Tasks / Product Craft

Extract discovery insights

Can the model separate evidence, themes and hypotheses without inventing consensus?

Task type v1.1 · 2 tasksLast changed 6 Oct 2026 · ChangelogDifficulty

What AI gets right here, and what you’ll still have to catch

From 18 graded outputs by 9 models. 50% were usable with at most a quick edit.

Reliably right

  1. Frames findings for the decision100% pass
    Each main finding is framed as an opportunity (delivery-to-invoice pain, concentrated supplier need) and linked directly to what to build next quarter.
    GPT-6 Astra · ChatGPT · The loudest ticket
  2. Corrects the draft's claim100% pass
    It corrects the draft's headline by counting interviews (8 describe delivery/invoice pain, 2 mention supplier expansion) and showing the marketplace claim is unsupported.
    GPT-6 Astra · ChatGPT · The loudest ticket
  3. Keeps dissent visible100% pass

Where it slips

  1. Says how many sources support each finding46% pass
    The finding on smaller issues sizes Ordering as 31 tickets, but the actual count is 30, so the finding is not accurately sized.
    Sonnet 5.5 · API · The loudest ticket
  2. Uses the supplied evidence correctly56% pass
    The output states Ordering has 31 tickets, but the CSV contains 30 Ordering tickets; this factual claim is not supported by the supplied data.
    GPT-6.1 Sol · API · The loudest ticket
  3. Addresses the actual decision57% pass
    The output commits to a clear recommendation but does not state what result or condition would change that decision.
    Gemini 3.8 Flash · API · The loudest ticket

How it’s graded

Each check is built on how world-class product and growth leaders do this work, as they’ve described it on Lenny’s Podcast, and names the leader it comes from.

  1. Weights behaviour over opinion

    Does the synthesis distinguish evidence of what customers actually did (specific past instances, observed behaviour, workarounds) from stated preferences, predictions and feature requests, and weight them accordingly?

    Passes when Themes are labelled or weighted by evidence type, and at least one theme resting mainly on stated intent or requests is flagged as weaker.

  2. Frames findings for the decision

    Does the synthesis turn its findings into opportunities tied to the decision or outcome the team faces, saying what each one means for that decision?

    Passes when Each main finding is framed as an opportunity (a need or pain to address) and linked to the decision at hand, with what it implies.

  3. Reports what cuts against the plan

    Does the synthesis report the evidence that cuts against the team's current belief or plan, rather than only what supports it?

    Passes when Names at least one finding that challenges the team's hypothesis or plan, and says what it means for it.

Plus our standard checks

Uses the supplied evidence correctly · Addresses the actual decision · Respects explicit constraints · Identifies material uncertainty · Avoids unsupported claims · Produces the required deliverable · Says how many sources support each finding · and 2 written for each task, which you’ll see in the tasks below.

The tasks

Read the brief, then compare up to three outputs, each with the LLM judge’s verdict on every check. Highlights mark what a PM had to fix.

The brief

You're the PM for Larder's ordering product. A colleague has drafted the synthesis below for our CPO, recommending a supplier marketplace. Before it goes, rewrite the synthesis from the evidence, in no more than 800 words: the main findings, how strong the evidence for each is, and what they mean for what we build next quarter. The interviews and the ticket export are attached.

What the model was given4 items: About Larder, draft_synthesis.md, interview_excerpts.md, support_tickets_last_quarter.csv
About LarderOrdering software for restaurants: order from your suppliers in one place, track deliveries and invoices. 1,140 accounts, most of them independent restaurants.
draft_synthesis.md6 lines · Download
# Draft synthesis (PM, for the CPO)

Headline: customers want a supplier marketplace.
- "Add more suppliers" is our top support category by far.
- Most customers we spoke to want a marketplace (see Calder Group and the pizzeria).
- Recommendation: build a supplier marketplace next quarter.
interview_excerpts.md12 lines · Download
# Interview excerpts (restaurant owners and managers, September)

