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 16 graded outputs by 8 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 finding52% 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 correctly59% 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. Avoids unsupported claims63% pass
    The claim 'Ordering (31 tickets)' is presented as an established fact, but the evidence shows 30 Ordering tickets, so it is an unsupported confident claim.
    Sonnet 5.5 · 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 · v1.0

The outputs

8 setups answered this brief.

Usable with a quick edit10 of 12 checks passed

The output is well-structured and evidence-based; a quick edit to add a condition for changing the decision would make it fully usable without re-checking evidence or redoing reasoning.

Output

To: Chief Product Officer From: Product Manager, Ordering Subject: Q4 Product Strategy Synthesis: Debunking the Marketplace Hypothesis

---

Executive Summary

The recommendation in the draft synthesis—to build a supplier marketplace—is based on a misinterpretation of support data and interview signals.

A closer analysis reveals that demand for a discovery marketplace is weak. Instead, the data reveals a critical, systemic gap in our core product: delivery verification and invoice reconciliation. Restaurants are losing hundreds of pounds monthly and spending hours manually reconciling short deliveries, substitutions, and incorrect invoices.

Next quarter, we should not build a marketplace. We should build an end-to-end delivery check-in and automated reconciliation workflow.

---

Key Findings & Evidence Strength

#### 1. Demand for a "Supplier Marketplace" is an artifact of a single customer * Finding: Customers are not asking for supplier discovery. What looks like our highest-volume ticket category is heavily skewed by one account, and the few requests for "more suppliers" reflect a desire to bring existing suppliers onto Larder, not discover new ones. * Evidence Strength: High (Quantitative & Qualitative) * Ticket Skew: "Add more suppliers" represents 75 of 200 tickets (37.5%). However, 58 of those 75 tickets (77%) were submitted by a single customer: Calder Group (14 sites), repeatedly requesting onboarding for four specific existing vendors (Brakes, meat, dairy, wine). * Only 17 other accounts (out of 1,140 total accounts, ~1.5%) submitted requests in this category. * Interviews: Only 1 of 10 respondents (Pizzeria) actively asked for a marketplace for choice/pricing. In contrast, multi-site operators explicitly stated: "Finding suppliers isn't the problem. Knowing what actually arrived is" (Café group) and "I'd use a marketplace if it existed, I suppose. But day to day, what kills me is the paperwork" (Tapas bar). Even Calder Group’s interview makes clear their goal is centralizing their existing supply base onto one system.

#### 2. The critical operational pain is post-order delivery check-in and invoice reconciliation * Finding: Restaurants face daily operational chaos and margin loss between order placement and invoice payment. Deliveries arrive short or substituted, chefs lack a frictionless way to record discrepancies, and finance teams waste days chasing credit notes. * Evidence Strength: Very High (Triangulated across support & interviews) * Support Tickets: Combined, Delivery didn't match order (34 tickets) and Invoice reconciliation (29 tickets) account for 63 tickets across ~60 distinct accounts—nearly 4x the unique account breadth of general supplier requests. Core sub-issues include: "Invoice includes items not delivered", "Short delivery not shown in app", "Credit note missing", and "Export for accountant doesn't show credits". * Interviews: 8 out of 10 interviewees cited this as their primary operational bottleneck. The impact is acute and financial: * Direct losses: Gastropub lost ~£600 last month paying for unchecked deliveries; Steakhouse pays unmatched invoices due to lack of time. * Wasted admin: Hotel finance team spends days chasing credit notes; Bistro owner spends Sundays matching notes with highlighters; Noodle bar re-types paper notes into the app. * Dispute friction: Bakery notes supplier disputes are "my word against theirs."

---

What This Means for Next Quarter

Building a two-sided marketplace would require massive operational lift (supplier acquisition, vetting, liquidity) while failing to solve the core retention and efficiency problems of our existing 1,140 accounts.

Instead, we must complete our core product loop: Order $\rightarrow$ Receive $\rightarrow$ Reconcile.

#### Next Quarter Roadmap Priorities

1. P0: Mobile/Tablet Delivery Check-In Flow * Problem solved: Staff currently check goods on paper clipboards or sign blindly. * Feature: Fast, touch-friendly check-in workflow at the delivery door. Staff can mark line items as accepted, short, rejected, or substituted, with photo capture for damaged goods.

2. P1: Discrepancy & Credit Note Tracking * Problem solved: Finance teams manually chase suppliers for credit notes; owners lack proof of short deliveries. * Feature: Automatically generate and email a discrepancy report to the supplier upon delivery check-in, creating an audit trail and tracking outstanding credit notes until resolved.

