Tasks / Growth

Write a GTM plan

Can the model pick the customers who value the difference most, position against what they do today, and build a go-to-market that lasts beyond launch day?

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

Positioned against the real alternative100% pass
It names the real alternatives (accountant-run payroll with mistakes, owner spreadsheet with risk) and ties the message to confidence and accuracy that those alternatives lack.
GPT-6 Astra · ChatGPT · Payroll, for the people who fear it
Protects the self-serve engine100% pass
Uses the 70% free-to-paid stat and product-qualified deal data to argue against capping the free plan, and keeps self-serve open.
GPT-6 Astra · ChatGPT · Enterprise, without killing the engine
Does the capacity sums100% pass
Works out deals needed (69-80), qualified opportunities (134), and AE-years (8) with checkable arithmetic.
GPT-6 Astra · ChatGPT · Enterprise, without killing the engine

Where it slips

Avoids unsupported claims47% pass
Presents the incorrect 'five of six no decision' figure as established fact, which is an unsupported claim about the current situation.
Opus 5.5 · Claude · Enterprise, without killing the engine
Uses the supplied evidence correctly52% pass
The claim 'Five of our six losses on existing-user deals ended in no decision' is not supported by the data (4 no-decision, 2 Miro), so a statement about the current situation is invented.
Opus 5.5 · Claude · Enterprise, without killing the engine
Identifies material uncertainty73% pass
The plan does not name specific unknowns that could change the decision, nor does it say how they would be resolved or what would trigger a pivot.
Gemini 3.8 Flash · API · Payroll, for the people who fear it

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.

Positioned against the real alternative
Does the plan name what the target customer does today without this product (including the status quo or doing nothing), and tie the message to the value that alternative lacks?
Passes when Names the real alternative and the differentiated value against it, and picks the segment that cares most about that value.
April DunfordLenny’s Podcast
Sells against a pain the buyer feels
Is the core message built on a pain or risk the target buyer already feels, with evidence from the material, rather than on generic upside like saving time?
Passes when Leads with the pain the evidence shows buyers care about, and uses that evidence to choose the message.
Jeanne DeWitt GrosserLenny’s Podcast
Diagnoses message or channel first
Does the plan work out whether the problem is the message (people reach it but don't convert) or the channels (the right people never see it), and spend accordingly?
Passes when Uses conversion and reach data to say which it is, and puts the effort there.
Emily KramerLenny’s Podcast
Plans beyond launch week
Does the plan say how demand keeps coming after launch, with the ongoing engine and what's measured over months, rather than treating launch day as the whole plan?
Passes when Gives the ongoing motion after launch and the measures that show whether it's working over time.
Arielle JacksonLenny’s Podcast

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 · and 3 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 VP Product at Flowboard. The board wants $5M of new enterprise ARR next year, and our CRO, Jordan Hale, has drafted the enterprise go-to-market below. The CEO has asked for your version before the board meeting: who we sell to, how the self-serve product and the sales team work together, what we say against the alternatives, the capacity it takes, and what you'd change in Jordan's plan and why. No more than 1,300 words. The files are attached.

What the model was given6 items: About Flowboard, today.md, cro_enterprise_plan.md, enterprise_deals_last_12_months.csv, large_companies_using_flowboard.csv (companies over 1,000 employees with active self-serve or free teams), enterprise_win_loss_interviews.md

About FlowboardOnline whiteboards and diagramming for teams. Free plan, self-serve paid plans, and an enterprise plan sold by a small sales team.

today.md5 lines · Download

# Today

- Self-serve: 31,000 paying teams; free plan feeds 70% of new paying teams.
- Enterprise: 46 customers, average contract $64,000.
- Sales team: 3 account executives, closing about 10 enterprise deals a year each.