1. Bistro, 2 sites: "Last Tuesday the fish order came short again. I only found out when the invoice came, a week later. I spent Sunday night matching delivery notes to invoices with a highlighter."
2. Café group, 5 sites: "We switched produce supplier twice this year, but finding suppliers isn't the problem. Knowing what actually arrived is."
3. Pizzeria, 1 site: "Honestly I'd love more suppliers in the app. More choice, better prices."
4. Gastropub: "My head chef signs the delivery note without checking. Then I pay for things we never got. Last month that was about £600."
5. Hotel restaurant: "Our finance team chases credit notes for short deliveries every week. It takes days."
…
support_tickets_last_quarter.csv200 rows · Download
ticket,account,account_sites,category,summary
LT-5000,Calder Group,14,Add more suppliers,Can you onboard our meat supplier
LT-5001,Account 1886,3,Ordering,Change delivery day
LT-5002,Account 2004,1,Delivery didn't match order,Short delivery not shown in app
LT-5003,Calder Group,14,Add more suppliers,Add dairy supplier please
LT-5004,Calder Group,14,Add more suppliers,Add dairy supplier please
LT-5005,Account 1858,2,Add more suppliers,Any plans for more suppliers?
…
What a strong answer doesThe answer key the graders mark against

Shows that the marketplace headline doesn't hold. 'Add more suppliers' is the biggest ticket category (75 of 200), but 58 of those come from one account, Calder Group; across other accounts, delivery and invoice problems ('delivery didn't match order' and 'invoice reconciliation', 63 tickets) are far more common than supplier requests (17). In the interviews, 7 of 10 describe a specific recent instance of the same pain (short deliveries, invoices that don't match, credit notes, re-typing paper checks), while the marketplace mentions are wishes (the pizzeria 'would love' it, the tapas bar 'I suppose') or come from Calder. Weights what people did over what they say they'd want, counts how many sources support each finding, and recommends focusing next quarter on checking deliveries and reconciling invoices, with the marketplace treated as one large customer's request to handle separately.

Critical failures (cap the score at 40)
  • Recommends building the supplier marketplace next quarter as the main finding
Task

Staff-level · v1.1

The outputs

9 setups answered this brief.

Needs more than a quick edit9 of 12 checks passed

The output contains several arithmetic errors in ticket counts (e.g., 72 instead of 75, 36 instead of 33) that would force a PM to re-verify the data before using it.

Output

Product Discovery Synthesis: Q3 Interviews & Support Tickets

To: Chief Product Officer From: PM, Ordering Subject: Correcting the Q4 roadmap recommendation: Delivery & Invoice Reconciliation vs. Supplier Marketplace

Executive Summary

An initial look at last quarter’s data suggested we should build a supplier marketplace next quarter. However, a deeper analysis of our 200 support tickets and 10 customer interviews shows the marketplace signal is a mirage caused by a single vocal enterprise account.

For our core customer base of independent restaurants, the most urgent, expensive, and widespread problem is delivery tracking and invoice reconciliation—specifically handling short deliveries, substitutions, and credit notes. We should focus Q4 on fixing the post-order workflow, not building a marketplace.

---

Main Findings & Strength of Evidence

#### 1. The "Supplier Marketplace" demand is heavily skewed and misunderstood * Strength of Evidence: Weak * What the data actually shows: On the surface, "Add more suppliers" is our #1 ticket category, making up 72 of 200 tickets (36%). However, 56 of those 72 tickets (78%) came from a single 14-site customer: Calder Group. The remaining 16 tickets came from just 16 unique accounts (out of 1,140 total). * The Nuance: Even Calder Group’s tickets are not requests to discover new suppliers in a marketplace. They are repeatedly asking us to onboard their existing suppliers (their meat supplier, dairy supplier, wine merchant, and Brakes) so they can centralize ordering. * Interview Context: In interviews, only 1 of 10 customers (the single-site Pizzeria) genuinely wanted a marketplace for supplier discovery and price comparison. Calder Group used the word "marketplace" to mean having all their current vendors on the platform. Two others actively dismissed the idea: the Tapas Bar expressed lukewarm interest before pivoting to paperwork pain, and the 5-site Café Group explicitly stated: "Finding suppliers isn't the problem. Knowing what actually arrived is."

