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Feature-advantage-value messaging ladder mapped to personas
The buyer gets a messaging framework JSON: one feature-advantage-value row for every feature they list, every distinct value grouped under exactly one named theme, a matrix with exactly one cell for every persona and theme pairing (each stating a use case and a benefit), and summaries of exactly one, two, and five sentences.
You receive: A JSON object with a ladder array (one row per listed feature: feature, advantage, value), a themes array (objects with a name and a values list), a matrix array (one cell per persona and theme pair: persona, theme, use_case, benefit), and summaries {one_sentence, two_sentence, five_sentence}.
Part of Launch Product
Example
A sample of what this play produces. Your result is generated for your inputs.
Ladder
| Feature | Advantage | Value |
|---|---|---|
| Workspace fact store | You state each business fact once and it flows into every run automatically | Stop re-explaining your business in every AI session |
| Play catalog of 265 ready-made plays | A browsable catalog across 14 playbooks replaces the blank prompt with a named, scoped piece of work | Know what to ask for without prompt craft |
| Prefilled intakes | Each play's short intake fills itself from your workspace facts, so starting a run takes seconds | Go from need to running work in under a minute |
| Acceptance check on every deliverable | Every deliverable passes its play's published acceptance check before it lands in your workspace | Trust what lands without proofreading every output |
| Structured deliverables that feed later runs | Results land as structured, current artifacts that later plays ingest as inputs | Every result makes the next one better |
| Credit-priced runs | Each play states its credit price up front, 250 to 1,000 credits ($10 to $40) from a prepaid pack, so cost is known before you commit | Predictable spend on every piece of work |
Themes
Compounding knowledge
- Values
- Stop re-explaining your business in every AI session
- Every result makes the next one better
Speed to done
- Values
- Know what to ask for without prompt craft
- Go from need to running work in under a minute
Confidence in the output
- Values
- Trust what lands without proofreading every output
- Predictable spend on every piece of work
Matrix
| Persona | Theme | Use case | Benefit |
|---|---|---|---|
| Solo founder | Compounding knowledge | States the venture's positioning, pricing, and customer facts once, then runs marketing and finance plays that all draw on them | The business's knowledge does the re-explaining, so a one-person company operates with institutional memory |
| Solo founder | Speed to done | Picks a launch-week play from the catalog and runs it from a prefilled intake between customer calls | Real deliverables ship in the gaps of a founder's day instead of waiting for a free afternoon |
| Solo founder | Confidence in the output | Commissions a pricing page rewrite and lets the play's acceptance check screen the result before reading it | Spends credits knowing the price up front and reviews work that already cleared a published bar |
| Small product team lead | Compounding knowledge | Keeps the team's product facts in one workspace so every teammate's runs use the same source of truth | New runs stay consistent with past decisions without a docs archaeology session |
| Small product team lead | Speed to done | Assigns routine analysis and copy work to catalog plays instead of writing prompts or specs for each request | The team stays small while marketing, finance, and product work keeps moving |
| Small product team lead | Confidence in the output | Reviews only deliverables that passed their acceptance check, with each run's credit cost visible in advance | Review time goes to judgment calls, not to catching structural mistakes, and the budget stays predictable |
| Ops and engineering lead | Compounding knowledge | Adopts AI agents through a system where prior run outputs become structured inputs to later automation | Agent adoption compounds into a knowledge base instead of a pile of one-off chat transcripts |
| Ops and engineering lead | Speed to done | Replaces ad hoc agent scripting with catalog plays that run from a short, prefilled intake | Standing up a new piece of agent-run work takes minutes, not an integration project |
| Ops and engineering lead | Confidence in the output | Routes agent work through plays whose acceptance checks define exactly what a passing deliverable contains | Agent output enters production processes with a machine-checked structure and a known per-run cost |
Summaries
- One sentence
- STUD is a living system for your venture: it knows your business, does the work, and learns from every result.
- Two sentence
- STUD gives your venture a living system: a workspace that holds what the business knows, a catalog of ready-made plays that AI agents run from a short prefilled intake, and results that land structured 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.
- Five sentence
- AI can produce real knowledge work, but getting that work out of it is still manual craft, and nothing compounds when every session starts from zero. STUD replaces the blank prompt with a catalog of 265 ready-made plays across 14 playbooks. Your workspace holds what the business knows, so each play's short intake fills itself and a fact stated once reaches everything you run. Results land as structured deliverables that stay current and feed later runs, and every deliverable passes its play's acceptance check before it counts. The system knows your business, does the work, and learns from every result, so it is worth more to your venture every week you use it.
Opens soon
STUD opens runs for everyone soon, on every play at once.
Scope of the check
What's verified: STUD verifies structure and coverage: the deliverable parses as a JSON object of at most 120,000 characters; your inputs are in range (3 to 20 unique features, 1 to 8 unique personas, 2 to 6 themes); the ladder has exactly one row per feature you listed, each row stating an advantage and a value of at least 3 words that differ from the feature and from each other; there are exactly the requested number of uniquely named theme objects; every distinct ladder value is grouped under exactly one theme and themes contain only ladder values; the matrix has exactly one cell for every persona and theme pairing, each cell stating a use case and a benefit of at least 3 words; and the summaries contain exactly 1, 2, and 5 sentences, counted by ., ! and ? marks. STUD does NOT verify that the advantages, values, themes, use cases, benefits, or summaries are persuasive, accurate about your product, or well written; your product description and target customer are context for the operator only and are not checked.
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