3. P2: 3-Way Invoice Reconciliation & Accounting Export * Problem solved: Invoices don't match orders, and exports fail to reflect credits. * Feature: Match PO $\rightarrow$ Delivery Note $\rightarrow$ Final Invoice. Highlight variances automatically and update accounting exports to include verified credits.

4. Fast-Follow / Targeted: Self-Serve Supplier Onboarding Portal * Problem solved: Satisfies Calder Group and similar multi-site accounts wanting their off-platform suppliers integrated. * Feature: A lightweight "Invite Supplier" flow where restaurants send an onboarding link directly to their existing vendors to set up a digital catalog.

Grades and run details

Decision model 88 · LLM judge 85
Decision model checks
  • passUses the supplied evidence correctly18%
  • passAddresses the actual decision44%
  • passRespects explicit constraints46%
  • failIdentifies material uncertainty93%
  • partialAvoids unsupported claims57%
  • passProduces the required deliverable94%
  • passSees one account behind the top ticket99%
  • passCorrects the draft's claim88%
  • passWeights behaviour over opinion100%
  • passSays how many sources support each finding54%
  • passFrames findings for the decision100%
  • passReports what cuts against the plan100%
Run
Run
#1
API response time
36 s
Submitted
6 Oct 2026

Usable with a quick edit12 of 12 checks passed

The synthesis is clear, evidence-based, and directly usable by a product manager with at most light editing for emphasis or wording.

Output

Next-quarter synthesis: prioritise delivery-to-invoice accuracy, not a marketplace

Recommendation: Build and validate a focused workflow for recording what arrived, flagging discrepancies and resolving invoice/credit issues. Continue targeted supplier onboarding, but do not commit next quarter to a broad marketplace. The evidence supports operational accuracy more broadly than supplier discovery.

Main findings and evidence strength

1. Supplier requests lead ticket volume—but are heavily concentrated in one account

The export contains 200 tickets:

CategoryTicketsDistinct accounts
------:---:
Add more suppliers7518
Delivery didn’t match order3434
Invoice reconciliation2928
Ordering3129
Login and access3131

Account counts are deduplicated within categories; they should not be summed across categories.

Calder Group generated 58 of the 75 supplier tickets (77%), repeatedly requesting onboarding for four named suppliers or supplier types. The other 17 supplier tickets came from 17 accounts.

Evidence strength: Strong evidence of a concentrated supplier-coverage need; weak evidence that a marketplace is the right solution. Ticket volume measures support demand, not independent customer votes. Repeated requests may indicate an important unresolved problem, but not broad market demand.

Calder’s 14 sites and stated willingness to move all ordering to Larder warrant commercial follow-up. However, this is a conditional intention—not a commitment—and we have no revenue or order-volume evidence to size the opportunity. Requests to add existing suppliers must also be distinguished from discovering new ones.

2. Delivery and invoice problems affect a broader set of accounts

Together, delivery discrepancies and invoice reconciliation account for 63 tickets from 62 distinct accounts, compared with supplier requests from 18 accounts.

The interviews reinforce this pattern. Eight of ten excerpts describe delivery, reconciliation or paperwork pain, although the tapas bar’s comment is less specific. Examples include:

  • Discovering a short delivery only when the invoice arrives.
  • Paying for undelivered goods; one gastropub reported approximately £600 in one month.
  • Chasing credit notes weekly.
  • Having no shared evidence when suppliers dispute shortages.
  • Recording deliveries on paper, then re-entering them in Larder.

The café group explicitly says finding suppliers is not the problem; knowing what arrived is.

Evidence strength: Strong convergence across these two sources that this is a meaningful problem spanning multiple restaurant formats. Evidence for its prevalence across all 1,140 accounts, typical financial impact or the best solution remains limited. The £600 figure is one customer’s report, not an estimate we can extrapolate.

3. Marketplace interest exists, but “most customers want one” is unsupported

The pizzeria strongly wants more choice and better prices. Calder explicitly mentions a marketplace, but frames its need as getting every supplier onto the platform. The tapas bar expresses tentative interest, then prioritises paperwork.

Evidence strength: Directional evidence for supplier expansion, but weak evidence for broad marketplace demand. Only Calder explicitly advocates a marketplace; the pizzeria’s desired outcome could potentially be served in other ways. These excerpts do not establish expected adoption, switching behaviour or willingness to pay.