#### 2. Delivery mismatches and invoice reconciliation are our largest widespread pain points * Strength of Evidence: Strong * What the data actually shows: In our interviews, 8 out of 10 customers identified post-order tracking (short deliveries, substitutions, invoice matching, and chasing credit notes) as their primary operational headache. This directly causes financial loss (e.g., the Gastropub losing ~£600/month paying for undelivered goods) and severe time waste (owners spending Sunday nights matching paper notes to invoices, or re-typing clipboard notes into Larder). * Support Ticket Alignment: In the ticket data, "Delivery didn't match order" (36 tickets across 36 unique accounts) and "Invoice reconciliation" (31 tickets across 30 unique accounts) are two halves of the exact same problem. Combined, they account for 67 tickets across 64 unique accounts—four times the unique account volume of the supplier requests (excluding Calder Group). * Specific Product Gaps: Customers are explicitly telling us where the product breaks down: * At the back door: Short deliveries aren't showing in the app (9 tickets), users don't know how to log substitutions (8 tickets), and chefs check deliveries on paper clipboards—likely exacerbated by our tablet login bug (10 tickets). * In the back office: Invoices include undelivered items (9 tickets), invoice totals don't match orders (9 tickets), users can't match invoices to orders (10 tickets), credit notes are missing (8 tickets), and accountant exports fail to show credits (10 tickets).

#### 3. Core ordering and access friction creates unnecessary noise * Strength of Evidence: Moderate (Ticket data only) * What the data actually shows: We saw 31 tickets for basic "Ordering" issues (10 duplicate orders, 11 delivery day changes, 10 cut-off time questions) and 30 for "Login and access" (10 password resets, 10 adding users, 10 tablet login failures). While unmentioned in interviews, these represent 30% of support volume and hurt day-to-day usability.

---

What This Means for Q4: Recommendations

Building a two-sided supplier marketplace next quarter would be a costly misstep. It solves a problem (supplier discovery) that almost none of our customers have, based on un-deduplicated ticket data from one large account.

Instead, we should align our Q4 roadmap with our core value proposition—"track deliveries and invoices"—where our independent restaurants are bleeding time and money:

1. Build a "Goods-In" & Discrepancy Workflow (Primary Q4 Bet): * Allow kitchen staff to easily check off deliveries on mobile/tablet at the back door, logging short weights, missing items, and substitutions in real time. * Automatically flag discrepancies against the original order, adjust expected invoice totals, and track pending credit notes through to the accountant export. 2. Fix the Tablet Login Bug & Ordering UX (Quick Wins): * Fixing the tablet login issue (10 tickets) is a prerequisite for kitchen staff checking deliveries digitally instead of on clipboards. * Address duplicate order prevention and surface supplier cut-off times clearly in the UI to cut ordering support tickets. 3. Handle Calder Group Operationally, Not via a Marketplace: * Have Customer Success and Partnerships work directly with Calder Group to onboard their four requested suppliers (Brakes, meat, dairy, wine). For the broader base, improve our "request to onboard my existing supplier" intake flow so sites don't spam support.

Claims the judge couldn’t find in the brief

  • "Add more suppliers" is the #1 ticket category, making up 72 of 200 tickets (36%).
  • 56 of those 72 tickets (78%) came from Calder Group.
  • The remaining 16 tickets came from 16 unique accounts.
  • "Delivery didn't match order" has 36 tickets.
  • "Invoice reconciliation" has 31 tickets.

Grades and run details

Decision model 71 · LLM judge 77
Decision model checks
  • failUses the supplied evidence correctly37%
  • failAddresses the actual decision31%
  • passRespects explicit constraints67%
  • failIdentifies material uncertainty77%
  • partialAvoids unsupported claims51%
  • passProduces the required deliverable95%
  • passSees one account behind the top ticket97%
  • passCorrects the draft's claim94%
  • passWeights behaviour over opinion100%
  • passSays how many sources support each finding43%
  • passFrames findings for the decision97%
  • passReports what cuts against the plan100%
Run
Run
#1
API response time
15 s
Submitted
13 Oct 2026

Needs more than a quick edit9 of 12 checks passed

Factual errors in ticket counts (ordering 31 vs 30, credits export 8 vs 10) and word count exceedance mean a PM would need to re-check evidence and edit for length.

Output

Synthesis: what customers need from Larder next (for the CPO)

Headline: The broadest and best-evidenced problem is that customers can't easily tell what actually arrived, and it costs them money and hours every week. Demand for "more suppliers" is real, but nearly all of it comes from one account. That account wants its own four suppliers connected, which is not the same as a marketplace. Next quarter we should build delivery check-in and discrepancy tracking, and handle Calder Group's request as a bounded onboarding project.

Finding 1: Delivery and invoice mismatches are the most widespread pain

Evidence: strong. It is consistent across both sources, comes from many accounts, and customers describe concrete costs.

