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

  1. 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
  2. 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
  3. 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

  1. 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
  2. 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
  3. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

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.

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:

SourceWon / opportunitiesWin rateAverage won ACVAverage won sales cycle
------:---:---:---:
Existing product users19 / 2576%$72.6K63 days
Inbound demo requests6 / 1540%$55.1K86 days
Outbound4 / 2516%$36.8K190 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:

  1. Aggregate teams by company domain, with appropriate access and privacy controls.
  2. Score sustained usage, multi-team adoption, growth and governance intent.
  3. Offer relevant help in-product: “Bring your teams together with centralized administration and security.”
  4. Route high-intent accounts to sales promptly; nurture lower-intent accounts while preserving checkout.
  5. 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

Needs more than a quick edit11 of 13 checks passed

The output contains a factual error in the median cycle and an unsupported claim about data residency, so a product manager would need to re‑verify the data before using it.

Output

To: CEO From: VP Product Re: Enterprise plan for the board

Bottom line: Jordan is right that enterprise revenue should run through sales. But the draft caps the motion that produces most of our enterprise deals, and it spends hiring budget on the motion that loses most often. I'd keep the free plan open, route accounts on buying signals, hire against proven pipeline, and present $3.0M committed with $5M as a stretch tied to specific gates.

What the last 12 months show (65 enterprise opportunities, 29 won, $1.86M ACV)

SourceOppsWonWin rateAvg won ACVNotes
Product-qualified (teams already on Flowboard)251976%$72.6K74% of won ACV; median cycle 66 days
Inbound demo request15640%$55K
Outbound25416%$37KWins took ~190 days; 17 of 21 losses went to Miro

Across all 36 losses, 26 went to Miro as company standard and 10 to no decision. All seven enterprise wins in the interviews had teams already using Flowboard.

Who we sell to

Our best enterprise buyers are large companies where we already have several teams. Of the 24 companies over 1,000 employees using us, 16 have looked at our security page in the last 30 days. Six have also asked for SSO: Aldergate, Brightfield, Corvel, Dunmore, Eastlake and Fennick. Together they hold 116 paying seats. That's the first account list, and it's warm.

I would not start with Fortune 2000 cold outbound. That motion wins 16% of the time, and the companies on those lists are the ones most likely to have standardised on Miro.

How self-serve and sales work together

  • The free plan stays open. A 3-user cap breaks the motion that generated 19 of our 29 wins. The enterprise deals came from teams that grew to hundreds of people on free, with designers who "refused to give it up." A cap doesn't make those teams call us. It sends them to Miro, or it keeps them on the free plan without ever reaching the buyer.
  • Routing should follow signals, not one seat number. Route an account to sales when it has three or more teams in one company, asks for SSO or SCIM, views the security page, or passes about 25 paying seats. Self-serve checkout stays available below that line. Removing checkout at 10 seats would push paying mid-size teams into a sales cycle they don't need, and we'd lose that ARR.
  • Division of labour. Sales owns the contract, the security review and the SSO rollout. Product owns in-app signals, admin controls and the usage data that tells sales where to go. AEs work the warm account list, not cold lists.

What we say against the alternatives

  • Against Miro: Don't compete on feature parity. Buyers chose us for real-time diagrams that stay tidy and a free start that doesn't need IT. "The visual collaboration platform for the enterprise" puts us in Miro's category and invites a feature comparison we lose. I'd use: "The whiteboard your teams already chose, now with the controls IT needs."
  • Where Miro is already company standard, we lose most of the time. Don't spend outbound effort there. Instead, scope a Miro board import. Buyers cited migrating thousands of boards as the reason they stayed. If we can make the migration a file import, we can contest some of those standardisation decisions. Engineering needs to size this.
  • Against no decision (10 losses): My hypothesis is that these stall in IT or security review. SSO, SCIM and audit logs address exactly that. Sales should check lost-deal notes to confirm.

Capacity

  • Three AEs closed 29 deals, about ten each, or roughly $620K per AE. New AEs take around six months to ramp.
  • The binding constraint is pipeline, not AE headcount. At the product-qualified average of $72.6K, $5M is about 69 wins. At a 76% win rate, that needs roughly 90 product-qualified opportunities a year. We have 25.
  • Hiring eight AEs before that pipeline exists leaves most of them waiting. Six SDRs working outbound would be pointed at our lowest-converting motion.
  • My estimate: three current AEs plus four hires in Q1 (about half a year of productivity each) gives roughly five effective AEs, or about $3.1M. The large-account program could add $0.5–1M. That supports a $3.0–3.5M base. The $5M case requires roughly tripling product-qualified pipeline and having the new hires productive by Q3. Nothing in the data shows that yet.

