STUD
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Investor due-diligence answer register

A category-balanced register of answers to standard investor due-diligence questions, structured as data-room-ready JSON, with category coverage, answered-count consistency, and per-answer length floors machine-checked.

You receive: A JSON object { company, round_stage, items: [ { id, category, question, answer, evidence_refs: [..], status } ], category_coverage: [ { category, answered_count } ] }. category must be exactly one of: financing, technology, product, market, business_model, team, financials_returns. status must be exactly one of: answered, na, pending. Each category_coverage answered_count must equal the count of answered items in that category.

Part of Launch Product, Pitch Investors

Example

A sample of what this play produces. Your result is generated for your inputs.

Company
STUD.com
Round stage
pre-seed

Items

Id
fin-1
Category
financing
Question
What is the company raising, and on what basis was the amount set?
Answer
STUD is targeting an illustrative $1,000,000 pre-seed raise (a target, not a committed round). The amount is sized to fund the path from soft launch to the illustrative revenue objective of $250,000 in annual revenue, on a concierge fulfillment model whose capacity is one founder-operator at roughly 30 plays per week.
Evidence refs
  • fact:finance.raise_amount_usd (illustrative)
  • fact:metrics.revenue_objective_usd (illustrative)
Status
answered
Id
fin-2
Category
financing
Question
What prior financing has the company taken?
Answer
None. STUD is pre-revenue and has taken no outside capital to date. There are no convertible notes, SAFEs, or venture debt outstanding, so the pre-seed round would be the first priced or convertible instrument on the cap table.
Evidence refs
  • fact:company.stage
Status
answered
Id
fin-3
Category
financing
Question
Is the company incorporated, and is the cap table clean?
Answer
Yes. STUD is a registered trade name of TELEVORT INC, a Delaware corporation. Dan Schmitz is the sole founder, and the company has taken no outside capital, so the cap table enters the round with no legacy investor claims to unwind.
Evidence refs
  • fact:company.incorporated
  • fact:company.founder
Status
answered
Id
tech-1
Category
technology
Question
What is the core technology and what is defensible about it?
Answer
STUD is a living system for a venture: a workspace that holds what the business knows, a catalog of ready-made plays that AI agents run from a short intake, and structured results that stay current and feed the next play. The defensible part is the compounding loop, a fact stated once flows into every run, and every deliverable passes its play's acceptance check before it lands, so the catalog and the acceptance harness improve with every run.
Evidence refs
  • fact:company.what_it_does
  • fact:company.one_liner
Status
answered
Id
tech-2
Category
technology
Question
How is output quality controlled when AI agents do the work?
Answer
Every play carries a machine acceptance check that screens each deliverable before it counts. Calculators are graded against hand-written reference oracles with held-out cases, and document plays are graded by validators that enforce the published contract, so quality control is structural rather than a manual review step.
Evidence refs
  • fact:company.solution
Status
answered
Id
prod-1
Category
product
Question
What does the product ship today, and how mature is it?
Answer
The live catalog holds 265 plays across 14 playbooks (measured dev-seed count, 2026-08-09), covering marketing, finance, product, and engineering work. The platform is in a soft launch: the search-first marketplace and waitlist are live, and paid fulfillment opens behind gated auth and payments.
Evidence refs
  • fact:metrics.catalog_size
  • fact:company.stage
Status
answered
Id
prod-2
Category
product
Question
What problem does the product solve for its user?
Answer
AI can produce real knowledge work, but extracting it is still manual craft: users must know what to ask, phrase it well, re-explain the business every session, and carry outputs somewhere by hand before they go stale. STUD replaces the blank prompt with a catalog of plays whose intakes fill themselves from workspace facts, so nothing starts from zero and results compound.
Evidence refs
  • fact:company.problem
  • fact:company.solution
Status
answered
Id
mkt-1
Category
market
Question
How large is the market and how fast is it growing?
Answer
The AI agents market is about $8B in 2025, growing at a 42 to 50 percent CAGR (cited external estimates). STUD's beachhead within it is code-first verifiable knowledge work for early-stage ventures, a segment where the buyer already trusts agents but lacks a system that makes their output land structured and reusable.
Evidence refs
  • fact:metrics.market_size_note
Status
answered
Id
mkt-2
Category
market
Question
Who is the target customer and how are they reached?
Answer
Solo and early-stage founders and small product teams who need marketing, finance, product, and engineering work done while staying small, with ops and engineering leads adopting AI agents as a secondary segment. Acquisition is content-led through SEO, developer documentation, and launch communities, at an illustrative blended CAC of about $40.
Evidence refs
  • fact:company.target_customer
  • fact:metrics.acquisition_channels (illustrative CAC)
Status
answered
Id
bm-1
Category
business_model
Question
How does the company make money?
Answer
Prepaid credit packs, with 1 credit always 4 cents. One-time packs are 500 credits for $20, 1,000 for $40 (+50 bonus), 2,500 for $100 (+250 bonus), and 5,000 for $200 (+750 bonus); annual prepay packs are 6,000 credits for $240 (+1,500 bonus) and 18,000 for $720 (+5,000 bonus). Purchased credits never expire, bonus credits included. A play costs 250 to 1,000 credits ($10 to $40), most plays 500 credits ($20); credits are held when a run is commissioned and spent only on an accepted run. Paid runs open at launch. A marketplace take rate on third-party supply is a later phase.
Evidence refs
  • fact:pricing.plans_summary
  • fact:pricing.credit_peg_usd
  • fact:pricing.play_cost_range
  • fact:pricing.business_model
Status
answered
Id
bm-2
Category
business_model
Question
What are the unit economics of a buyer account?
Answer
Gross margin is estimated at 87 percent (modeled 85 to 90 percent at launch prices), driven by inference cost per play sitting well under the credit price. Illustrative account economics: about $300 average annual revenue per account (about $25 a month in pack purchases, roughly 1.25 plays a month at the typical 500-credit price) against an illustrative $40 content-led CAC, with illustrative 6 percent monthly logo churn and 108 percent NRR.
Evidence refs
  • fact:finance.gross_margin_pct (estimated)
  • fact:metrics.avg_annual_revenue_per_account_usd (illustrative)
  • fact:metrics.cac_usd (illustrative)
  • fact:metrics.monthly_logo_churn_pct (illustrative)
  • fact:metrics.nrr_pct (illustrative)
Status
answered
Id
team-1
Category
team
Question
Who is on the team and what is the key-person risk?
Answer
STUD is founder-led by Dan Schmitz, currently a team of one operating the platform and the concierge fulfillment loop (capacity about 30 plays per week). Key-person risk is real and disclosed: the mitigation path is that fulfillment is agentic by design, so headcount is a throughput lever rather than the delivery mechanism.
Evidence refs
  • fact:company.founder
  • illustrative launch scenario: concierge capacity
Status
answered
Id
team-2
Category
team
Question
What are the first hires the raise would fund?
Answer
No hires are committed. The working plan is that the raise funds growth engineering and supply curation ahead of any go-to-market headcount, because the concierge capacity ceiling (one operator, about 30 plays per week) is the first constraint the illustrative launch scenario hits.
Evidence refs
  • illustrative launch scenario
Status
answered
Id
finret-1
Category
financials_returns
Question
What are current revenue and the near-term revenue objective?
Answer
Current revenue is zero: STUD is at pre-seed, in a pre-revenue soft launch. The illustrative revenue objective is $250,000 in annual revenue from pack sales, built from the illustrative launch scenario of 200 launch signups, 40 activated buyers in month 1 (a first pack bought and a play run), 25 retained at month 3, a 60:25:10:5 first-pack mix across the $20 / $40 / $100 / $200 packs (average first pack $42), and about $25 a month in pack purchases per active buyer.
Evidence refs
  • fact:company.stage
  • fact:metrics.revenue_objective_usd (illustrative)
  • illustrative launch scenario
Status
answered
Id
finret-2
Category
financials_returns
Question
Are audited financial statements available?
Answer
Not applicable at this stage: TELEVORT INC is pre-revenue and there is no revenue history to audit. Management accounts will be the reporting basis through the pre-seed period.
Evidence refs
  • fact:company.incorporated
  • fact:company.stage
Status
na