  • Tickets: "Delivery didn't match order" (34) and "Invoice reconciliation" (29) describe the same problem from two ends: ordered, received and invoiced don't line up. Together they account for 63 of 200 tickets (32%) from about 62 different accounts. By number of customers affected, this is our largest problem.
  • Interviews: 8 of 10 describe it. Examples:
  • About £600 lost in a month on undelivered items (gastropub).
  • A finance team spending days every week chasing credit notes (hotel).
  • Sunday nights reconciling invoices with a highlighter (bistro).
  • Disputes with suppliers that can't be won without proof (bakery).
  • Checking deliveries on a clipboard, then re-typing everything (noodle bar).
  • Paying mismatched invoices because there's no time to check (steakhouse).

Finding 2: "Add more suppliers" is the top ticket category, but it is essentially one customer

Evidence: strong that the demand is concentrated; weak that there is broad demand for a marketplace.

- Calder Group's tickets: 75 tickets (37.5%) fall in this category, but 58 come from Calder Group alone. They repeatedly ask for the same four suppliers: - Brakes (15 tickets) - Their meat supplier (15) - Their dairy supplier (14) - Their wine merchant (14) They want suppliers they already use connected to Larder. They are not asking to discover new suppliers. - Everyone else: The remaining 17 tickets come from 17 accounts and are mostly generic ("Any plans for more suppliers?", "Would like more produce suppliers"). - Interviews: Only 2 of 10 clearly want this (the pizzeria and Calder). The tapas bar is lukewarm ("I suppose") and says paperwork is the real problem. The café group says outright that "finding suppliers isn't the problem."

The earlier claim that "most customers we spoke to want a marketplace" is not supported.

Finding 3: Calder is a real commercial opportunity of a specific kind

Evidence: moderate. This is one account's clear statement, untested.

  • Calder has 14 sites and says it would move all its ordering to us if its suppliers were on the platform.
  • We don't yet know how much of its ordering runs through Larder today or what the account is worth. We need that before sizing the effort.
  • Their need is met by onboarding four named suppliers, not by building a marketplace.

Other signals

  • Login and access (31 tickets) and ordering questions (31) look routine.
  • One subset matters for the recommendation below: 10 tickets are "can't log in on tablet," and the tablet is where delivery check-in would happen.

Limits of this evidence

  • Small interview sample. There are 10 interviews and we don't know how participants were chosen.
  • One quarter of tickets. Ticket volume measures who contacts support, not who has the problem. Customers who silently pay wrong invoices, like the steakhouse, won't file tickets, so mismatch pain is probably under-counted.
  • Data quality. Support staff assign the categories, and a few accounts show inconsistent site counts across tickets.
  • No usage or revenue data yet. We can't weigh Calder against the long tail of smaller accounts.

What this means for next quarter

1. Build delivery check-in and discrepancy tracking. This is our core promise ("track deliveries and invoices") and is where the broad pain sits. Scope: - Check a delivery against the order at the door, on phone or tablet, replacing the clipboard and the re-typing. - Record shorts, substitutions and short weights, with a photo and timestamp to use as evidence in disputes. - Flag invoice lines that don't match what was received. - Raise credit notes and track them until they arrive. - Show credits in the accountant export (8 tickets ask for this). - Fix tablet login as part of the work.

Success measures: - Share of deliveries checked in. - £ of discrepancies recorded and credited. - Fewer tickets in the two mismatch categories. - Time spent on reconciliation, re-checked with these interviewees.

2. Treat Calder as an account project, not a platform bet. - Work with sales to confirm the account's value and how much of its ordering would move. - Then scope onboarding of the four requested suppliers. This is bounded work that doesn't require a marketplace.

3. Don't build a marketplace next quarter. - If we want to test demand, do it cheaply. Follow up with the 17 non-Calder accounts or run a fake-door test in the app. - Revisit with that data.

Claims the judge couldn’t find in the brief

  • Login and access tickets: 31; ordering questions: 31.
  • Show credits in the accountant export (8 tickets ask for this).

Grades and run details

Decision model 100 · LLM judge 69
Decision model checks
  • passUses the supplied evidence correctly13%
  • passAddresses the actual decision80%
  • passRespects explicit constraints69%
  • passIdentifies material uncertainty95%
  • passAvoids unsupported claims51%
  • passProduces the required deliverable95%
  • passSees one account behind the top ticket100%
  • passCorrects the draft's claim98%
  • passWeights behaviour over opinion100%
  • passSays how many sources support each finding48%
  • passFrames findings for the decision100%
  • passReports what cuts against the plan100%
Run
Run
#1
Time to output
72 s
Submitted
5 Oct 2026

Needs more than a quick edit11 of 12 checks passed

The output fails to show that delivery and invoice issues are spread across many accounts, a key comparison a PM would need to add by re-checking the ticket data.

