StakeMap

Inspiration

Early-stage founders often focus on the product before identifying the people, organisations, channels, and dependencies that can make the idea work—or fail. We built StakeMap to make those hidden relationships visible before founders spend time and money building.

Instead of relying on a fixed list of generic business personas, StakeMap starts with a founder’s actual idea and asks: who can influence adoption, delivery, trust, access, payment, and the economics of this specific business?

What it does

StakeMap is a founder-learning workshop that turns a rough business idea into an editable stakeholder panel.

A founder submits an idea, target customer, market, and business model. StakeMap then:

1. Generates 3–5 idea-specific stakeholder profiles.
2. Lets the founder review, remove, or add a missing stakeholder.
3. Runs independent synthetic stakeholder viewpoints in parallel.
4. Identifies material conflicts between stakeholder incentives.
5. Produces a GO, PIVOT, or HOLD learning recommendation and a concrete 48-hour experiment.

The goal is not to predict whether a business will succeed. It is to turn assumptions into the cheapest next piece of evidence a founder can collect.

How we built it

StakeMap is built with Next.js, React, TypeScript, Tailwind CSS, Zod, and the OpenAI JavaScript SDK.

The Stakeholder Architect creates validated stakeholder specifications from the founder brief. Each founder-approved stakeholder becomes a runtime panelist with its own focused prompt. Those panelists run in parallel, then their validated viewpoints are passed to a Conflict Mapper and Gatekeeper.

All OpenAI calls run server-side, so API credentials never reach the browser. We use strict Zod schemas to validate requests and model responses at every stage. The app also includes a pre-generated local demo scenario that can be explored without an API key or live model call, and saves completed reports locally in the browser.

Challenges we ran into

The first challenge was avoiding generic stakeholder maps. A fixed “customer, finance, operations” panel would not reflect the real dependencies of a particular idea, so we designed the stakeholder panel to be generated dynamically and made it editable before it runs.

The second challenge was reliability in a multi-step AI workflow. One unavailable panelist should not discard every completed viewpoint, so StakeMap preserves successful analyses when possible and only creates a Gate Report when there is enough validated panel evidence.

We also had to be careful not to overstate what AI can know. StakeMap is designed to treat every output as a synthetic hypothesis rather than market research, legal advice, financial advice, or a prediction of success.

Accomplishments that we're proud of

We are proud that StakeMap creates a genuinely idea-specific panel instead of wrapping a generic chatbot in a founder interface.

We are also proud of the workflow’s educational focus: the final output does not simply give advice. It highlights stakeholder tensions, identifies a critical assumption, and gives the founder a small, falsifiable experiment to run next.

Finally, we built the experience with practical safeguards: server-only API access, structured outputs, strict validation, partial-result recovery, clear error states, and an offline-ready demo report.

What we learned

The most useful AI output is often not a confident answer—it is a better question. StakeMap is strongest when it helps founders recognise uncertainty and speak with real people rather than treating generated text as evidence.

We also learned that reliability and transparency matter as much as generation quality. Structured output validation, clear labels for demo content, and honest limitations make the tool more useful and trustworthy.

What's next for StakeMap

Next, we want to make StakeMap more useful across repeated founder-learning sessions. Possible directions include:

• Comparing reports across multiple idea iterations.
• Letting founders record real interview evidence alongside each stakeholder hypothesis.
• Adding experiment templates for common validation goals such as trust, willingness to pay, supply participation, and channel access.
• Improving stakeholder editing so founders can refine incentives, power, and decision questions directly.
• Creating facilitator views for incubators, classrooms, and entrepreneurship programmes.
• Adding stronger report export and sharing options while keeping founder data private.

The long-term goal is to help more early-stage founders replace confident assumptions with fast, responsible learning.

Built With

• api
• css
• docker
• eslint
• fly.io
• gpt-5.6
• javascript
• next.js
• node.js
• npm
• react
• sdk
• tailwind
• typescript
• vitest
• zod