Research question boundary workshop

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 graduate research advisors use it to solve "projects lack a clear boundary between questions"?
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 unresolved scope questions; reviewer correction minutes; buyer acceptance and repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Costed pilot: One organization, one defined input format and one representative pilot batch using researcher-approved proposals and scope limits. Professional judgment, physical inspections, live external actions and production certification remain outside this prototype. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: separate research questions; map dependencies. Support the third task through an assisted review queue: flag untestable breadth. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of advisor-reviewed scope 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 advisor-reviewed scope brief, automate link proposed outputs to original source records; capture reviewer corrections and approval; export a versioned advisor-reviewed scope brief. 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 organization, one defined input format and one representative pilot batch using researcher-approved proposals and scope limits. Professional judgment, physical inspections, live external actions and production certification remain outside this prototype.

What the build depends on. A defensible calculation model, explicit units, constraint validation and representative boundary tests. Advanced forecasting or optimization needs adequate historical data. Obtain representative authorized inputs, an agreed review rubric and a buyer-side owner. Specific scope: One organization, one defined input format and one representative pilot batch using researcher-approved proposals and scope limits. Professional judgment, physical inspections, live external actions and production certification remain outside this prototype.

Pricing to test

Test USD 750-3,000 for a scoped planning setup and review, then USD 200-900 monthly for refreshes within agreed complexity. Data integration and optimization are separately scoped. All ranges are hypotheses. For this buyer, package the first sale around prepare a sample advisor-reviewed scope brief from a small authorized set of researcher-approved proposals and scope limits and the defined advisor-reviewed scope brief. Record actual review effort before offering a recurring allowance. The commercial pilot fee is distinct from the platform development budget.

Message to test

Explicit boundaries before data collection. Demonstrate the result with prepare a sample advisor-reviewed scope brief from a small authorized set of researcher-approved proposals and scope limits for graduate research advisors. Use a concrete before-and-after example without promising unmeasured savings.

Where to find buyers

University research offices and scientific data communities

Lead magnet

Prepare a sample advisor-reviewed scope brief from a small authorized set of researcher-approved proposals and scope limits

The first 30 days

1. Week 1: interview five prospective buyers from graduate research advisors and inspect how they handle projects lack a clear boundary between questions.
2. Week 2: prepare prepare a sample advisor-reviewed scope brief from a small authorized set of researcher-approved proposals and scope limits using authorized or synthetic material.
3. Week 3: share the demonstration through university research offices and scientific data communities and seek one bounded paid pilot.
4. Week 4: measure unresolved scope questions; reviewer correction minutes; buyer acceptance and repeat purchase, 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 prepare a sample advisor-reviewed scope brief from a small authorized set of researcher-approved proposals and scope limits and deliver advisor-reviewed scope brief. Compare unresolved scope questions; reviewer correction minutes; buyer acceptance and repeat purchase 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

Unresolved scope questions; reviewer correction minutes; buyer acceptance and repeat purchase

Retention and expansion

Build repeat use around advisor-reviewed scope brief. Save approved configurations and review decisions with permission, revisit unresolved exceptions and show progress on unresolved scope questions; reviewer correction minutes; buyer acceptance and repeat purchase. 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 validated domain model, customer-approved constraints and forecast or decision history that improves practical planning. For this concept, accumulate permissioned examples and reviewer corrections around explicit boundaries before data collection. The durable asset is reliable task-specific execution and trusted customer configuration, not access to a general-purpose AI model.

Alternatives and positioning

Spreadsheets, planners, specialist forecasting tools and existing scheduling or configuration software. Position this concept around explicit boundaries before data collection. 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

Data preparation, domain modeling, validation, scenario computation, reviewer support and ongoing assumption maintenance. Initial validation additionally budgets for representative sample preparation, interviews with graduate research advisors, and qualified domain review of advisor-reviewed scope brief. Track model usage, storage, reviewer minutes, exception handling and customer support per accepted deliverable.