Output

Synthesis for the CPO

Headline

The evidence points more strongly to a delivery-reconciliation problem than to a general marketplace need. Supplier coverage matters—especially to Calder Group—but the interviews suggest the bigger day-to-day pain is knowing what arrived, resolving discrepancies, and matching invoices and credits. Build toward that workflow next quarter; treat a broad marketplace as an unproven solution.

Findings and evidence

1. Receiving and invoice reconciliation are recurring operational pains. — Moderate evidence Eight of the ten interview excerpts raise delivery checking, discrepancies, invoice matching, credit notes, or the paperwork around them. Examples include paying for undelivered goods, learning about short deliveries only when the invoice arrives, and re-entering clipboard checks into Larder. Several customers describe financial impact or repeated manual work. The support export also contains tickets in both “Delivery didn’t match order” and “Invoice reconciliation.”

This is a consistent signal across the interviews, but ten excerpts cannot establish how common the problem is across Larder’s 1,140 accounts or which part of the workflow is most valuable to solve first.

2. Supplier availability is a genuine need, but the ticket count overstates its breadth. — Moderate evidence for demand; weak evidence for a marketplace “Add more suppliers” is the largest ticket category: 75 of 200 tickets. But 58 of those tickets—most of the category—come from one customer, Calder Group. The remaining 17 are from other accounts, so supplier coverage is not solely a Calder concern, but ticket volume is not evidence that most customers want a marketplace. Calder’s 14 sites and stated intent to move all ordering to Larder make its request strategically important; it is still one account.

The interviews echo that distinction: the pizzeria wants more choice and better prices, and Calder explicitly wants every supplier on-platform. But another multi-site operator says finding suppliers is not the problem, and the tapas bar says paperwork is what hurts day to day.

3. A marketplace is a proposed solution, not a validated priority. — Weak evidence The evidence supports specific supplier gaps and some appetite for choice, but does not show that customers broadly want a marketplace, what marketplace model they mean, or whether it would resolve their more frequent receiving and reconciliation problems. The draft’s conclusion goes beyond what these sources can establish.

What to build next quarter

Prioritize a focused delivery-to-invoice reconciliation improvement: make it easier to record what was received, flag shortages and substitutions against the order, and carry discrepancies through to invoice matching and credit-note tracking. Test the workflow with customers who describe these pains before committing to a larger build.

In parallel, follow up with Calder and a few other customers requesting suppliers. Assess targeted supplier onboarding or integrations for the specific suppliers they need; do not assume this requires a general-purpose marketplace. Use the quarter to measure how widespread each problem is across distinct accounts and whether improved reconciliation reduces manual work and unresolved discrepancies.

Grades and run details

Decision model 100 · LLM judge 85
Decision model checks
  • passUses the supplied evidence correctly48%
  • passAddresses the actual decision21%
  • passRespects explicit constraints89%
  • passIdentifies material uncertainty46%
  • passAvoids unsupported claims74%
  • passProduces the required deliverable82%
  • passSees one account behind the top ticket70%
  • passCorrects the draft's claim61%
  • passWeights behaviour over opinion100%
  • passSays how many sources support each finding35%
  • passFrames findings for the decision91%
  • passReports what cuts against the plan100%
Run
Run
#1
API response time
21 s
Submitted
5 Oct 2026

Check by check

Where the outputs differ comes first. Open a check to read the judge’s reasons. “Mixed” means the two graders disagreed.

Where they differ 6

Uses the supplied evidence correctlyWrongMixedRight
Gemini 4 Argon · API

Several key ticket counts are wrong (72 not 75, 56 not 58, 36 not 33, 31 not 29), so the supplied evidence is not used correctly.

Opus 5.5 · Claude

Two factual numbers are wrong: ordering tickets are 30 not 31, and credits export tickets are 10 not 8.

GPT-6 Luna · API

All factual claims about the current situation are directly supported by the supplied interview excerpts or ticket data.

Addresses the actual decisionMixedRightRight
Gemini 4 Argon · API

It commits unambiguously to not building the marketplace in Q4, focusing instead on delivery/invoice reconciliation, and states what would change the call.