What I'd change in Jordan's plan, and why

  1. Keep the free plan unlimited. It produces the enterprise pipeline, and a cap would reduce that pipeline.
  2. Replace the 10-seat rule with signal-based routing (see above) and keep checkout below about 25 seats. This sends sales the accounts most likely to buy without cutting paying self-serve revenue.
  3. Hire four AEs now and two more on a gate: 40 or more qualified product-qualified opportunities in a quarter, and a trailing win rate above 60%. If the gate isn't met, we don't carry the cost.
  4. Cancel the six outbound SDR roles. Put two SDRs on inbound demo follow-up (40% win rate, and most inbound losses were to Miro or no decision, so speed-to-follow-up matters). Put two on expansion of the 24 large accounts. Hold two until the gate is met.
  5. Build SSO, SCIM and audit logs first. Six accounts have asked for SSO, and 16 of 24 large accounts have looked at the security page. Defer data residency. No deal in the last 12 months cited it, so build it only when a named deal requires it.
  6. Change the messaging as above and drop "for the enterprise."
  7. Scope the Miro board import as a product workstream, with a decision in 30 days.

What I'd ask the board for

  • A committed number of $3.0M enterprise ARR, with $5M as a stretch.
  • Gates the board can track quarterly: qualified product-qualified opportunities, win rate, the six SSO accounts closed, and no-decision losses as a share of the total.
  • Approval to hire four AEs now and two on the gate.

I'd rather present a number we can defend than one that depends on hiring eight people before we have the pipeline to keep them busy.

Claims the judge couldn’t find in the brief

  • Median sales cycle for product-qualified deals is 66 days.
  • No deal in the last 12 months cited data residency as a requirement.
  • New AEs take around six months to ramp.

Grades and run details

Decision model 88 · LLM judge 79
Decision model checks
  • failUses the supplied evidence correctly13%
  • passAddresses the actual decision97%
  • passRespects explicit constraints52%
  • passIdentifies material uncertainty95%
  • partialAvoids unsupported claims34%
  • passProduces the required deliverable91%
  • passFollows the deals data98%
  • passProtects the self-serve engine100%
  • passDoes the capacity sums97%
  • passPositioned against the real alternative97%
  • passSells against a pain the buyer feels86%
  • passDiagnoses message or channel first98%
  • passPlans beyond launch week100%
Run
Run
#1
API response time
54 s
Submitted
8 Oct 2026

Needs more than a quick edit10 of 13 checks passed

The output invents Miro's tagline and calls Miro the category leader without evidence, requiring fact-checking before use. Otherwise the plan is solid.

Output

MEMORANDUM

TO: Chief Executive Officer FROM: VP Product DATE: October 24, 2023 SUBJECT: Counter-Proposal to CRO Enterprise GTM Plan: Path to $5M New Enterprise ARR

*

Executive Summary

Reaching $5M in new enterprise ARR next year is achievable, but Jordan’s draft will choke the very engine that powers our business.

Jordan’s proposal relies on a legacy top-down playbook: restricting free/self-serve adoption and deploying cold outbound reps against Fortune 2000 accounts. The data from our last 12 months shows this approach is commercially unviable: * Cold outbound fails: Outbound win rates are 16%, sales cycles take 190 days, ACV is low ($36.8k), and 68% of losses go to Miro because they already hold the enterprise standard. * Product-Qualified Leads (PQLs) dominate: Enterprise deals originating from existing Flowboard usage boast a 76% win rate, a 64-day cycle, and an average ACV of $72.6k.

Flowboard does not win by out-shouting Miro in cold enterprise RFPs. We win via Product-Led Sales (PLS): landing virally with teams via our free and self-serve plans, and expanding into six-figure IT-sponsored enterprise contracts once organic adoption takes root.

