RED: Clause 2 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” — and Clause 4 states “No confidentiality carve-outs are stated in this Agreement,” requiring full legal review.