
Methods transfer question generator
Show what a paper does not specify before attempted replication.
- For
- Research teams adopting published methods
- Solves
- Teams overlook practical details missing from a methods section.
- Delivers
- Researcher-reviewed methods clarification brief
- Built in
- about 4 weeks of creation time, MVP in 5 days
- Investment
- $11,500 for the MVP, $39,000 for the full product
- Run it
- Inside your business, or as part of your offer to clients
What it does
For research teams adopting published methods, turn licensed papers and investigator-defined transfer goals into researcher-reviewed methods clarification brief.
- Extract reported parameters.
- Link exact passages.
- Flag unspecified conditions.
- Compare local constraints.
- Draft clarification questions.
- Export transfer briefing.
What goes in, what comes out
- Licensed papers
- Investigator-defined transfer goals
AI drafts, people review. Research evidence workspace with reviewed deliverables.
- Researcher-reviewed methods clarification brief
How it works
The workflow
- InStart with
Licensed papers and investigator-defined transfer goals
- 1
The buyer creates a project
- 2
Supplies licensed papers and investigator-defined transfer goals
- 3
Confirms scope and access
- OutFinish with
Researcher-reviewed methods clarification brief
AI does the heavy lifting, people stay in charge
Identify reporting gaps without inventing experimental parameters. 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.
What your team sees
Key screens: Method evidence, Missing details, Author questions. Organize work by research question. Show a source library, an evidence matrix and a draft findings panel with linked quotations. Keep contradictory findings and unanswered questions visible. Allow reviewers to inspect the original context before accepting an interpretation. Open with method evidence; move into missing details for the detailed task; finish in author questions for review and handoff. Show the source record, uncertainty and approval status beside each proposed output.
Accounts and administration
Source provenance, participant consent where applicable, research questions, coding definitions, reviewer disagreements, citations and versioned conclusions. 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.
Integrations and data access
Authorized datasets, papers, protocols, code and research records. Permitted research libraries, interview recording imports, citation exports and document editors. Preserve original source metadata throughout the workflow. Begin with uploads and exports of licensed papers and investigator-defined transfer goals. Any named system or connector is a candidate requiring current access and compatibility checks; no live connection is included by default.
How we build it
We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.
- 1
Scoping call
Day 1Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.
- 2
MVP
5 daysOne buyer segment, one recurring use case; first modules: extract reported parameters; link exact passages. Manual review in the loop. Built by our AI software factory.
- 3
Paid pilot
6 daysAccounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.
- 4
Full product
2 weeksSelf-serve onboarding, billing, monitoring and the wider integration set.
- 5
Run and improve
MonthlyWe host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.
Why we start with an MVP
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.
- Pick the riskiest assumption. Here: will research teams adopting published methods use it to solve "teams overlook practical details missing from a methods section"?
- Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
- Run a paid pilot. Agree the acceptance criteria, input limits and reviewer responsibilities before starting.
- Measure, then decide. Track unspecified parameters and researcher-rated question usefulness. Then expand, change course or stop, with evidence instead of opinions.
MVP scope for this solution. Costed pilot: General evidence review; hazardous procedural optimization excluded. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: extract reported parameters; link exact passages. Support the third task through an assisted review queue: flag unspecified conditions. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of researcher-reviewed methods clarification 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 researcher-reviewed methods clarification brief, automate compare local constraints; draft clarification questions; export transfer briefing. 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. General evidence review; hazardous procedural optimization excluded.
What the build depends on. A clear research protocol, source access, citation tracking and qualified interpretation. Interview work also needs relevant participants and consent management. Obtain representative authorized inputs, an agreed review rubric and a buyer-side owner. Specific scope: General evidence review; hazardous procedural optimization excluded.
Investment
A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.
- Phase 1
MVP
One buyer segment, one recurring use case; first modules: extract reported parameters; link exact passages. Manual review in the loop.
- Phase 2
Paid pilot
Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.
- Phase 3
Full product
Self-serve onboarding, billing, monitoring and the wider integration set.
Indicative total, MVP to full product$39,000about 4 weeks of creation time · start with the MVP from $11,500
Running costs per month
A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.
| Stage | Hosting and infrastructure | AI usage | Total per month |
|---|---|---|---|
| MVP and paid pilotabout 3 customers | $30–$60 | $80–$160 | $110–$220 |
| Full productabout 50 customers | $110–$210 | $880–$1,750 | $990–$1,960 |
Run it or resell it
For your own team
Research teams adopting published methods run it inside the business: licensed papers and investigator-defined transfer goals in, researcher-reviewed methods clarification brief out, reviewed by your people.
As part of your offer
Agencies, consultancies and software companies can offer it to their own clients under their brand. We build and maintain it; you sell and deliver it.
Your brand, or this one
Run it under your own brand, or start from this concept style.
- primary
#91274c - accent
#54c9ba - surface
#f1e4e9 - ink
#22201e
- Headings
- DM Serif Display
- Text
- DM Sans
- Voice
- Rigorous, transparent, cited
Selling it to your own clients: the go-to-market playbook
Pricing to test
Test USD 750-3,000 for one tightly bounded research question and evidence pack. Participant recruitment, specialist review and licensed data are separately scoped. Repeat tracking can become a retainer. Prices are hypotheses. For this buyer, package the first sale around review one non-sensitive methods paper and the defined researcher-reviewed methods clarification brief. Record actual review effort before offering a recurring allowance. The commercial pilot fee is distinct from the platform development budget.
Message to test
Show what a paper does not specify before attempted replication. Demonstrate the result with review one non-sensitive methods paper for research teams adopting published methods. Use a concrete before-and-after example without promising unmeasured savings.
Where to find buyers
Research methods workshops and laboratory networks
Lead magnet
Review one non-sensitive methods paper
The first 30 days
- Week 1: interview five prospective buyers from research teams adopting published methods and inspect how they handle teams overlook practical details missing from a methods section.
- Week 2: prepare review one non-sensitive methods paper using authorized or synthetic material.
- Week 3: share the demonstration through research methods workshops and laboratory networks and seek one bounded paid pilot.
- Week 4: measure unspecified parameters and researcher-rated question usefulness, 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 review one non-sensitive methods paper and deliver researcher-reviewed methods clarification brief. Compare unspecified parameters and researcher-rated question usefulness 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
Unspecified parameters and researcher-rated question usefulness
Retention and expansion
Build repeat use around researcher-reviewed methods clarification brief. Save approved configurations and review decisions with permission, revisit unresolved exceptions and show progress on unspecified parameters and researcher-rated question usefulness. 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
Niche research protocols, credible researcher relationships and a rights-cleared evidence archive with consistent interpretation methods. For this concept, accumulate permissioned examples and reviewer corrections around show what a paper does not specify before attempted replication. The durable asset is reliable task-specific execution and trusted customer configuration, not access to a general-purpose AI model.
Alternatives and positioning
Research consultants, internal analysts, literature databases and general search or summarization tools. Position this concept around show what a paper does not specify before attempted replication. 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
Researcher time, source access, participant recruitment, transcription, evidence coding, expert review and report revisions. Initial validation additionally budgets for domain researcher review. Track model usage, storage, reviewer minutes, exception handling and customer support per accepted deliverable.
Safeguards
Preserve original data, methods, citations and research limitations. Use researcher review and document every substantive transformation. General evidence review; hazardous procedural optimization excluded. Require appropriate access and publication approval. Preserve source material, label AI drafts and make corrections traceable. Measure false positives and missed cases alongside speed.