cro_enterprise_plan.md8 lines · Download

# Enterprise plan (CRO, draft for the board)

Target: $5M new enterprise ARR next year.
1. Cap the free plan at 3 users (today: unlimited users, 3 boards each), so teams have to talk to us.
2. Route every team over 10 seats to sales; remove self-serve checkout above 10 seats.
3. Hire 8 account executives and 6 SDRs; SDRs work outbound lists of Fortune 2000 companies.
4. Enterprise features: SSO, SCIM, audit logs, data residency.
…

enterprise_deals_last_12_months.csv65 rows · Download

deal,source,outcome,sales_cycle_days,acv_usd,lost_to
E-200,Outbound,won,201,32700,
E-201,Product-qualified (teams already using Flowboard),won,62,97000,
E-202,Product-qualified (teams already using Flowboard),won,61,108000,
E-203,Outbound,lost,192,,Miro (company standard)
E-204,Outbound,lost,204,,Miro (company standard)
E-205,Product-qualified (teams already using Flowboard),won,77,54200,
…

large_companies_using_flowboard.csv (companies over 1,000 employees with active self-serve or free teams)company,employees,teams_using_flowboard,active_users_30d,paying_seats,viewed_security_page_30d,asked_for_sso Aldergate Bank,15000,2,18,10,yes,yes Brightfield Bank,2400,6,54,36,yes,yes Corvel Logistics,15000,5,45,22,yes,yes Dunmore Holdings,4800,3,12,4,yes,yes Eastlake Health,1200,3,30,14,yes,yes Fennick Holdings,4800,8,48,30,yes,yes Galloway Retail,9000,9,36,20,yes,no Hartwell Group,1200,4,48,21,yes,no Iverson Retail,1200,3,18,9,yes,no Juniper Retail,1200,3,24,10,yes,no Kestrel Logistics,4800,1,12,6,yes,no Linton Group,1200,2,10,4,no,no Marlowe Group,2400,2,10,7,no,no Northcote Retail,9000,2,10,6,yes,no Oakhurst Group,9000,3,36,18,yes,no Pemberton Health,1200,1,6,2,yes,no Quayside Bank,4800,2,12,4,no,no Redfern Group,15000,3,18,10,no,no Stanway Health,9000,3,30,12,no,no Thornbury Bank,15000,3,24,12,no,no Upton Bank,9000,3,33,23,yes,no Varden Bank,2400,3,12,5,no,no Whitmore Bank,4800,2,8,5,no,no Yardley Logistics,15000,2,8,4,yes,no

enterprise_win_loss_interviews.md5 lines · Download

# Enterprise win/loss interviews (12 buyers, this year)

- Won (7): every win had teams already using Flowboard. "Our designers refused to give it up." "IT approved it because 300 people were already on it."
- Lost (5): four had standardised on Miro company-wide; switching meant migrating thousands of boards. One stayed on whiteboard photos and slides.
- What buyers said Flowboard does that Miro doesn't: real-time diagramming that stays tidy, and a free plan teams can start on without IT.

What a strong answer doesThe answer key the graders mark against

Builds enterprise sales on top of the self-serve engine instead of throttling it. The deals file shows product-qualified deals (teams already using Flowboard) won 19 of 25 at an average of about $72,600 and about 63 days, against outbound winning 4 of 25 at about $36,800 and about 190 days; product-qualified wins were about $1.38M of the $1.86M won. The free plan feeds 70% of new paying teams. So capping the free plan at three users and removing self-serve checkout above ten seats would choke the pipeline the enterprise business depends on, and the interviews say IT approved Flowboard because teams were already on it. Proposes product-led sales: AEs work the large-company list (multiple teams, SSO requests, security-page visits) with SSO and admin controls as the bridge, while keeping self-serve open. Positions against Miro and the status quo, not 'visual collaboration for the enterprise': where a company has standardised on Miro, don't lead with a migration; win where teams already use Flowboard, on tidy real-time diagramming and starting without IT. Reads outbound's poor results as a message problem as well as a channel one. Works out capacity: $5M at about $72,000 is about 70 deals, far more than three AEs close (about 30 a year), so it needs AE hiring focused on product-qualified accounts rather than SDR outbound lists. Plans the ongoing motion and the measures (product-qualified accounts, conversion, cycle time) beyond the first quarter.

Critical failures (cap the score at 40)

Endorses capping the free plan at three users or removing self-serve checkout above ten seats

Task

Staff-level · v1.0

The outputs

8 setups answered this brief.

