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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 growth teams running repeated tests use it to solve "teams rerun failed experiments because learning is hard to retrieve"?
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 repeated proposals and evidence retrieval time. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Costed pilot: Archive retrieval; no automated causal conclusions. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: index hypotheses; link results and limitations. Support the third task through an assisted review queue: match new proposals. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of cited experiment learning brief. 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 cited experiment learning brief, automate surface contradictory findings; flag changed contexts; export learning briefs. 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. Archive retrieval; no automated causal conclusions.

What the build depends on. Permission-filtered retrieval, document versioning, a question evaluation set, staff handoff and a source update process. Reliability depends on source quality and scope. Obtain representative authorized inputs, an agreed review rubric and a buyer-side owner. Specific scope: Archive retrieval; no automated causal conclusions.

Pricing to test

Test USD 500-2,000 setup plus USD 150-600 monthly for one defined source collection and usage allowance. Price multi-location deployments and specialist support separately. Validate willingness to pay; these are hypotheses. For this buyer, package the first sale around index thirty historical tests and the defined cited experiment learning brief. Record actual review effort before offering a recurring allowance. The commercial pilot fee is distinct from the platform development budget.

Message to test

Retrieve relevant past learning without treating correlation as proof. Demonstrate the result with index thirty historical tests for growth teams running repeated tests. Use a concrete before-and-after example without promising unmeasured savings.

Where to find buyers

Growth communities and experimentation consultants

Lead magnet

Index thirty historical tests

The first 30 days

1. Week 1: interview five prospective buyers from growth teams running repeated tests and inspect how they handle teams rerun failed experiments because learning is hard to retrieve.
2. Week 2: prepare index thirty historical tests using authorized or synthetic material.
3. Week 3: share the demonstration through growth communities and experimentation consultants and seek one bounded paid pilot.
4. Week 4: measure repeated proposals and evidence 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 index thirty historical tests and deliver cited experiment learning brief. Compare repeated proposals and evidence 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

Repeated proposals and evidence retrieval time

Retention and expansion

Build repeat use around cited experiment learning brief. Save approved configurations and review decisions with permission, revisit unresolved exceptions and show progress on repeated proposals and evidence 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 maintained domain knowledge collection, realistic evaluation questions, useful escalation paths and integrations in the customer’s daily work. For this concept, accumulate permissioned examples and reviewer corrections around retrieve relevant past learning without treating correlation as proof. The durable asset is reliable task-specific execution and trusted customer configuration, not access to a general-purpose AI model.

Alternatives and positioning

Manual search, static FAQs, general chat tools and support or intranet suites. Position this concept around retrieve relevant past learning without treating correlation as proof. 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

Document ingestion, retrieval and generation, source maintenance, support, evaluation and staff time handling unresolved cases. Initial validation additionally budgets for experimentation specialist review. Track model usage, storage, reviewer minutes, exception handling and customer support per accepted deliverable.