Negative-result study archive

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 research groups and laboratory consortia use it to solve "failed experiments disappear and are unnecessarily repeated"?
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 retrieval usefulness and documented repeat avoidance. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Costed pilot: Private pilot archive; no scientific validity scoring. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: record unsuccessful attempts; link methods. Support the third task through an assisted review queue: capture boundary conditions. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of searchable negative-result evidence archive. 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 searchable negative-result evidence archive, automate flag incomplete metadata; match related questions; 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. Private pilot archive; no scientific validity scoring.

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: Private pilot archive; no scientific validity scoring.

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 index twenty internal attempts and the defined searchable negative-result evidence archive. Record actual review effort before offering a recurring allowance. The commercial pilot fee is distinct from the platform development budget.

Message to test

Recover what was tried and why it was inconclusive. Demonstrate the result with index twenty internal attempts for research groups and laboratory consortia. Use a concrete before-and-after example without promising unmeasured savings.

Where to find buyers

Lab manager networks and research collaborations

Lead magnet

Index twenty internal attempts

The first 30 days

1. Week 1: interview five prospective buyers from research groups and laboratory consortia and inspect how they handle failed experiments disappear and are unnecessarily repeated.
2. Week 2: prepare index twenty internal attempts using authorized or synthetic material.
3. Week 3: share the demonstration through lab manager networks and research collaborations and seek one bounded paid pilot.
4. Week 4: measure retrieval usefulness and documented repeat avoidance, 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 twenty internal attempts and deliver searchable negative-result evidence archive. Compare retrieval usefulness and documented repeat avoidance 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

Retrieval usefulness and documented repeat avoidance

Retention and expansion

Build repeat use around searchable negative-result evidence archive. Save approved configurations and review decisions with permission, revisit unresolved exceptions and show progress on retrieval usefulness and documented repeat avoidance. 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 recover what was tried and why it was inconclusive. 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 recover what was tried and why it was inconclusive. 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 researcher curation. Track model usage, storage, reviewer minutes, exception handling and customer support per accepted deliverable.