---

What to Change in Jordan’s Plan & Why

Jordan's CRO ProposalVP Product RecommendationWhy (Data-Backed Rationale)
:---:---:---
1. Cap free plan at 3 users (today: unlimited users, 3 boards).Keep unlimited users / 3 boards. Introduce team-level sharing limits, but preserve viral intra-company collaboration.Free plans feed 70% of self-serve paying teams. In enterprise win interviews, buyers explicitly cited that having "300 people already on it" forced IT approval. Capping users kills viral distribution and hands bottom-up adoption to competitors.
2. Remove self-serve above 10 seats; force teams to talk to sales.Keep self-serve checkout up to 50 seats. Trigger automated sales outreach at 15+ seats or multi-team domain clusters.31,000 paying teams rely on friction-free checkout. Shunting 11-seat teams to AEs bogs sales down in sub-$5k transactions instead of closing $70k+ enterprise contracts.
3. Hire 8 AEs and 6 cold SDRs targeting the Fortune 2000.Hire 4 AEs and 2 Product BDRs (totaling 7 AEs, 2 BDRs). Point them exclusively at internal expansion and high-intent inbound.Cold outbound yielded just 4 wins in 12 months. Reps running outbound into the Fortune 2000 run directly into entrenched Miro enterprise agreements. Product BDRs mining our existing base will generate 4x higher pipeline value.
4. Enterprise features: SSO, SCIM, audit logs, residency.Agree, with precise sequencing: Prioritize SAML SSO, SCIM, and Domain Capture/Admin Console immediately.6 large accounts (e.g., Aldergate Bank, Fennick Holdings) have already viewed security pages and requested SSO. Domain capture turns rogue usage into an immediate enterprise contract trigger.
5. Messaging: "The visual collaboration platform for the enterprise."Messaging: "Precision diagramming and structured collaboration teams actually adopt.""Visual collaboration platform" is Miro’s tagline. We cannot beat the category leader at their own positioning; we win on structured diagramming clarity and viral ease-of-use.

---

1. Who We Sell To (Target ICP)

We do not target cold accounts. Our Enterprise ICP consists of: * Target Accounts: Mid-market and Enterprise companies (1,000–15,000+ employees), heavily indexing on engineering, architecture, product, and design organizations. * Entry Footprint: Organizations with $\ge$2 active Flowboard teams or $\ge$20 active users across free/self-serve tiers. * Economic Buyers: VP of IT / CISO (concerned with governance, security, consolidation) paired with internal VP/Director Champions in Product/Engineering. * Immediate Target List: The 24 large enterprises already using Flowboard (e.g., Corvel Logistics, Brightfield Bank). Six of these companies already have multiple teams, paying seats, and active requests for SSO—representing an immediate pipeline of ~$450k ARR.

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2. The Product-Led Sales Engine: Product & Sales Alignment