GPT-6 AstraChatGPT100Usable with a quick edit Opus 5.5Claude91Usable with a quick edit Sonnet 5.5API84Needs more work GPT-6.1 SolAPI82Needs more work GPT-6 LunaAPI80Needs more work Haiku 5.5API80Needs more work Gemini 3.8 FlashAPI71Needs more work Gemini 3.5 Flash-LiteGemini57Needs more work

GPT-6.1 SolAPI

Needs more than a quick edit10 of 13 checks passed

The output contains multiple factual errors in the deal data (product-qualified ACV, sales cycle, total won) that would require re-checking the CSV and correcting the numbers before a product manager could rely on it.

Output

Flowboard enterprise plan: turn team adoption into governed company adoption

Recommendation: Keep the $5M target, but build a product-led enterprise motion—not a cold-outbound motion powered by restrictions on self-serve. Sell first to companies where teams already depend on Flowboard, then help an internal champion bring IT and procurement along.

The target is achievable only if we generate substantially more qualified opportunities and ramp sales capacity early. Neither is proven today; the board should approve a milestone-based investment, not treat $5M as an assured forecast.

1. What the evidence says

The last 12 months of deals show markedly different economics:

Source	Won / opportunities	Win rate	Average won ACV	Average won sales cycle
---	---:	---:	---:	---:
Existing product users	19 / 25	76%	$72.6K	63 days
Inbound demo requests	6 / 15	40%	$55.1K	86 days
Outbound	4 / 25	16%	$36.8K	190 days

These are small, non-random samples—not guarantees—but the direction is compelling. Product-qualified wins are nearly twice the size of outbound wins and close in one-third the time. The interviews reinforce the pattern: all seven interviewed winners already had teams using Flowboard; four of five losses had standardized on Miro.

Our advantage is existing user commitment plus differentiated diagramming, not a generic enterprise feature list.

2. Who we sell to

Primary ICP: Large companies with multiple active Flowboard teams, recurring diagramming or visual collaboration work, and a reason to consolidate purchasing or introduce governance.

Employee count helps establish potential, but adoption and buying intent should determine priority. We should not infer a winning industry segment from this small dataset.

Prioritize accounts using three signals:

Adoption: active users, multiple teams, usage growth and repeat collaboration.
Intent: SSO requests, security-page visits and explicit procurement questions.
Commercial potential: a credible departmental rollout, sponsor and budget—not simply total employees.

Start with Brightfield Bank, Fennick Holdings, Corvel Logistics and Eastlake Health: each has multiple teams, meaningful activity, security interest and an SSO request. Aldergate Bank and Dunmore Holdings also merit immediate discovery, but their smaller active footprints make rollout potential less certain. Security-interested accounts such as Hartwell, Galloway and Upton form a second wave.

Map the buying group: a user champion, a departmental budget owner, and IT/security/procurement. Sell an initial governed deployment around demonstrated demand, with an expansion path.

The 24 listed companies are a starting queue, not sufficient pipeline. Even if all became qualified opportunities, applying the historical 76% win rate and $70K incremental ARR assumption yields only about $1.3M. We must discover many more qualified accounts across the installed base.

3. How self-serve and sales work together

Keep free adoption and self-serve checkout open. Free supplies 70% of new paying teams, and buyers explicitly value starting without IT. Capping free at three users would damage the mechanism that creates our strongest enterprise opportunities.

Likewise, crossing ten seats should trigger an optional sales-assist offer, not a purchasing barrier. Seat count alone does not establish enterprise need.

Build an account-level product-qualified motion:

Aggregate teams by company domain, with appropriate access and privacy controls.
Score sustained usage, multi-team adoption, growth and governance intent.
Offer relevant help in-product: “Bring your teams together with centralized administration and security.”
Route high-intent accounts to sales promptly; nurture lower-intent accounts while preserving checkout.
Equip AEs with an account adoption summary and a champion toolkit: rollout proposal, security documentation and an economic justification.

Sales should close the governance and purchasing gap, not force users through one. Customer success then drives onboarding, broader adoption and expansion.

Keep Jordan’s SSO, SCIM, audit logs and data-residency direction, but sequence delivery against buyer requirements. Establish a minimum enterprise-readiness package first; validate residency demand and delivery cost before committing to regions. These capabilities enable procurement; they are not our differentiation.

4. What we say against alternatives

Replace “The visual collaboration platform for the enterprise” with:

> Real-time diagramming that stays tidy—now governed for the teams already relying on it.