Category coverage

CategoryAnswered count
financing3
technology2
product2
market2
business_model2
team2
financials_returns1

Opens soon

Cost50 credits
ProtectionSpent when the run starts · returned only if STUD is at fault

STUD opens runs for everyone soon, on every play at once.

Scope of the check

What's verified: STUD verifies structure and internal consistency of the register, not the truth of its contents. Specifically, STUD verifies: the submission is a JSON object of at most 200000 characters with at most 200 items; company and round_stage are non-empty strings; items is a non-empty array where every item has a non-empty id, category, question, and status plus a string answer; ids are unique; every status is exactly one of: answered, na, pending; every category is exactly one of: financing, technology, product, market, business_model, team, financials_returns; every answered item has an answer of at least min_answer_chars characters after trimming whitespace (default 40, clamped to 1-2000); each required category has at least min_per_category answered items (default 1, clamped to 1-20); at least min_items_total items are answered overall (default 7, clamped to 1-200); no item in a required category is left pending; evidence_refs, when present on an item, is a list of non-empty strings; and category_coverage is present as a list where each answered_count equals the answered count recomputed from the items. That recount is the ONLY arithmetic checked: no valuations, totals, or financial figures inside the answers are computed or verified. Names in required_categories outside the seven-name list are dropped, and an empty selection falls back to all seven. STUD does NOT verify that any answer is true, accurate, or complete, that evidence_refs point to real documents, that the questions match what a specific investor will ask, or that meeting the length floor makes an answer substantive. company, round_stage, company_context, and known_gaps are operator context only and never change the machine verdict.

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