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Multi-stakeholder value proposition canvases
A multi-stakeholder VPC set with exactly one canvas per role you need to win, each meeting your jobs and pains minimums with at least one gain, and no two canvases sharing an identical profile, ready for a complex B2B sale.
You receive: JSON with: stakeholderCanvases (array of {stakeholderRole: string, jobs: [string], pains: [string], gains: [string]}), exactly one canvas per requested role, within the size bounds (at most 25 canvases, 50 entries per list, 500 characters per entry).
Part of Win Market
Example
A sample of what this play produces. Your result is generated for your inputs.
Stakeholder Canvases
- Stakeholder Role
- end-user
- Jobs
- Get marketing, finance, and product deliverables produced without writing prompts from scratch
- Keep the team's business facts current so every run starts from real context
- Turn a rough weekly to-do list into finished, structured artifacts
- Pains
- Re-explaining the business in every AI chat session, then watching the output go stale in a doc
- Blank-prompt paralysis: knowing AI could do the work but not how to ask for it
- Hand-carrying AI output between tools before it is usable
- Gains
- Pick a play from a catalog of 265, the intake fills itself from workspace facts, and the result lands structured
- Every deliverable passes its play's acceptance check before it counts
- Each run leaves the workspace knowing more, so the next run starts further ahead
- Stakeholder Role
- economic-buyer
- Jobs
- Get marketing, finance, product, and engineering work done while keeping headcount flat
- Justify tool spend against a pre-seed budget where every subscription competes with runway
- Predict monthly cost of knowledge work instead of scoping freelance projects one at a time
- Pains
- Freelance marketplaces bill per project with unpredictable scope and turnaround
- Paying for AI seats the team underuses because getting good output is still manual craft
- No way to see what a given piece of work will cost before commissioning it
- Gains
- Plans from $20/mo (500 credits), with plays costing 10 to 40 credits ($0.40 to $1.60) each, so spend is legible per unit of work
- One subscription replaces a patchwork of one-off freelance engagements for routine knowledge work
- Stakeholder Role
- decision-maker
- Jobs
- Standardize how the team gets AI work done instead of everyone freelancing their own prompts
- Adopt AI agents without betting the roadmap on unreviewable output
- Keep institutional knowledge in a system, not in individual chat histories
- Pains
- Every AI session starts from zero, so nothing the team learns compounds
- No acceptance bar on AI output: quality depends on whoever prompted that day
- Vendor tools that demo well but leave no durable asset when the subscription ends
- Gains
- A living system: the workspace holds what the business knows, plays do the work, and results feed the next run
- An acceptance check screens every deliverable before it lands, so the floor is enforced by the system, not by review effort
Opens soon
STUD opens runs for everyone soon, on every play at once.
Scope of the check
What's verified: STUD verifies that the deliverable parses as JSON, contains a non-empty stakeholderCanvases array within the disclosed size bounds (at most 25 canvases, 50 entries per list, 500 characters per entry), includes exactly one canvas for each stakeholder role you name, meets your minimum jobs and pains per required role (defaults 2 and 2, counting only non-empty entries; values outside 1 to 10 are clamped into that range), lists at least one non-empty gain per required role, and confirms no two canvases share an identical jobs, pains, and gains profile. STUD does NOT verify that the jobs, pains, or gains are accurate, relevant, or specific to your product: the product or offer field and your stakeholder notes are context for the operator and never affect the machine verdict.
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