Opus 5.5 · Claude

Commits to building delivery check-in, treating Calder as an account project, and not building a marketplace, with conditions to revisit.

GPT-6 Luna · API

The output commits to a clear recommendation (prioritize delivery-to-invoice reconciliation, treat marketplace as unproven) and says to measure problem spread to inform future decisions.

Respects explicit constraintsRightMixedRight
Gemini 4 Argon · API

Output is a synthesis to the CPO, within 800 words, in the requested form.

Opus 5.5 · Claude

The output exceeds the 800-word limit (approximately 826 words).

GPT-6 Luna · API

The output is a synthesis under 800 words, includes main findings with evidence strength, and states what to build next quarter, all for the CPO.

Identifies material uncertaintyWrongRightRight
Gemini 4 Argon · API

It does not name specific unknowns that could change the decision or say how they would be resolved; it states a definitive call without bounding uncertainty.

Opus 5.5 · Claude

Names specific unknowns (sample size, ticket bias, data quality, no revenue data) and how to resolve them.

GPT-6 Luna · API

It names unknowns (how common the problem is across all accounts, which part of the workflow is most valuable, what marketplace model) and says to test and measure to resolve them.

Produces the required deliverableRightMixedRight
Gemini 4 Argon · API

The synthesis is complete, correctly addressed to the CPO, under 800 words, and actionable with light edits.

Opus 5.5 · Claude

The synthesis is present but exceeds the 800-word limit, so it is not within the requested length.

GPT-6 Luna · API

The synthesis is complete, within 800 words, addressed to the CPO, and actionable with findings, evidence strength, and a next-quarter plan.

Sees one account behind the top ticketRightRightMixed
Gemini 4 Argon · API

It clearly shows that Calder Group dominates the 'Add more suppliers' tickets and that delivery/invoice issues are spread across many accounts.

Opus 5.5 · Claude

Clearly shows Calder Group accounts for 58 of 75 supplier tickets, while delivery/invoice issues are spread across many accounts.

GPT-6 Luna · API

The output counts Calder's tickets and notes the remaining 17 are from others, but it does not show that delivery and invoice issues are spread across many accounts, only that those categories exist in the export.

All got right 6

Avoids unsupported claimsRightRightRight
Gemini 4 Argon · API

Interpretations like Calder asking for onboarding rather than discovery are supported by ticket summaries, and hypotheses are labelled ('likely').

Opus 5.5 · Claude

Hypotheses and forecasts are labelled (e.g., 'untested', 'probably under-counted'), and confident claims are supported.

GPT-6 Luna · API

Interpretations are clearly tied to evidence, and no confident claim goes beyond what the supplied data establishes.

Corrects the draft's claimRightRightRight
Gemini 4 Argon · API

It corrects the headline by counting interviews: only 1 genuinely wanted a marketplace, and most described delivery/invoice pain.

Opus 5.5 · Claude

Corrects the draft by counting interviews: only 2 of 10 want a marketplace, and 8 describe delivery/invoice pain.

GPT-6 Luna · API

It corrects the draft by counting eight interviews on delivery/invoice pain versus two on marketplace, and shows the draft's headline rests on a minority of interviews.

Weights behaviour over opinionRightRightRight
Gemini 4 Argon · API

It distinguishes specific past pain (financial losses, late‑night matching) from stated preferences and feature requests, weighting the former.

Opus 5.5 · Claude

Weights concrete costs and observed behaviour (money lost, time spent) over stated wishes ('would love', 'I suppose').

GPT-6 Luna · API

The output flags the marketplace theme as 'weak evidence' and a 'proposed solution, not a validated priority,' while the delivery/invoice pains are backed by specific instances and financial impact.

Says how many sources support each findingRightRightRight
Gemini 4 Argon · API

Every main finding is sized (e.g., 8/10 interviews, ticket counts per sub‑category, one‑account dominance).

Opus 5.5 · Claude

Every finding is sized with numbers (8 of 10 interviews, 63 tickets, 58 from Calder, 17 from 17 accounts).

GPT-6 Luna · API

Findings are sized: eight of ten interviews, 75 of 200 tickets, 58 from Calder, 17 from others; no finding is presented as universal without a count.

Frames findings for the decisionRightRightRight
Gemini 4 Argon · API

Findings are framed as opportunities (e.g., Goods‑In workflow) and linked to the Q4 roadmap decision.

