{"slug":"negative-result-study-archive","name":"Negative-result study archive","category":"Science and Research","customer":"Research groups and laboratory consortia","problem":"Failed experiments disappear and are unnecessarily repeated.","value":"For research groups and laboratory consortia, turn authorized experiment summaries and researcher annotations into searchable negative-result evidence archive. Address this specific problem: failed experiments disappear and are unnecessarily repeated. The aim: recover what was tried and why it was inconclusive. The pilot tests whether that benefit holds up against reviewer effort and real operating costs.","format":"Searchable structured library and data stewardship console","screens":"Key screens: Study catalog, Outcome context, Search evidence. Use a searchable table or visual gallery with filters for the domain’s important attributes. Open each item into a detail drawer containing source records, ownership and history. Put proposed merges and field changes in a separate review queue. Provide a preview before any bulk export. Open with study catalog; move into outcome context for the detailed task; finish in search evidence for review and handoff. Show the source record, uncertainty and approval status beside each proposed output.","functionality":"1. Record unsuccessful attempts. 2. Link methods. 3. Capture boundary conditions. 4. Flag incomplete metadata. 5. Match related questions. 6. Export learning briefs.","workflow":"The buyer creates a project, supplies authorized experiment summaries and researcher annotations, and confirms scope and access. The working sequence is: 1. Record unsuccessful attempts. 2. Link methods. 3. Capture boundary conditions. 4. Flag incomplete metadata. 5. Match related questions. 6. Export learning briefs. Users correct extracted facts, resolve flagged uncertainties and approve the final searchable negative-result evidence archive before use. Retain source links and a version history for the next cycle.","ai":"Classify reported outcomes without interpreting absence of effect as proof. Keep model suggestions separate from verified facts. Link factual outputs to authorized input evidence and show missing information explicitly. Use deterministic checks for counts, dates, identifiers and arithmetic where applicable. A designated reviewer validates consequential outputs and signs off the delivered result.","inputs":"Authorized experiment summaries and researcher annotations","deliverables":"Searchable negative-result evidence archive","admin":"Record ownership, access permissions, change proposals, original-value retention, version history, review dates, bulk import/export and duplicate resolution. Include organization-scoped access, named project owners, review queues, usage limits, export history and retention settings. Never reuse private customer material for other accounts without permission.","mvp":"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.","expansion":"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.","usp":"Recover what was tried and why it was inconclusive.","defensibility":"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":"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.","revenue":"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.","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.","integrations":"Authorized datasets, papers, protocols, code and research records. Source systems, catalog exports and cloud file storage. Start with reversible CSV or file imports and validate identifiers before any direct writes. Begin with uploads and exports of authorized experiment summaries and researcher annotations. Any named system or connector is a candidate requiring current access and compatibility checks; no live connection is included by default.","dependencies":"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.","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.","plan30":"Week 1: interview five prospective buyers from research groups and laboratory consortia and inspect how they handle failed experiments disappear and are unnecessarily repeated. Week 2: prepare index twenty internal attempts using authorized or synthetic material. Week 3: share the demonstration through lab manager networks and research collaborations and seek one bounded paid pilot. 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.","metrics":"Retrieval usefulness and documented repeat avoidance","channels":"Lab manager networks and research collaborations","leadMagnet":"Index twenty internal attempts","message":"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.","retention":"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.","controls":"Preserve original data, methods, citations and research limitations. Use researcher review and document every substantive transformation. Private pilot archive; no scientific validity scoring. Require appropriate access and publication approval. Preserve source material, label AI drafts and make corrections traceable. Measure false positives and missed cases alongside speed.","crossSector":"Education; Executives and Strategy","fn":["Record unsuccessful attempts","Link methods","Capture boundary conditions","Flag incomplete metadata","Match related questions","Export learning briefs"],"sc":{"opp":8,"pain":7,"feas":7,"now":7},"phases":[{"name":"MVP","scope":"One buyer segment, one recurring use case; first modules: record unsuccessful attempts; link methods. Manual review in the loop.","time":{"days":5,"label":"5 days"},"usd":13000},{"name":"Paid pilot","scope":"Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.","time":{"days":6,"label":"6 days"},"usd":13000},{"name":"Full product","scope":"Self-serve onboarding, billing, monitoring and the wider integration set.","time":{"days":11,"label":"2 weeks"},"usd":18000}],"running":[{"stage":"MVP and paid pilot","note":"about 3 customers","hosting":[30,60],"ai":[50,100],"total":[80,160]},{"stage":"Full product","note":"about 50 customers","hosting":[110,210],"ai":[350,700],"total":[460,910]}],"total":44000,"complexity":0.58,"days":22,"shot":true,"demo":true}