Against Miro: Lead with diagramming and demonstrated user preference. Where Miro is entrenched, propose an approved specialist deployment rather than company-wide replacement. Do not assume customers will migrate thousands of boards.
Against slides and whiteboard photos: Demonstrate a live, editable shared diagram for one recurring workflow. Prove improved collaboration through a bounded pilot rather than unsupported productivity claims.
Against no decision: Establish the sponsor, purchase trigger and rollout outcome early. Enterprise governance must solve a current problem, not merely add features to a pitch.

5. Revenue, pipeline and capacity

Define the board target as incremental enterprise ARR added next year, excluding self-serve ARR already paid by teams that convert. Report new logos, conversion uplift and existing-enterprise expansion separately; do not count the same revenue twice.

Historical product-qualified ACV is $72.6K, but we do not know the existing self-serve revenue attached to those wins. For planning, use $70K incremental ARR per win as an assumption to validate.

$5M requires 72 wins at $70K.
At the historical 76% product-qualified win rate, that requires approximately 95 qualified opportunities.
Target 105–110 qualified opportunities, allowing some execution cushion.
At today’s ten annual wins per fully productive AE, 72 wins require 7.2 fully productive AE-years.

Three current AEs provide approximately 30 wins. Eight new AEs averaging six wins each during their first year would add 48: 78 wins and $5.46M at the assumed incremental deal size.

Thus, Jordan’s eight AE hires can be justified—but only with early hiring, sufficient pipeline and a credible ramp. At five wins per new AE, we reach only 70 wins, or $4.9M. Smaller incremental contracts or slower qualification would widen the shortfall.

Hire in Q1 against explicit pipeline and ramp gates. Start with two SDRs focused on installed-base account development, not six running broad Fortune 2000 lists. Provide named solutions/security support and customer-success capacity for enterprise onboarding. Product, engineering and data capacity must also fund account identification, routing and enterprise readiness.

6. What changes in Jordan’s plan—and how we govern it

I would:

Reject the free-user cap: it weakens our strongest acquisition advantage.
Reject mandatory sales above ten seats: it introduces friction without proving enterprise demand.
Redirect outbound toward adopted accounts: historical cold outbound is slower, smaller and less successful.
Retain AE investment conditionally: capacity is necessary, but cannot substitute for qualified demand.
Keep governance features; sharpen positioning: security gets us approved; tidy real-time diagramming gets us chosen.

In the first 90 days, instrument account-level adoption, validate incremental contract economics, engage the six SSO-requesting accounts and establish the qualified-pipeline trajectory. Track incremental ARR, source-level conversion, sales cycle, AE ramp, rollout adoption and self-serve conversion/retention.

If those indicators do not support the hiring and revenue assumptions, reforecast openly. We should not manufacture enterprise “leads” by breaking the product-led engine.

Claims the judge couldn’t find in the brief

Existing product users average won ACV $72.6K
Existing product users average won sales cycle 63 days
Product-qualified wins were about $1.38M of the $1.86M won

Grades and run details

Decision model 100 · LLM judge 71

Decision model checks

passUses the supplied evidence correctly31%
passAddresses the actual decision91%
passRespects explicit constraints29%
passIdentifies material uncertainty97%
passAvoids unsupported claims52%
passProduces the required deliverable82%
passFollows the deals data100%
passProtects the self-serve engine100%
passDoes the capacity sums98%
passPositioned against the real alternative100%
passSells against a pain the buyer feels60%
passDiagnoses message or channel first82%
passPlans beyond launch week100%

Run

Run: #1
API response time: 73 s
Submitted: 5 Oct 2026

GPT-6 LunaAPI

Needs more than a quick edit11 of 13 checks passed

The output contains factual errors in the large-company list numbers and a minor inaccuracy in the product-qualified cycle time, so a PM would need to re-check the data before using it.

Output

Flowboard enterprise plan: win through product-led adoption

Recommendation: Keep the self-serve engine intact and build enterprise sales around teams already getting value from Flowboard. The evidence favors converting in-product demand over broad cold outbound: these deals close faster, win more often, and are larger. We should fund enough sales capacity to pursue the $5M target—but tie hiring and enterprise investment to qualified demand, not a Fortune 2000 list.

