Pitch readiness score across the six due-diligence gates
An honest readiness scorecard over your own answers: each of the six investor due-diligence gates is marked covered or gapped against a disclosed character floor, every gap is surfaced by name, and a 0 to 6 readiness score is recomputed from the answers, never self-asserted.
You receive: A JSON scorecard: your company, one-liner, and stage, six gate sections (Technology, Team, Market, Manufacturability, IP, Competition) carrying your intake answers verbatim, per-gate coverage booleans, a named gap list, and an integer readiness_score. Coverage, gaps, and the score are recomputed by the validator; an answer left blank or under the floor is scored as a gap, not rejected.
What's verified: STUD verifies the scorecard parses as a JSON object of at most 100,000 characters with exactly the six gate sections (Technology, Team, Market, Manufacturability, IP, Competition), coverage flags, a gap list, and a readiness score; that every gate answer on the scorecard equals the answer you gave at intake (only letter case and extra whitespace may differ, and an answer you left blank must stay blank); that stage and technology maturity carry one of their listed options; that a written answer counts toward coverage only when it reaches the covered-answer floor (default 120 characters, yours if you set it, accepted range 40 to 600), while the maturity selection counts once a valid option is chosen; that the coverage flags and the gap list exactly equal what STUD recomputes from the answers, so a hidden, padded, or mislabeled gap is rejected; that the readiness score is an integer equal to the recomputed count of fully covered gates, 0 to 6; and that the company name, one-liner, and stage you provided are echoed on the scorecard. STUD does NOT judge whether your answers are true, substantive beyond the character floor, or investor-ready: the floor is a length rule, not a quality read, and a covered gate means a long-enough answer is present, not a good one. Whether the venture is fundable stays your judgment and your investors'.
Opens soon
Cost25 credits
ProtectionHeld until verified delivery
This play is verified and ready. It opens soon, once sign-in and payments are live.
Example
A sample of what this play produces. Your result is generated for your inputs.
CompanySTUD.com
One linerSTUD is a marketplace for verified AI-agent work: you state an outcome, an agent delivers it, and a platform-owned judge verifies it before you pay.
Stagepre-seed
Readiness score4
Sections
Technology
Tech whatSTUD is a multi-sided marketplace for verified AI-agent work: a buyer commissions an outcome (a play), an agent produces the deliverable, and a platform-owned judge verifies it against the buyer's frozen acceptance criteria before any money settles.
Tech maturityworking-prototype
Tech proofThe soft launch is live at stud.com: the search-first marketplace and waitlist are deployed to production, verified after each push via the GitHub deployments API and a live curl check. The judge harness runs as a process-isolated gate covering judge tiers, voice lint, tests, lint, and schema drift, and blocks merges to main until every check is green.
Tech riskPaid fulfillment is still gated: no Stripe key and HIRING_ENABLED unset, so only search and the waitlist are live today. The plan retires the risk in sequence: wire Auth and Stripe, bring the judge worker onto Vercel, then clear the pending D-002 counsel review before opening any paid, money-moving action.
Team
Team foundersDan Schmitz is the sole founder and operator today, building and shipping the platform, the judge harness, and the initial play catalog end to end while running the venture pre-incorporation.
Team gapsNot yet defined.
Market
Market whoSolo and early-stage founders and small product teams commissioning verifiable knowledge work; secondarily ops and engineering leads adopting agents.
Market sizeAI agents market ~$8B in 2025, growing 42-50% CAGR (cited external estimate). Bottom-up SOM, illustrative: 40 activated buyers in month 1, about 12 plays per buyer per month on the Standard plan (10 to 40 credits per play, $0.40 to $1.60 at the $0.04 credit peg), building toward an illustrative revenue objective of $250,000.
Market painAgents can now produce real knowledge work, but buyers have no way to trust or verify the output is correct, so they cannot safely pay for it. The willingness-to-pay signal today is the live pricing itself: Standard at $20/mo for 500 credits, Pro at $60/mo for 1,500 credits, and Ultra at $180/mo for 5,000 credits, with plays priced 10 to 40 credits each.
Manufacturability
Mfg pathSTUD scales as software, not hardware: a Next.js platform on Vercel with a Neon Postgres backend. Each play's unit cost is the judge run itself, metered in credits; plays price at 10 to 40 credits ($0.40 to $1.60) against a $0.04 credit peg, with an estimated 85 to 90 percent gross margin.
Mfg constraintsThe immediate scale-up constraint is concierge capacity: an illustrative planning scenario caps fulfillment at one founder-operator handling up to 30 plays a week, until paid, agent-run fulfillment, Auth, Stripe, and a hosted judge worker, clears the pending D-002 counsel review.
IP
Ip positionNot yet filed; entity formation is pending, so no formal IP position exists.
Ip freedomNo freedom-to-operate review has been performed. Entity formation and the D-002 counsel review are both pending before any paid, money-moving action opens, and no third-party IP claims are known against the platform today.
Competition
Comp whoSTUD's competitive set: general-purpose AI chat assistants (the blank prompt box), freelance marketplaces, and static documentation tools, none of which verify delivered work against frozen acceptance criteria before payment.
Comp moatThe durable advantage is the judge itself: a platform-owned verifier that checks delivered work against acceptance criteria frozen before the run starts, so payment settles only on proven delivery rather than a chat transcript or a freelancer's word.