Solution Database / Science and Research
Reproducibility review service
Results cannot always be regenerated from supplied materials. Independent execution evidence with exact discrepancies and environment context.
01The offer
For research teams preparing computational publications, turn authorized code, data, environment definitions and expected outputs into reproducibility review report. Address the recurring problem: results cannot always be regenerated from supplied materials. The value hypothesis is a more complete, reviewable deliverable with less repeated preparation; the pilot must establish whether that benefit is real.
- For
- Research teams preparing computational publications
- Takes in
- Authorized code, data, environment definitions and expected outputs
- Delivers
- Reproducibility review report
- Message
- Reproducibility review service for research teams preparing computational publications. Independent execution evidence with exact discrepancies and environment context. Demonstrate the claim through a reproduction audit of one reported figure.
- Lead magnet
- A reproduction audit of one reported figure
02How it works
- Inspect instructions
- Recreate approved environments
- Run supplied analyses
- Compare outputs
- Document deviations
- Suggest reproducibility fixes
Workflow
Agree review criteria, ingest a sample, generate candidate findings, inspect supporting evidence, let reviewers confirm or dismiss each item, assign corrections, and recheck the affected material. Start with authorized code, data, environment definitions and expected outputs and finish with reproducibility review report.
AI and people
Propose possible inconsistencies, omissions and rubric matches. Combine extraction with deterministic checks where rules are explicit. Reviewers make the final judgment. Keep false positives and missed cases visible during evaluation.
Screens
Key screens: Execution checklist, output comparison, issue evidence. Open on a review queue ordered by reviewer-selected priorities. Show each finding beside the original evidence and applicable rule. Provide accept, dismiss and needs-information controls with reasons. A separate report view summarizes confirmed findings and unresolved items, not raw AI flags. In this product, the first view is execution checklist, followed by output comparison and issue evidence.
Admin
Versioned review criteria, evidence links, reviewer decisions, disagreement handling, correction assignments, recheck status and exportable review history.
03Market gap
Alternatives buyers use today
Manual reviewers, checklists, generic scanning tools and specialist audit services. Differentiate on this specific proposed advantage: independent execution evidence with exact discrepancies and environment context. Test it against the buyer's current method on the same task. Competitor coverage and uniqueness have not been established.
Where this wins
A domain-specific review rubric and rights-cleared examples of confirmed defects, false alarms and reviewer reasoning. For this solution, build around independent execution evidence with exact discrepancies and environment context. This advantage requires execution and accumulated customer trust; the base model alone is not a defensible asset.
04Why now
Science and Research teams are adopting AI for exactly this kind of repeatable work, and the cost of language and vision models has dropped far enough that a narrow, reviewed workflow pays back quickly. The buyer already feels the problem: results cannot always be regenerated from supplied materials.
05Proof & signals
Channels where buyers gather: Research software communities. Metrics that prove it works: Reproduced outputs, actionable issues.
Paid pilot
Have a qualified reviewer independently assess the same sample. Compare confirmed findings, false alarms and omissions. Repeat on unseen material before agreeing recurring volume. For this solution, use authorized code, data, environment definitions and expected outputs and evaluate reproducibility review report. Agree success thresholds with the buyer before starting; collect a baseline for reproduced outputs, actionable issues. A positive signal is payment and repeat use with acceptable quality and delivery cost, not a favorable demo reaction alone.
06Execution plan
MVP
Begin with research teams preparing computational publications and one recurring use case. Build the first two modules: inspect instructions; recreate approved environments. Provide operator assistance for the third module: run supplied analyses. Deliver reproducibility review report through a manual review queue. Perform other necessary full-scope functions manually during the pilot. Include all applicable access, accuracy and professional-review controls from the start.
First 30 days
Week 1: interview five prospective buyers in this segment: research teams preparing computational publications. Ask to see a recent example of the problem and their current process. Week 2: prepare this demonstration using authorized or synthetic material: a reproduction audit of one reported figure. Week 3: present it through research software communities and seek one narrowly scoped paid pilot. Week 4: review reproduced outputs, actionable issues, total delivery effort and a concrete renewal decision before increasing scope.
After the pilot
After paid pilots establish value, automate the remaining modules: compare outputs; document deviations; suggest reproducibility fixes. Add one validated source integration, reusable customer configuration and recurring delivery. Expand to additional teams, document formats or languages only after testing the new scope.
Retention
Offer recurring reviews and rechecks of previously confirmed issues. Expand document or case types after validating the new rubric with qualified reviewers.
Integrations
Authorized datasets, papers, protocols, code and research records. Source repositories, task trackers and report exports. Keep findings as review proposals until authorized owners accept the resulting actions. These are candidate integration categories, not verified supported connectors.
07Investment and running costs
| Phase | Scope | Time | Budget |
|---|---|---|---|
| MVP | One buyer segment, one recurring use case; first modules: inspect instructions; recreate approved environments. Manual review in the loop. | 3 days | $7,000 |
| Paid pilot | Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers. | 4 days | $7,500 |
| Full product | Remaining modules: compare outputs; document deviations; suggest reproducibility fixes. Self-serve onboarding, billing, monitoring and the wider integration set. | 8 days | $10,500 |
| Total | $25,000 | ||
| Running | Hosting | AI usage | Total a month |
|---|---|---|---|
| MVP and paid pilot (about 3 customers) | $30–$60 | $80–$160 | $110–$220 |
| Full product (about 50 customers) | $110–$210 | $880–$1,750 | $990–$1,960 |
Revenue model to test
Test USD 500-2,000 for a defined audit sample and report. Offer recurring review priced by reviewed items and specialist hours. Software-only access can follow a reliable reviewed service. All prices require validation.
Cost drivers
Document or media processing, model evaluation, expert review, false-positive handling, rechecks and customer-specific rubric calibration.
Safeguards
Preserve original data, methods, citations and research limitations. Use researcher review and document every substantive transformation. Validate source access and reviewer availability during the pilot. Maintain customer-level access, data deletion controls and a record of final approvals.
Take it further
Concept proposal expanded from the 315-solution conversation. Demand, pricing, differentiation, build scope and integration feasibility are hypotheses, not verified market findings. Category link is inspiration rather than evidence of business viability.