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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
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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Example
A sample of what this play produces. Your result is generated for your inputs.
Stakeholder Canvases
- Commission a play and get a finished, checkable deliverable back without babysitting the agent step by step.
- Fit STUD into an existing workflow: paste requirements in, pull the deliverable out, move to the next task.
- See exactly what a play costs in credits before committing to run it.
- A blank AI chat box gives an answer with no way to check it is right before using it downstream.
- Freelance marketplaces take days to return work and still need manual review.
- Static documentation and templates do not adapt to this specific deal or dataset.
- A play's acceptance criteria are frozen up front, so the deliverable either meets them or it does not.
- Credits are priced at $0.04 each, so the cost of a play is visible before running it.
- Control software spend across a small team without a per-seat enterprise contract.
- Compare the cost of a play against contracting a freelancer or adding headcount for the same task.
- Forecast monthly spend before committing to a plan.
- Usage-based AI tools can produce surprise bills with no visibility into what drove the cost.
- It is hard to prove ROI on AI tools when there is no record of what was verified versus produced.
- Freelance quotes vary by client and are hard to budget against.
- Standard, Pro, and Ultra plans give a fixed monthly credit budget ($20, $60, $180) before any play runs.
- Each play costs 10 to 40 credits ($0.40 to $1.60), so per-task cost is known in advance.
- Spend maps to acceptance criteria being met, since STUD settles payment only on verified delivery.
- Choose a vendor whose catalog is broad enough to cover the team's recurring knowledge-work tasks, not just one narrow use case.
- Confirm that adopting an AI tool will not introduce accuracy risk into decisions the team relies on.
- Weigh STUD against the team's default alternative: a general chat assistant with no acceptance check.
- General-purpose AI chat assistants require the requester to catch their own mistakes, with no independent check.
- A narrow point-solution tool that only covers one task type will not justify a team-wide rollout.
- Switching the team onto a new tool has a real adoption cost if the catalog turns out too thin.
- STUD's catalog spans roughly 265 plays across 14 playbooks, covering many task types instead of one.
- Each play's acceptance criteria are frozen and checked by a platform-owned judge before payment settles, shifting review burden from the requester to the criteria.
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