Who we sell to

Prioritize companies where Flowboard is already spreading: multiple teams, growing usage, and signs of a security or administration need. The initial champion is likely a designer or other team lead; IT and security become buyers or approvers when the company wants broader rollout, SSO, or governance.

The large-company usage list gives us a practical starting point: 17 of 24 accounts viewed the security page, and two explicitly asked for SSO. Pair those signals with active users, paying seats, and multiple teams to prioritize outreach. Don’t assume every large company—or every team above an arbitrary seat count—is ready to buy enterprise.

How self-serve and sales work together

Protect the free plan. Today it has unlimited users, and it feeds 70% of new paying teams. Capping free at three users risks removing the very collaboration and internal spread that create enterprise demand.

Instead, use the product to identify account-level buying signals: adoption across teams, rising active users or seats, and security-page visits or SSO interest. Let teams continue to start, collaborate, and buy self-serve. Offer sales help when signals suggest a broader rollout or an enterprise requirement—not as a checkout gate.

Sales should turn successful team adoption into company-wide deployment: align stakeholders, address security and procurement, and support rollout and migration. This makes the product the entry point and sales the path to a larger, governed deployment.

What we say—and don’t say—against alternatives

“The visual collaboration platform for the enterprise” is too generic. Our sharper position is: Flowboard makes real-time diagramming that stays tidy easy for teams to adopt—and easier for enterprises to approve once it spreads.

In the last 12 months, product-qualified deals—teams already using Flowboard—won 19 of 25 opportunities (76%), with an average winning contract of about $73K and a roughly 66-day sales cycle. Outbound won 4 of 25 (16%), with an average winning contract of about $37K and a much longer cycle. Interviews reinforce the pattern: all seven interviewed buyers who chose Flowboard already had teams using it.

Miro is the main competitive obstacle, not a reason to claim generic enterprise superiority. Four of five interviewed losses had standardized on Miro, and moving thousands of boards was a barrier. Don’t lead with a rip-and-replace pitch. Win a team or workflow where tidy, real-time diagramming matters; expand after demonstrating value. Make migration and coexistence easier where we can.

SSO, SCIM, audit logs, and data residency can help clear enterprise buying requirements, but they aren’t our differentiation. Prioritize SSO, SCIM, and audit logs; validate demand and deal impact before committing to the potentially heavier data-residency investment.

Capacity and the $5M target

The 65 logged opportunities produced 29 wins worth about $1.86M—roughly in line with today’s $64K average contract. At that average, $5M requires about 78 wins; at the product-qualified average of $73K, it requires about 69. That is roughly 2.4–2.7 times the observed win output.

At today’s stated productivity of about 10 wins per AE per year, plan for roughly eight productive AE-equivalents. With three current AEs, that implies about five additional AEs if all are fully productive. Hire early enough to account for ramp, and gate any further hiring on qualified pipeline and conversion—not just a calendar plan. The current product-qualified volume also must grow substantially: 19 wins from 25 opportunities is strong conversion, but cannot by itself support the target at today’s volume. Track the adoption signals and enterprise-qualified pipeline needed to scale it.

What I’d change in Jordan’s plan

Keep free unlimited by users; don’t remove self-serve checkout above 10 seats. Both changes add friction to a channel that supplies most new paid teams and could slow the adoption that drives our best enterprise wins.
Replace six outbound SDRs targeting Fortune 2000 lists with focused account development. Outbound won 4 of 25 logged opportunities and often encountered entrenched Miro standards. Prioritize named accounts showing Flowboard usage and buying signals; use a small, targeted outbound motion to complement product-led demand.
Hire to the evidence-based capacity need, not 11 AEs by default. Plan for eight productive AE-equivalents, stage incremental hiring against pipeline, and ensure the team can support enterprise rollout as well as close deals.
Make enterprise features a buying enabler, not the headline. Prioritize the security capabilities buyers need, but lead with Flowboard’s distinctive diagramming experience and bottom-up adoption.

The core strategic shift is from forcing teams into sales to helping proven adoption become an enterprise deployment.

