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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
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: prepaid credit packs | Investors can price the revenue engine from published numbers: buyers prepay for credit packs (500 credits for $20, 1,000 for $40, 2,500 for $100, 5,000 for $200, with bonus credits on the larger packs and annual prepay packs at $240 and $720), 1 credit is always 4 cents, purchased credits never expire, and a play costs 250 to 1,000 credits ($10 to $40, most plays 500 credits or $20), with a marketplace take rate on third-party supply as a later phase. | Prepaid credit packs are the revenue engine: credits are spent only on an accepted run, 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 TELEVORT INC, the Delaware corporation behind STUD, from a pre-seed soft launch through 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? | 250 to 1,000 credits per play, which is $10 to $40 at 4 cents a credit; most plays cost 500 credits, or $20. | 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 |
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 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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