Research consent scope registry

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 university research data stewards use it to solve "researchers cannot easily tell which data uses participants approved"?
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 unclear use permissions and review time. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Costed pilot: Metadata only; no independent ethics or legal clearance. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: extract stated use limits; link consent versions. Support the third task through an assisted review queue: flag ambiguous scope. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of steward-approved consent scope register. 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 steward-approved consent scope register, automate track withdrawal procedures; record steward decisions; export permission summaries. 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. Metadata only; no independent ethics or legal clearance.

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: Metadata only; no independent ethics or legal clearance.

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 synthetic study dataset and the defined steward-approved consent scope register. Record actual review effort before offering a recurring allowance. The commercial pilot fee is distinct from the platform development budget.

Message to test

Keep dataset use restrictions tied to consent versions. Demonstrate the result with map one synthetic study dataset for university research data stewards. Use a concrete before-and-after example without promising unmeasured savings.

Where to find buyers

Research data offices and university libraries

Lead magnet

Map one synthetic study dataset

The first 30 days

1. Week 1: interview five prospective buyers from university research data stewards and inspect how they handle researchers cannot easily tell which data uses participants approved.
2. Week 2: prepare map one synthetic study dataset using authorized or synthetic material.
3. Week 3: share the demonstration through research data offices and university libraries and seek one bounded paid pilot.
4. Week 4: measure unclear use permissions and review 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 synthetic study dataset and deliver steward-approved consent scope register. Compare unclear use permissions and review 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

Unclear use permissions and review time

Retention and expansion

Build repeat use around steward-approved consent scope register. Save approved configurations and review decisions with permission, revisit unresolved exceptions and show progress on unclear use permissions and review 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 keep dataset use restrictions tied to consent versions. 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 keep dataset use restrictions tied to consent versions. 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 ethics and data-steward review. Track model usage, storage, reviewer minutes, exception handling and customer support per accepted deliverable.