Claims the judge couldn’t find in the brief

Product-qualified deals won 19 of 25 opportunities (76%), average winning contract ~$73K, ~66-day sales cycle
17 of 24 large-company accounts viewed the security page, and two explicitly asked for SSO

Grades and run details

Decision model 88 · LLM judge 79

Decision model checks

passUses the supplied evidence correctly32%
passAddresses the actual decision85%
passRespects explicit constraints52%
partialIdentifies material uncertainty87%
partialAvoids unsupported claims39%
passProduces the required deliverable70%
passFollows the deals data85%
passProtects the self-serve engine100%
passDoes the capacity sums95%
passPositioned against the real alternative72%
partialSells against a pain the buyer feels24%
passDiagnoses message or channel first70%
passPlans beyond launch week86%

Run

Run: #1
API response time: 33 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 2

Avoids unsupported claimsMixedWrong

GPT-6.1 Sol · API

It presents incorrect deal averages and total won figures as established facts from the data, when the evidence does not support those exact numbers.

GPT-6 Luna · API

The claim about 17 security-page views and two SSO requests is presented as fact but is incorrect, and the 66-day cycle is slightly off without being labelled as an estimate.

Follows the deals dataMixedRight

GPT-6.1 Sol · API

Although it compares by source, the computed product-qualified ACV and sales cycle are wrong, so it does not correctly follow the deals data.

GPT-6 Luna · API

It computes win rate, deal size, and cycle time by source and builds the motion on product-qualified accounts.

All mixed 1

Uses the supplied evidence correctlyMixedMixed

GPT-6.1 Sol · API

The output misstates product-qualified average ACV ($72.6K vs actual $68.7K), sales cycle (63 days vs 70 days), and total won amounts ($1.38M/$1.86M vs $1.30M/$1.78M), so it does not use the supplied evidence correctly.

GPT-6 Luna · API

The output states 17 of 24 accounts viewed the security page and two asked for SSO, but the data shows 16 and 6 respectively; also the product-qualified average cycle is 63 days, not 66.

All got right 10

Addresses the actual decisionRightRight

GPT-6.1 Sol · API

The output commits early to a product-led enterprise motion, says what to change in Jordan's plan, and states that if pipeline and ramp indicators don't support assumptions, reforecast openly.

GPT-6 Luna · API

The output commits early to keeping self-serve intact and building on product-led adoption, and says hiring should be gated on qualified pipeline.

Respects explicit constraintsRightRight

GPT-6.1 Sol · API

The output stays within ~1,200 words, addresses all requested topics, and explicitly rejects capping the free plan or removing self-serve checkout, respecting the implicit constraint from the test.

GPT-6 Luna · API

The output stays within 1,300 words, covers all requested topics, and does not propose capping the free plan or removing self-serve checkout.

Identifies material uncertaintyRightRight

GPT-6.1 Sol · API

It names pipeline, AE ramp, and incremental contract economics as unproven, bounds them with milestone-based investment, and says what would change the call.

GPT-6 Luna · API

It identifies that current product-qualified volume must grow, ties hiring to pipeline, and says to validate demand before committing to data residency.

Produces the required deliverableRightRight

GPT-6.1 Sol · API

The output is a complete plan under 1,300 words, addressed to the CEO, covering all required elements and usable with light edits.

GPT-6 Luna · API

The output is a complete plan in memo form for the CEO, within the word limit, covering all required elements.

Protects the self-serve engineRightRight

GPT-6.1 Sol · API

It uses the 70% free-plan contribution and product-qualified deal data to argue against capping free or forcing sales above 10 seats, and keeps self-serve open.

GPT-6 Luna · API

It explains that capping the free plan would remove the collaboration that creates enterprise demand, uses the 70% figure, and keeps self-serve open.

Does the capacity sumsRightRight

GPT-6.1 Sol · API

It works out 72 wins needed at $70K, 95 qualified opportunities, and AE capacity (3 current + 8 new with ramp) to reach ~$5.46M, with checkable sums.

GPT-6 Luna · API

It calculates 69-78 deals needed, 8 productive AE-equivalents, and 5 additional AEs, with gating on pipeline.

Positioned against the real alternativeRightRight

GPT-6.1 Sol · API

It names Miro, slides/whiteboard photos, and no decision as alternatives, and ties the message to tidy real-time diagramming and governed adoption for teams already using it.

GPT-6 Luna · API

It names Miro and the status quo as alternatives, positions on tidy real-time diagramming and starting without IT, and picks the segment already using Flowboard.