Instead of sales acting as a barrier to product adoption, the product serves as the primary pipeline generation channel for sales.

``` [ Free Plan: Unlimited Users ] ──> [ Self-Serve Expansion (up to 50 seats) ] │ │ └───> [ Domain Consolidation Trigger ] <───┘ │ ▼ [ Enterprise Sales (SSO, Governance, Security) ] ```

1. Top of Funnel (Free): Maintain the unlimited-user tier to let individual squads adopt Flowboard without procurement hurdles. 2. Expansion Triggers (PQL Engine): Instrument automated alerts to the BDR/AE team when: * A single company domain crosses 20 active monthly users across workspaces. * A workspace admin visits `/security`, `/compliance`, or searches for "SSO". * Multiple corporate credit cards from the same domain are paying for disjointed self-serve accounts. 3. In-Product Enterprise Gates: * Domain Control & SSO: When IT attempts to enforce SSO, the domain is claimed, prompting an enterprise consolidation conversation. * Centralized Admin & Audit Logs: Free and self-serve plans lack cross-workspace audit logs, data retention policies, and SCIM provisioning.

---

3. Positioning Against Alternatives (Winning Against Miro)

In head-to-head enterprise RFPs against Miro, we lose (17 outbound losses last year were to Miro standards). Switching thousands of legacy boards is an impossible sell. We win by changing the battleground:

DimensionMiroFlowboard
:---:---:---
Core ValueBroad, freeform digital whiteboard. Can quickly become cluttered and unwieldy.Structured, precision diagramming that stays organized in real time.
Target UserGeneral business users, workshop facilitators, broad marketing.Technical & Product teams: System architects, engineers, UX designers, technical PMs.
Enterprise Pitch"Standardize everyone on a single massive whiteboard.""Complement or replace messy whiteboards with structured architecture and technical execution workflows."
Pricing / ProcurementExpensive wall-to-wall licensing with heavy IT mandates.Frictionless bottoms-up landing that consolidates high-value technical teams with zero onboarding friction.

Key Narrative: "Miro is where teams brainstorm; Flowboard is where technical teams design, architect, and build. Keep Miro for casual sticky-notes, or standardize on Flowboard for clean, mission-critical systems architecture."

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4. Capacity Model & Math to $5M New Enterprise ARR

Jordan’s proposal to hire 8 AEs and 6 cold SDRs adds excessive headcount burn with terrible pipeline efficiency. By focusing on PQLs and high-intent inbound, our reps will achieve substantially higher conversion and velocity.

#### Operating Assumptions (Derived from Historical Data) * Average Enterprise ACV: $70,000 (historical PQL average is $72.6k; outbound was $36.8k). * Target ARR: $5,000,000 $\rightarrow$ Requires 72 closed enterprise deals. * Win Rate: 60% (blended between 76% historical PQL win rate and 40% inbound demo rate). * Deals Needed in Late-Stage Pipeline: $72 / 0.60 = \mathbf{120\text{ qualified opportunities}}$. * Sales Cycle: 65–75 days (allowing fast pipeline velocity and intra-year closes).

#### Staffing Requirements * Account Executives (AEs): * Experienced enterprise AE capacity on warm PQLs = 12 deals/year ($840k ARR quota). * 72 deals $\div$ 12 deals/rep = 6 fully productive AEs. * Plan: Retain our 3 current AEs; hire 4 new AEs (staggered: 2 in Q1, 2 in Q2 to account for ramp). Total = 7 AEs. * Business Development Reps (Product BDRs): * Hire 2 Product BDRs (not 6 cold outbound SDRs). * BDRs do not dial cold Fortune 2000 lists. They mine our 31,000 paying teams, identifying multi-license domains, mapping org charts, and securing meetings with IT directors and engineering leads. * Capacity: 2 BDRs generating 5 qualified enterprise pipeline meetings/month each = 120 qualified opportunities/year. * Total Headcount Investment: 6 new hires (4 AEs, 2 BDRs) vs. Jordan’s 14 new hires (8 AEs, 6 SDRs).

This delivers more than 50% savings in customer acquisition cost (CAC) while cutting execution risk.

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5. Summary of Recommended Product & GTM Deliverables

1. Q1 Product Roadmap Enablers: * Ship SAML 2.0 SSO, SCIM user provisioning, and Centralized Domain Capture. * Build internal PLG scoring dashboard connecting Segment/Mixpanel product events to Salesforce/HubSpot (flagging $\ge$2 teams or SSO searches). 2. Q1 Pricing & Packaging: * Keep Free (unlimited users, 3 active boards). * Keep Self-Serve Pro/Team up to 50 seats. * Package SSO, SCIM, custom data retention, and audit logs exclusively in Enterprise. 3. Sales Enablement: * Arm sales with the "Architecture vs. Whiteboarding" competitive battlecards against Miro. * Launch warm outreach sequences immediately into the 24 identified large accounts already using Flowboard.

Claims the judge couldn’t find in the brief

  • Miro is the category leader.
  • 'Visual collaboration platform' is Miro's tagline.

Grades and run details

Decision model 77 · LLM judge 71
Decision model checks
  • failUses the supplied evidence correctly51%
  • passAddresses the actual decision84%
  • partialRespects explicit constraints26%
  • failIdentifies material uncertainty75%
  • partialAvoids unsupported claims43%
  • passProduces the required deliverable77%
  • passFollows the deals data94%
  • passProtects the self-serve engine100%
  • passDoes the capacity sums94%
  • passPositioned against the real alternative73%
  • passSells against a pain the buyer feels56%
  • passDiagnoses message or channel first80%
  • passPlans beyond launch week90%
Run
Run
#1
API response time
47 s
Submitted
6 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 4

Uses the supplied evidence correctlyMixedWrongWrong
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.

Haiku 5.5 · API

The median cycle for product‑qualified deals is stated as 66 days but the data gives 62, and the claim that no deal cited data residency is unsupported.

Gemini 3.8 Flash · API

