Product assumption traceability graph

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will product discovery teams use it to solve "requirements lose the research assumptions that originally justified them"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree the acceptance criteria, input limits and reviewer responsibilities before starting.
4. Measure, then decide. Track unsupported decisions and retrieval time. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Costed pilot: One feature area; no invented confidence scores. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: extract stated assumptions; link feature decisions. Support the third task through an assisted review queue: attach supporting research. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of product assumption evidence graph. Authentication, account isolation, deletion controls and basic operational logging are included. Specialized production certification, live write integrations and broader rollout are not included unless explicitly stated.

After the MVP. After paying customers repeatedly accept product assumption evidence graph, automate flag contradictory findings; track confidence reviews; export evidence maps. Add one tested read integration, reusable customer configuration and scheduled repeat delivery. Increase supported formats or teams only when evaluation cases and reviewer capacity cover the new scope. One feature area; no invented confidence scores.

What the build depends on. Stable identifiers, an agreed data schema, reversible imports, mapping review and source ownership. Data quality work can exceed model development effort. Obtain representative authorized inputs, an agreed review rubric and a buyer-side owner. Specific scope: One feature area; no invented confidence scores.

Pricing to test

Test USD 500-2,500 for one collection cleanup and launch, followed by USD 100-500 monthly for maintenance within agreed record limits. Larger migrations and complex rights management are separately scoped. Prices are hypotheses. For this buyer, package the first sale around map one feature's evidence chain and the defined product assumption evidence graph. Record actual review effort before offering a recurring allowance. The commercial pilot fee is distinct from the platform development budget.

Message to test

Trace each product decision to its still-valid assumptions. Demonstrate the result with map one feature's evidence chain for product discovery teams. Use a concrete before-and-after example without promising unmeasured savings.

Where to find buyers

Product operations communities and discovery coaches

Lead magnet

Map one feature's evidence chain

The first 30 days

1. Week 1: interview five prospective buyers from product discovery teams and inspect how they handle requirements lose the research assumptions that originally justified them.
2. Week 2: prepare map one feature's evidence chain using authorized or synthetic material.
3. Week 3: share the demonstration through product operations communities and discovery coaches and seek one bounded paid pilot.
4. Week 4: measure unsupported decisions and retrieval time, review delivery effort and ask for a repeat purchase. This is a validation schedule, not a promise that the full product can be built in thirty days.

Paid pilot

Agree the acceptance criteria, input limits and reviewer responsibilities before starting. Run map one feature's evidence chain and deliver product assumption evidence graph. Compare unsupported decisions and retrieval time with the buyer's current process on comparable cases; include corrections, missed issues and reviewer time. Seek payment and repeat use. Stop or revise the scope if data access, accuracy or unit economics fail.

Success metrics

Unsupported decisions and retrieval time

Retention and expansion

Build repeat use around product assumption evidence graph. Save approved configurations and review decisions with permission, revisit unresolved exceptions and show progress on unsupported decisions and retrieval time. Offer a recurring volume allowance after repeat demand; expand to adjacent tasks only when the buyer asks and delivery quality remains acceptable.

Why clients would pick it

A useful niche taxonomy, customer-approved mappings and accumulated correction history that improve retrieval and reduce repeated cleanup. For this concept, accumulate permissioned examples and reviewer corrections around trace each product decision to its still-valid assumptions. The durable asset is reliable task-specific execution and trusted customer configuration, not access to a general-purpose AI model.

Alternatives and positioning

Spreadsheets, shared folders, existing asset or information management systems and manual data cleanup. Position this concept around trace each product decision to its still-valid assumptions. Compare it against the customer's current process on the same representative task. This is proposed differentiation; no exhaustive competitor study or uniqueness claim has been established.

Main delivery costs

Import cleanup, extraction, storage, indexing, steward review, duplicate investigation and recurring source updates. Initial validation additionally budgets for product researcher review. Track model usage, storage, reviewer minutes, exception handling and customer support per accepted deliverable.