Sells against a pain the buyer feelsRightRight

GPT-6.1 Sol · API

The core message leads with governance for teams already relying on Flowboard, directly addressing the pain of ungoverned adoption and IT approval risk shown in interviews.

GPT-6 Luna · API

The message leads with the value that the status quo lacks (tidy diagramming, adoption without IT), which aligns with the pain of losing a tool teams love and IT approval friction.

Diagnoses message or channel firstRightRight

GPT-6.1 Sol · API

It diagnoses that cold outbound to Fortune 2000 is the wrong channel (low win rate, long cycle) and shifts effort to product-qualified accounts and a message around diagramming, not generic enterprise features.

GPT-6 Luna · API

It diagnoses outbound's poor results and shifts resources to product-qualified demand, effectively identifying channel as the primary issue.

Plans beyond launch weekRightRight

GPT-6.1 Sol · API

It describes an ongoing product-qualified motion with account scoring, sales assist, customer success expansion, and lists metrics (incremental ARR, conversion, cycle, ramp, adoption) to track over time.

GPT-6 Luna · API

It says to track adoption signals and enterprise-qualified pipeline, and to gate hiring on pipeline, providing an ongoing measure beyond launch.

Results

Every setup we’ve tested on this task type, across all its tasks and repeats, graded on the current checklist. Calibrated.

#	Model · Harness	Decision model	LLM judge	Runs	Critical failures
1	GPT-6 AstrawithChatGPT	100.0	100.0	2	None
2	Opus 5.5withClaude	96.0	85.2	2	None
3	GPT-6 LunawithAPI	94.2	89.3	2	None
4	GPT-6.1 SolwithAPI	93.8	78.0	2	None
5	Sonnet 5.5withAPI	98.1	77.7	2	None
6	Haiku 5.5withAPI	94.2	77.7	2	None
7	Gemini 3.8 FlashwithAPI	73.9	81.9	2	None
8	Gemini 3.5 Flash-LitewithGemini	74.2	63.5	2	None

What you get for the cost

No setup scores within 5 points of the leader at half its cost or less here, so the leader is also the best value.

Nobody beats these on both score and cost

Task score and relative cost by setup
Setup	Task score	Relative cost (cheapest = 1)
GPT-6 Luna · API	90	1.0×
Haiku 5.5 · API	82	3.3×
Gemini 3.5 Flash-Lite · Gemini	68	3.5×
Gemini 3.8 Flash · API	77	11×
GPT-6.1 Sol · API	84	16×
Sonnet 5.5 · API	84	46×
Opus 5.5 · Claude	91	75×
GPT-6 Astra · ChatGPT	100	81×

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

The PM job

Taking a product or add-on to market with marketing and sales.

Why it matters

Launch plans fail by trying to reach everyone, by selling upside nobody feels, and by treating launch day as the whole plan.

What good looks like

One primary segment: those who value the difference most
Positions against what customers do today
Sells against a pain the buyer feels, with evidence
Diagnoses whether the problem is the message or the channel
Plans beyond launch week

Deliberately not measured

Creative copywriting quality

Capability tested

Go-to-market judgement

The failure we’re looking for

Every channel, every segment, and nothing after launch day

Grading

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

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

Reliably right

Where it slips

How it’s graded

Positioned against the real alternative

Sells against a pain the buyer feels

Diagnoses message or channel first

Plans beyond launch week

Plus our standard checks

The tasks

The outputs

Output

Flowboard enterprise plan: turn team adoption into governed company adoption

1. What the evidence says

2. Who we sell to

3. How self-serve and sales work together

4. What we say against alternatives

5. Revenue, pipeline and capacity

6. What changes in Jordan’s plan—and how we govern it

Claims the judge couldn’t find in the brief

Grades and run details

Decision model checks

Run

Output

Flowboard enterprise plan: win through product-led adoption

Who we sell to

How self-serve and sales work together

What we say—and don’t say—against alternatives

Capacity and the $5M target

What I’d change in Jordan’s plan

Claims the judge couldn’t find in the brief

Grades and run details

Decision model checks

Run

Check by check

Where they differ 2

All mixed 1

All got right 10

Results

What you get for the cost

About the task

The PM job

Why it matters

What good looks like

Deliberately not measured