Claims that Miro is the category leader and that 'visual collaboration platform' is Miro's tagline are not in the supplied context.

Identifies material uncertaintyRightRightWrong
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.

Haiku 5.5 · API

It identifies unknowns (e.g., why no‑decision losses stall, Miro import feasibility) and says how to resolve them.

Gemini 3.8 Flash · API

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

Avoids unsupported claimsMixedWrongWrong
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.

Haiku 5.5 · API

It presents the incorrect median cycle and the ‘no deal cited data residency’ statement as fact without support.

Gemini 3.8 Flash · API

It presents 'Miro is the category leader' and 'Miro's tagline' as facts without evidence, and 'cold outbound fails' as an unqualified conclusion.

Follows the deals dataMixedRightRight
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.

Haiku 5.5 · API

The plan computes win rate, size and cycle by source and builds the motion on product‑qualified accounts.

Gemini 3.8 Flash · API

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

All got right 9

Addresses the actual decisionRightRightRight
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.

Haiku 5.5 · API

The output commits to a clear plan ($3.0M committed, $5M stretch with gates) and states what would change it.

Gemini 3.8 Flash · API

The memo commits to a clear counter-proposal with specific changes, ICP, and capacity plan.

Respects explicit constraintsRightRightRight
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.

Haiku 5.5 · API

The memo is within 1,300 words, addresses all requested topics, and is addressed to the CEO.

Gemini 3.8 Flash · API

The output is a memo to the CEO under 1,300 words, respecting the form and length.

Produces the required deliverableRightRightRight
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.

Haiku 5.5 · API

It delivers a complete, actionable memo in the requested form, suitable for the CEO with minimal edits.

Gemini 3.8 Flash · API

The memo covers all requested elements: who to sell to, self-serve/sales alignment, positioning, capacity, and changes to Jordan's plan.

Protects the self-serve engineRightRightRight
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.

Haiku 5.5 · API

It explains why capping the free plan and removing checkout would hurt the pipeline and keeps self‑serve open.

Gemini 3.8 Flash · API

It uses the 70% free-to-paid stat and PQL data to argue against capping, and keeps self-serve open up to 50 seats.

Does the capacity sumsRightRightRight
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.

Haiku 5.5 · API

The output works out the deals needed (~69), pipeline required (~90 PQOs), and AE capacity to reach $5M.

Gemini 3.8 Flash · API

It calculates 72 deals at $70k ACV, 120 opportunities at 60% win rate, and 6 AEs, with checkable sums.

Positioned against the real alternativeRightRightRight
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.

Haiku 5.5 · API

It names Miro and ‘no decision’ as the real alternatives and ties the message to design adoption and IT controls.

Gemini 3.8 Flash · API

It names Miro and the status quo (whiteboard photos) as alternatives, and positions on structured diagramming for technical teams.

Sells against a pain the buyer feelsRightRightRight
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.

Haiku 5.5 · API

The message is built on buyer pain (IT losing control, design team refusal) evidenced by the interviews.

Gemini 3.8 Flash · API

The message leads with the pain of messy whiteboards and lack of adoption, supported by win/loss interviews.

Diagnoses message or channel firstRightRightRight
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.

Haiku 5.5 · API

It diagnoses that the outbound channel is underperforming (low win rate, high loss to Miro) and shifts effort to warm product‑qualified accounts.

Gemini 3.8 Flash · API

It diagnoses cold outbound as a channel problem and Miro's positioning as a message problem, shifting spend to PLS.

Plans beyond launch weekRightRightRight
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.

Haiku 5.5 · API

The plan includes ongoing routing, quarterly gates, and measures (PQOs, win rate, SSO closures) beyond launch.

Gemini 3.8 Flash · API

It describes ongoing PLS triggers, BDR mining, and a Q1 roadmap with scoring dashboard, not just launch.

Results

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

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6 AstrawithChatGPT100.0100.02None
2Opus 5.5withClaude96.085.22None
3GPT-6 LunawithAPI94.289.32None
4GPT-6.1 SolwithAPI93.878.02None
5Sonnet 5.5withAPI98.177.72None
6Haiku 5.5withAPI94.277.72None
7Gemini 3.8 FlashwithAPI73.981.92None
8Gemini 3.5 Flash-LitewithGemini74.263.52None

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.

Task score against relative cost for each setup. The table below has the same figures.607080901001×3×10×30×100×Cost per output, relative to the cheapest setup (log scale) →↑ Task scoreGPT-6 Astra · ChatGPT: task score 100, 81× the cheapestOpus 5.5 · Claude: task score 91, 75× the cheapestGPT-6 Luna · API: task score 90, the cheapestGPT-6.1 Sol · API: task score 84, 16× the cheapestSonnet 5.5 · API: task score 84, 46× the cheapestHaiku 5.5 · API: task score 82, 3.3× the cheapestGemini 3.8 Flash · API: task score 77, 11× the cheapestGemini 3.5 Flash-Lite · Gemini: task score 68, 3.5× the cheapestGPT-6 Astra · ChatGPTOpus 5.5 · ClaudeGPT-6 Luna · APIGPT-6.1 Sol · APISonnet 5.5 · APIHaiku 5.5 · APIGemini 3.8 Flash · APIGemini 3.5 Flash-Lite · Gemini
Nobody beats these on both score and cost
Task score and relative cost by setup
SetupTask scoreRelative cost (cheapest = 1)
GPT-6 Luna · API901.0×
Haiku 5.5 · API823.3×
Gemini 3.5 Flash-Lite · Gemini683.5×
Gemini 3.8 Flash · API7711×
GPT-6.1 Sol · API8416×
Sonnet 5.5 · API8446×
Opus 5.5 · Claude9175×
GPT-6 Astra · ChatGPT10081×

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

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