Shared research instrument queue optimizer

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 core facilities use it to solve "instrument capacity is lost to incompatible setup sequences"?
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 quality and outcome thresholds before the pilot using this measure: Accepted instrument hours gained minus coordination and setup costs.
4. Measure, then decide. Track accepted instrument hours gained minus coordination and setup costs; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: No autonomous instrument operation or safety decisions. Implement one approved input format, a bounded representative case set and the first two task modules: group compatible runs; simulate queue policies. Support the third module with operator review: compare setup and access tradeoffs. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around facility-professional-approved scheduling experiment. Retain the explicit scope boundary: No autonomous instrument operation or safety decisions.

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 cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: No autonomous instrument operation or safety decisions.

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. Package the initial sale as one bounded facility-professional-approved scheduling experiment. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Increase useful instrument access without extending operating hours. Demonstrate a concrete facility-professional-approved scheduling experiment using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

University core facilities professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample facility-professional-approved scheduling experiment from a small authorized input set, with a transparent calculation of accepted instrument hours gained minus coordination and setup costs and no promised savings.

The first 30 days

1. Week 1: interview five university core facilities and inspect a recent example of instrument capacity is lost to incompatible setup sequences.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted instrument hours gained minus coordination and setup costs, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Accepted instrument hours gained minus coordination and setup costs. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Accepted instrument hours gained minus coordination and setup costs; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs facility-professional-approved scheduling experiment. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A validated domain model, customer-approved constraints and forecast or decision history that improves practical planning. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for university core facilities. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Spreadsheets, planners, specialist forecasting tools and existing scheduling or configuration software. Compare this product with the buyer's present method on accepted instrument hours gained minus coordination and setup costs. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Data preparation, domain modeling, validation, scenario computation, reviewer support and ongoing assumption maintenance. Additional initial validation requires representative authorized sample preparation, buyer interviews, qualified domain review and bounded validation of facility-professional-approved scheduling experiment. Track cost per accepted output, including correction work, unsuccessful cases and support.