What we should build next quarter

Prioritise a narrow delivery-to-invoice pilot, rather than attempting full reconciliation automation immediately:

  1. Capture actual receipt against an order: quantities received, shortages and substitutions, without duplicate entry.
  2. Carry discrepancies forward: make them visible when checking invoices rather than leaving users to reconstruct events.
  3. Track resolution: preserve a discrepancy record and show outstanding supplier responses or credits.

Start with observing receiving and invoice-checking in a varied set of independent restaurants. In particular, test whether busy staff will actually record discrepancies: the unchecked delivery-note example suggests software alone may not solve the problem.

Measure receiving-workflow adoption, duplicate entry, reconciliation time, discrepancies identified before payment and time to resolve credits. Establish baselines before setting numerical success targets; use pilot results to decide how far to expand.

Keep supplier coverage as a separate, bounded workstream. Clarify Calder’s required suppliers, onboarding feasibility and commercial commitment. Test targeted onboarding before assuming we need marketplace discovery, comparison and transaction capabilities.

Confidence and limits: This is a prioritisation recommendation, not a proven business case. Ten interview excerpts and self-selected support tickets cannot establish population prevalence. Some accounts also have inconsistent site counts in the export, so site-weighted conclusions would be unreliable. Validate the proposed workflow and its value before a broad rollout.

Grades and run details

Decision model 100 · LLM judge 100
Decision model checks
  • passUses the supplied evidence correctly47%
  • passAddresses the actual decision88%
  • passRespects explicit constraints56%
  • passIdentifies material uncertainty86%
  • passAvoids unsupported claims84%
  • passProduces the required deliverable88%
  • passSees one account behind the top ticket100%
  • passCorrects the draft's claim68%
  • passWeights behaviour over opinion100%
  • passSays how many sources support each finding75%
  • passFrames findings for the decision98%
  • passReports what cuts against the plan100%
Run
Run
#1
Time to output
48 s
Submitted
5 Oct 2026

Usable with a quick edit11 of 12 checks passed

The only flaw is an off-by-one error in two ticket counts (31 vs 30), which can be fixed in a quick edit without re-checking the core reasoning or evidence.

Output

Synthesis: what customers are telling us

Headline: Our biggest problem is that customers can't easily check what arrived against what they were invoiced. A supplier marketplace is a real but narrow request, concentrated in one large account. I don't recommend building it next quarter.

Sources: 10 owner and manager interviews (September) and 200 support tickets from last quarter (LT-5000 to LT-5199).

What changed from the draft: "Top category by far" holds only because of one account. "Most customers we spoke to want a marketplace" is 2 of 10, and one of those is hedged. The recommendation doesn't follow from the evidence.

Findings

1. Checking deliveries and reconciling invoices is the main pain. Evidence: strong. - 63 of 200 tickets (32%) are "delivery didn't match order" or "invoice reconciliation." The most common issues are short deliveries not shown in the app, missing credit notes, invoices including items not delivered, invoices that can't be matched to orders, and accountant exports that omit credits. - These tickets come from many different accounts, not one. - 8 of 10 interviews describe this problem. A gastropub lost about £600 in a month paying for unchecked deliveries. A hotel finance team chases credit notes weekly. A bakery is in a "my word against theirs" dispute with a supplier. A noodle bar re-types clipboard checks into the app. - Caveats: ticket categories are assigned by support staff, and 10 interviews is a small sample.

2. Supplier demand is concentrated in one account. Evidence: strong for Calder Group, weak as a base-wide signal. - "Add more suppliers" is the largest ticket category (75 tickets), but 58 come from Calder Group (14 sites). Outside Calder it is 17 tickets, fewer than delivery issues (34) or ordering issues (31). - Calder's requests repeat four named suppliers: Brakes (15 tickets), meat (15), dairy (14), and a wine merchant (14). These are four specific onboarding requests, not a general need for a marketplace. - Outside Calder, requests are vague ("any plans for more suppliers?", "would like more produce suppliers"), spread across accounts. - In interviews, 2 of 10 want more suppliers: the pizzeria and Calder. A tapas bar says it would use a marketplace "I suppose," but says the paperwork is what hurts day to day. A café group says finding suppliers "isn't the problem. Knowing what actually arrived is."

3. Calder Group is a high-value but conditional signal. Evidence: one account, one conversation. - Calder says: "If you had a marketplace, we'd move all our ordering to you." That is the strongest commercial statement in the material, but it is one customer's conditional view, and it depends on four specific suppliers. - We don't know from these materials how much ordering volume or revenue Calder represents.

4. Ordering and login tickets are routine. Evidence: moderate, low priority. - Ordering (31) and login (31) tickets are mostly specific fixes: cut-off times, duplicate orders, delivery-day changes, tablet login problems, password resets, adding users. None of these came up in interviews.

