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STRONG-method pitch script
A structured, step-complete pitch script you can rehearse, with every STRONG element present, in order, and sized to your word limits.
You receive: A JSON object { dealName, audience, steps: [ { stepKey, stepName, body, wordCount } x5 ], totalWordCount } where stepKey is one of the five STRONG keys (setting_the_frame, telling_the_story, revealing_the_intrigue, offering_the_prize, getting_a_decision), stepName is the exact canonical name for that key ('Setting the frame', 'Telling the story', 'Revealing the intrigue', 'Offering the prize', 'Getting a decision', case-sensitive, verbatim), body is the spoken-script text for that step, and dealName and audience are non-empty.
Part of Pitch Investors
Example
A sample of what this play produces. Your result is generated for your inputs.
Deal Name
STUD.com
Audience
Pre-seed investors in a partner meeting
Steps
| Step Key | Step Name | Body | Word Count |
|---|---|---|---|
| setting_the_frame | Setting the frame | Thank you for the twenty minutes. I will use all of it, and at the end I will ask you for a specific decision, so you know exactly where this is going. I am Dan Schmitz, founder of STUD.com, and one frame governs everything you are about to hear: STUD is a living system for a venture. It knows the business, does the work, and learns from every result. This is not a concept deck. The product is live in soft launch today with 265 plays across 14 playbooks, and the script I am speaking from right now was produced by one of those plays, running on our own workspace facts. Hold every claim that follows against that frame. | 119 |
| telling_the_story | Telling the story | Here is the story behind the product. AI can produce real knowledge work now, but getting that work out of it is still manual craft: you have to know what to ask, phrase it well, re-explain your business in every session, and carry the output somewhere by hand before it goes stale. Every session starts from zero, so nothing compounds. I hit that wall in my own ventures, the same facts retyped into a blank prompt box every morning, the same outputs going stale in documents. So I built the missing layer: 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. A fact stated once flows into every run, and every deliverable passes its play's acceptance check before it lands. Then I turned it on itself. STUD's positioning, its pricing model, and this pitch script all came out of its own catalog. The company you are evaluating runs on the product you are evaluating. | 178 |
| revealing_the_intrigue | Revealing the intrigue | Now the part that took me longest to see. The market treats AI as a faucet: turn it on, take the output, and everything resets. External estimates put the AI agents market at about $8 billion in 2025, growing 42 to 50 percent a year, and nearly all of that spend still flows to tools that forget the customer between sessions. The intrigue is what happens when the system remembers. Every run leaves the workspace knowing more than it did before: one play's result feeds the next play's intake, and the customer never restates a fact twice. Switching cost stops being a pricing trick and becomes the accumulated operating knowledge of the venture itself. The longer a venture runs on STUD, the more expensive the blank prompt box looks next to it. Our competition, general-purpose chat assistants, freelance marketplaces, and static documentation tools, each holds one piece of this. None of them closes the loop. So here is the question I want to leave open for a moment: what does the market look like when a venture's whole operating memory lives in the same place the work gets done? | 189 |
| offering_the_prize | Offering the prize | Here is the business. Buyers purchase prepaid credit packs: 500 credits for $20, 1,000 for $40, 2,500 for $100, and 5,000 for $200, with bonus credits on the larger packs and annual prepay packs at $240 and $720. A play costs 250 to 1,000 credits, which is $10 to $40 per run, credits are pegged at $0.04, and purchased credits never expire. Credits are spent only on an accepted run. Gross margin models at 85 to 90 percent at launch prices. A marketplace take rate on third-party supply comes in a later phase, so the pack business is built to stand on its own. Our launch model, and I flag every figure in it as a model rather than a result: 200 signups at launch, 40 activated buyers in month one, 25 still running plays in month three, an average first pack of about $42, an active buyer spending about $25 a month on packs, and content-led acquisition at a modeled blended cost of about $40 per customer. We are pre-revenue, in soft launch, and I would rather hand you honest model assumptions than dressed-up numbers. The prize on the table is early ownership of the system ventures run on, at the point where the catalog, 265 plays across 14 playbooks and growing, is already live and the compounding loop is already turning. | 223 |
| getting_a_decision | Getting a decision | So here is the decision I am asking for. We are raising a pre-seed round with a target of $1,000,000, and I want to be precise about status: that figure is a target, not a committed round, and the entity is in place: STUD is a registered trade name of TELEVORT INC, a Delaware corporation, so funds would close into that company. The raise goes to three things: growing the catalog, opening paid fulfillment beyond the soft launch, and content-led acquisition. What I am asking from you today is not a wire. It is a clear next step: tell me by the end of next week whether you want to lead or pass. If you lead, we schedule diligence, and I will run the diligence materials out of the same workspace you have been hearing about. If you pass, say so plainly and we have both saved a month. Either answer moves this forward, and that is the decision I came here to get. | 164 |
Total Word Count
873
Opens soon
STUD opens runs for everyone soon, on every play at once.
Scope of the check
What's verified: STUD verifies that the submission parses as a JSON object within a 200,000-character size bound, that the word-limit settings sit in a sane range (minimum words per step between 1 and 1,000, total cap between 100 and 20,000 and at least five times the per-step minimum), that dealName and audience are present as non-empty strings, that all five STRONG steps appear exactly once in the canonical order, that each step's stepName equals the exact canonical string for its stepKey by case-sensitive match ('Setting the frame', 'Telling the story', 'Revealing the intrigue', 'Offering the prize', 'Getting a decision': any other spelling, casing, or wording is rejected), that every step body is a non-empty string meeting your per-step minimum word count with no bracketed tokens or placeholder phrases (todo, lorem, 'your deal here'), and that each step's wordCount and the totalWordCount are accurate with the total at or under your cap. STUD does NOT judge whether the pitch is persuasive, whether the frame will hold in the room, or whether the script actually reflects the deal facts you provide in intake (what it does, the problem, and the ask are operator context only, not enforced criteria).
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