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Build a presentation segment-and-quiz coverage map
A presentation map where every segment carries an audience-value takeaway and at least one quiz question that references that segment, with the segment count and per-segment quiz minimum enforced.
You receive: A JSON object: { talk_title, audience, segments: [ { id, name, audience_value, key_point } ], quiz: [ { question, answer, segment_id } ] }
Part of Pitch Investors
What's verified: STUD verifies the talk is segmented to the minimum, each segment has a value statement and key point, and every segment is covered by the required number of valid quiz questions referencing it. STUD does NOT judge whether the segments are well-chosen, whether the takeaways are genuinely valuable, or whether the audience would actually pass the quiz.
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Example
A sample of what this play produces. Your result is generated for your inputs.
Talk title
STUD: a living system for your venture (pre-seed pitch)
Audience
Pre-seed investors hearing STUD's raise pitch for the first time
Segments
| Id | Name | Audience value | Key point |
|---|---|---|---|
| seg-01 | The problem: AI work does not compound | Investors get a crisp diagnosis of why AI output stalls inside real businesses: getting knowledge work out of AI is still manual craft, you have to know what to ask, phrase it well, re-explain the business in every session, and carry the output somewhere by hand before it goes stale. | Every AI session starts from zero, so a venture's knowledge never accumulates into working output. |
| seg-02 | The living system: knows, does, learns | Investors see the category STUD is building rather than a feature list: a workspace that holds what the business knows, a catalog of ready-made plays that AI agents run from a short intake, and results that land structured, stay current, and feed the next play. | STUD is a living system for your venture: it knows your business, does the work, and learns from every result. |
| seg-03 | Product today: 265 plays live at soft launch | Investors get the concrete state of the product, not a roadmap: 265 plays across 14 playbooks are live at the pre-revenue soft launch, a fact stated once flows into every run, and every deliverable passes its play's acceptance check before it lands in the workspace. | The catalog is live now: 265 plays across 14 playbooks, with every result landing structured so the system's knowledge of the venture compounds. |
| seg-04 | Business model: subscriptions plus credits | Investors can price the revenue engine from published numbers: Standard $20/mo (500 credits), Pro $60/mo (1,500 credits), Ultra $180/mo (5,000 credits), annual at $15 / $45 / $140 per month, credits pegged at $0.04, and plays costing 10 to 40 credits ($0.40 to $1.60), with a marketplace take rate on third-party supply as a later phase. | Subscription plans plus one-time credit packs monetize every run today, at a modeled gross margin of 87 percent (estimated, 85 to 90 percent at launch prices). |
| seg-05 | Market and wedge: the buyer who needs the work done | Investors can size the opportunity and see the entry point: cited external estimates put the AI agents market at about $8B in 2025 growing 42 to 50 percent CAGR, and STUD's wedge is solo and early-stage founders and small product teams who need marketing, finance, product, and engineering work done while staying small, buyers the blank prompt box and freelance marketplaces both underserve. | STUD sells finished work, not tooling, into an AI agents market of about $8B in 2025 growing 42 to 50 percent a year. |
| seg-06 | The raise and the next quarter | Investors hear exactly what the money does: a $1,000,000 target raise (illustrative, not a committed round) takes the pre-seed company through entity formation (currently pending) and the opening of paid fulfillment, against an illustrative launch scenario of 200 signups, 40 activated buyers in month 1, and 25 retained at month 3 on one founder-operator's concierge capacity of 30 plays per week. | STUD is pre-seed and pre-revenue, founded by Dan Schmitz, raising toward an illustrative $1,000,000 target to open paid fulfillment. |
Quiz
| Question | Answer | Segment id |
|---|---|---|
| Why does a venture's AI output fail to compound from one session to the next? | Because every session starts from zero: the buyer has to re-explain the business each time and carry the output somewhere by hand before it goes stale. | seg-01 |
| What are the three things the STUD living system does for a venture? | It knows your business, does the work, and learns from every result. | seg-02 |
| What replaces the blank prompt box in STUD? | A catalog of ready-made plays: pick a play, the short intake fills itself from your workspace facts, and an AI agent runs it. | seg-02 |
| How large is the live STUD catalog at soft launch? | 265 plays across 14 playbooks (measured dev-seed play count, 2026-08-09). | seg-03 |
| What does one play cost a buyer in credits and in dollars? | 10 to 40 credits per play, which is $0.40 to $1.60 at the $0.04 per-credit peg. | seg-04 |
| What size and growth rate do the cited external estimates give the AI agents market? | About $8B in 2025, growing 42 to 50 percent CAGR. | seg-05 |
| What is STUD's stage and target raise as presented in this pitch? | Pre-seed and pre-revenue, with an illustrative $1,000,000 target raise, not a committed round. | seg-06 |
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