What this means for next quarter

Build: a receiving and invoice-matching workflow. This addresses the largest and most widely shared problem. - Let staff record received quantities at delivery, with shorts and substitutions flagged in the app. This covers the short-delivery tickets and "how do I record a substitution?" - Automatically match invoices to deliveries and orders, and flag differences before payment. This covers "can't match," "total differs," and "includes items not delivered." - Show credit notes against deliveries and in the accountant export. Fixing the export that omits credits could ship early as a quick win. - Measures: the 63-ticket category volume, and credit value recovered per account, which we can ask customers to report.

Don't build the marketplace next quarter. Instead: - Talk to Calder Group about their ordering volume and what they'd need to move everything to us. Check whether onboarding Brakes, their meat supplier, dairy, and the wine merchant is feasible as partnership or integration work. That is a much smaller decision than building a platform. - Add a "request a supplier" flow so demand from the wider base is captured in a structured way. Then we can see whether requests extend beyond Calder. - Triage the ordering and login tickets into the backlog as small fixes.

What would change my view

  • A clear rise in supplier requests from accounts other than Calder.
  • Around 20 more interviews, across account sizes and including some churned customers, where supplier access is named as the blocker.
  • Evidence that Calder's ordering value justifies the integrations, or that a marketplace would win or retain several accounts of Calder's scale.

Until then, the evidence supports fixing receiving and reconciliation first, and treating the supplier question as a targeted account decision rather than a platform bet.

Claims the judge couldn’t find in the brief

  • Outside Calder, supplier requests are 17 tickets, fewer than delivery issues (34) or ordering issues (31).
  • Ordering (31) and login (31) tickets are routine fixes; none appeared in interviews.

Grades and run details

Decision model 100 · LLM judge 92
Decision model checks
  • passUses the supplied evidence correctly51%
  • passAddresses the actual decision100%
  • passRespects explicit constraints64%
  • passIdentifies material uncertainty100%
  • passAvoids unsupported claims69%
  • passProduces the required deliverable98%
  • passSees one account behind the top ticket100%
  • passCorrects the draft's claim100%
  • passWeights behaviour over opinion100%
  • passSays how many sources support each finding82%
  • passFrames findings for the decision97%
  • passReports what cuts against the plan100%
Run
Run
#1
API response time
34 s
Submitted
8 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 3

Uses the supplied evidence correctlyRightRightMixed
Gemini 3.8 Flash · API

All factual claims about the current situation are directly supported by the supplied context or arithmetic from it.

GPT-6 Astra · ChatGPT

All claims about the current situation are directly supported by the supplied context or derived by correct arithmetic.

Haiku 5.5 · API

The ticket counts for ordering and login are both stated as 31, but the CSV contains 30 of each.

Addresses the actual decisionMixedRightRight
Gemini 3.8 Flash · API

The output commits to a clear recommendation but does not state what result or condition would change that decision.

GPT-6 Astra · ChatGPT

The output commits to a clear recommendation (prioritise delivery-to-invoice accuracy, not a marketplace) and states that validation results would change the call.

Haiku 5.5 · API

It commits early to not building a marketplace next quarter, says what to do instead, and names conditions that would change the call.

Identifies material uncertaintyWrongRightRight
Gemini 3.8 Flash · API

The output does not name specific unknowns that could change the decision or say how they would be resolved.

GPT-6 Astra · ChatGPT

It names specific unknowns (revenue impact, adoption, prevalence across all accounts) and says how to resolve them through pilot and measurement.

Haiku 5.5 · API

It names unknowns (Calder's volume/revenue, small interview sample, support staff categorisation) and says what further evidence would change the recommendation.

All got right 9

Respects explicit constraintsRightRightRight
Gemini 3.8 Flash · API

The output is a synthesis within 800 words, addressed to the CPO, covering main findings, evidence strength, and next-quarter implications.

GPT-6 Astra · ChatGPT

The output is a synthesis under 800 words, includes main findings with evidence strength, and says what to build next quarter, as requested.

Haiku 5.5 · API

The output is a synthesis for the CPO, with findings, evidence strength, and next-quarter actions, and appears within 800 words.

Avoids unsupported claimsRightRightRight
Gemini 3.8 Flash · API

Interpretations are clearly tied to evidence, and no confident claims go beyond what the evidence establishes.

GPT-6 Astra · ChatGPT

Interpretations and forecasts are clearly labelled as such, and confident claims are limited to what the evidence establishes.

Haiku 5.5 · API

The miscount is a factual error rather than presenting an interpretation as fact; otherwise, all confident claims are grounded in the supplied sources.