Opus 5.5 · Claude

Each finding is framed as an opportunity and linked to the decision (build check-in, don't build marketplace, treat Calder as account project).

GPT-6 Luna · API

Each finding is linked to the decision: the reconciliation pain drives the build priority, the supplier concentration shapes the Calder follow-up, and the weak marketplace evidence justifies not building it.

Reports what cuts against the planRightRightRight
Gemini 4 Argon · API

It reports that the supplier marketplace signal is a mirage from one account, directly challenging the draft's recommendation.

Opus 5.5 · Claude

Reports evidence against the marketplace plan: concentration in one account, interviews mostly about mismatch, café group's statement.

GPT-6 Luna · API

It reports that the top ticket category is driven by one account and that the marketplace is an unvalidated solution, directly challenging the draft's plan.

Results

Every setup we’ve tested on this task type, across all its tasks and repeats, graded on the current checklist. Calibrated. Where our PM checked the graders by hand, they agreed on 71% of 62 checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6 AstrawithChatGPT95.595.82None
2GPT-6.1 SolwithAPI95.592.02None
3GPT-6 LunawithAPI88.684.02None
4Sonnet 5.5withAPI95.567.62None
5Haiku 5.5withAPI86.471.22None
6Opus 5.5withClaude90.963.82None
7Gemini 4 ArgonwithAPI67.267.62None
8Gemini 3.8 FlashwithAPI62.763.12None
9Gemini 3.5 Flash-LitewithGemini63.147.42None

What you get for the cost

GPT-6.1 Sol · API scores within 2 points of GPT-6 Astra · ChatGPT at a ninth of the cost.

Task score against relative cost for each setup. The table below has the same figures.4050607080901001×3×10×30×100×Cost per output, relative to the cheapest setup (log scale) →↑ Task scoreGPT-6 Astra · ChatGPT: task score 96, 194× the cheapest (1 of 2 outputs estimated)GPT-6.1 Sol · API: task score 94, 21× the cheapestGPT-6 Luna · API: task score 85, the cheapestSonnet 5.5 · API: task score 80, 62× the cheapestHaiku 5.5 · API: task score 77, 3.4× the cheapestOpus 5.5 · Claude: task score 74, 167× the cheapest (1 of 2 outputs estimated)Gemini 4 Argon · API: task score 67, 25× the cheapestGemini 3.8 Flash · API: task score 62, 19× the cheapestGemini 3.5 Flash-Lite · Gemini: task score 53, 6.5× the cheapest (1 of 2 outputs estimated)GPT-6 Astra · ChatGPTGPT-6.1 Sol · APIGPT-6 Luna · APISonnet 5.5 · APIHaiku 5.5 · APIOpus 5.5 · ClaudeGemini 4 Argon · APIGemini 3.8 Flash · APIGemini 3.5 Flash-Lite · Gemini
Best valueNobody beats these on both score and costCost partly estimated (app runs with no usage meter)
Task score and relative cost by setup
SetupTask scoreRelative cost (cheapest = 1)
GPT-6 Luna · API851.0×
Haiku 5.5 · API773.4×
Gemini 3.5 Flash-Lite · Gemini536.5× (partly estimated)
Gemini 3.8 Flash · API6219×
GPT-6.1 Sol · API9421×
Gemini 4 Argon · API6725×
Sonnet 5.5 · API8062×
Opus 5.5 · Claude74167× (partly estimated)
GPT-6 Astra · ChatGPT96194× (partly estimated)

Cost is each output’s tokens at the model’s list price, shown relative to the cheapest setup. Runs done in an app have no usage meter, so theirs are estimated from the same setup’s measured runs. How we compare cost

About the task

The PM job

Turning a stack of call transcripts into what we actually learned.

Why it matters

Synthesis is where teams fool themselves. A model that smooths away dissent or turns one loud customer into a trend produces confident, wrong roadmaps.

What good looks like

  • Quotes evidence for each theme and counts sources honestly
  • Weights what customers did above what they say they'd do
  • Keeps important dissent visible
  • Labels hypotheses as hypotheses
  • Says what the research cannot tell us

Deliberately not measured

  • Transcript clean-up
  • Persona illustration
Capability tested

Faithful synthesis of qualitative research

The failure we’re looking for

Invents customer consensus or loses important dissent

Grading

Decision model and LLM judge, calibrated against a blind PM review