Produces the required deliverableRightRightRight
Gemini 3.8 Flash · API

The memo is complete, correctly formatted for the CPO, within the word limit, and actionable with light edits.

GPT-6 Astra · ChatGPT

The synthesis is complete, in the right form for the CPO, within the word limit, and actionable without major gaps.

Haiku 5.5 · API

A usable synthesis for the CPO with the required sections, within length, that can be acted upon with light edits.

Sees one account behind the top ticketRightRightRight
Gemini 3.8 Flash · API

The synthesis explicitly counts Calder Group's tickets (58 of 75) and contrasts the spread of delivery/invoice issues across many accounts.

GPT-6 Astra · ChatGPT

It counts tickets by account, shows Calder Group is 58 of 75 supplier tickets, and contrasts with delivery/invoice issues spread across many accounts.

Haiku 5.5 · API

It explicitly identifies that 58 of 75 supplier tickets are from Calder Group and contrasts this with the spread of delivery/invoice issues.

Corrects the draft's claimRightRightRight
Gemini 3.8 Flash · API

It corrects the draft by showing only two interviews mention a marketplace and that most interviews describe delivery/invoice pain.

GPT-6 Astra · ChatGPT

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.

Haiku 5.5 · API

It corrects the draft by noting that 2 of 10 interviews wanted a marketplace and that one of those is hedged, while delivery/invoice pain dominates.

Weights behaviour over opinionRightRightRight
Gemini 3.8 Flash · API

The output weights specific past instances (e.g., £600 loss, Sunday reconciliation) over stated wishes, and flags marketplace demand as weak.

GPT-6 Astra · ChatGPT

It distinguishes specific past instances and workarounds (delivery/invoice pain) from stated preferences (marketplace wishes) and weights the former more heavily.

Haiku 5.5 · API

It distinguishes actual behaviour and specific past pain from stated wishes, labelling the supplier signal as weaker and the delivery/invoice pain as strong.

Says how many sources support each findingRightRightRight
Gemini 3.8 Flash · API

Every finding is sized with numbers (8/10 interviewees, 58/75 tickets, 63 tickets across ~60 accounts).

GPT-6 Astra · ChatGPT

Every finding is sized with ticket counts, account counts, or interview counts, and nothing is presented as universal without support.

Haiku 5.5 · API

Every finding is sized with ticket counts or number of interviews; none presents a theme as universal without numbers.

Frames findings for the decisionRightRightRight
Gemini 3.8 Flash · API

Findings are framed as opportunities (delivery check-in, reconciliation) and directly linked to the decision not to build a marketplace.

GPT-6 Astra · ChatGPT

Each main finding is framed as an opportunity (delivery-to-invoice pain, concentrated supplier need) and linked directly to what to build next quarter.

Haiku 5.5 · API

Each finding is linked directly to what it means for the next-quarter plan, turning evidence into actionable opportunities.

Reports what cuts against the planRightRightRight
Gemini 3.8 Flash · API

The output reports evidence that cuts against the marketplace hypothesis, showing it is driven by one account and not the broader user base.

GPT-6 Astra · ChatGPT

It reports that supplier requests are concentrated in one account and that the café group says finding suppliers isn't the problem, both cutting against the marketplace plan.

Haiku 5.5 · API

It challenges the draft's marketplace plan and lists conditions under which the recommendation would change.

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 74% of 62 checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6 AstrawithChatGPT95.595.82None
2GPT-6.1 SolwithAPI97.792.02None
3GPT-6 LunawithAPI93.284.02None
4Sonnet 5.5withAPI95.567.62None
5Haiku 5.5withAPI86.471.22None
6Opus 5.5withClaude86.463.82None
7Gemini 3.8 FlashwithAPI71.063.12None
8Gemini 3.5 Flash-LitewithGemini69.347.42None

What you get for the cost

GPT-6.1 Sol · API scores within a point 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.50607080901001×3×10×30×100×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 95, 21× the cheapestGPT-6 Luna · API: task score 87, 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 72, 167× the cheapest (1 of 2 outputs estimated)Gemini 3.8 Flash · API: task score 66, 19× the cheapestGemini 3.5 Flash-Lite · Gemini: task score 56, 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 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 · API871.0×
Haiku 5.5 · API773.4×
Gemini 3.5 Flash-Lite · Gemini566.5× (partly estimated)
Gemini 3.8 Flash · API6619×
GPT-6.1 Sol · API9521×
Sonnet 5.5 · API8062×
Opus 5.5 · Claude72167× (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