
Negative-result replication triage notebook
Spend follow-up effort on informative tests instead of unstructured repeats.
- For
- Research groups deciding which inconclusive experiments to repeat
- Solves
- Failed or null experiments are rerun without a structured account of alternative explanations.
- Delivers
- Scientist-reviewed replication decision record and experiment proposal
- Built in
- about 5 weeks of creation time, MVP in 5 days
- Investment
- $19,500 for the MVP, $50,000 for the full product
- Run it
- Inside your business, or as part of your offer to clients
What it does
Spend follow-up effort on informative tests instead of unstructured repeats.
- Separate execution failures from interpretable null outcomes.
- List testable alternative explanations.
- Compare bounded follow-up experiments.
- Compare the reviewed result with the recorded baseline and value assumptions.
- Capture corrections and named-owner approval before consequential use.
- Export a versioned scientist-reviewed replication decision record and experiment proposal with source references and unresolved questions.
What goes in, what comes out
- Consented experiment records
- Predefined hypotheses
- Instrument logs
- Uncertainty estimates
AI drafts, people review. Research evidence workspace with reviewed deliverables.
- Scientist-reviewed replication decision record
- Experiment proposal
How it works
The workflow
- InStart with
Consented experiment records, predefined hypotheses, instrument logs and uncertainty estimates
- 1
Confirm the buyer's problem and scope
- 2
Collect consented experiment records
- 3
Predefined hypotheses
- 4
Instrument logs and uncertainty estimates
- 5
Then follow this sequence: 1
- OutFinish with
Scientist-reviewed replication decision record and experiment proposal
AI does the heavy lifting, people stay in charge
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Scientists approve methods and statistical interpretation; never invent results or suppress null findings. A model suggestion is never a verified fact, professional decision or authorization to act.
What your team sees
Primary screens: Research question and consent, Evidence comparison, Reviewed findings and experiment. 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. Make the task-specific outcome scientist-reviewed replication decision record and experiment proposal visible beside its evidence, review state and value baseline.
Accounts and administration
Source provenance, participant consent where applicable, research questions, coding definitions, reviewer disagreements, citations and versioned conclusions. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.
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. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
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: separate execution failures from interpretable null outcomes; list testable alternative explanations. 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 groups deciding which inconclusive experiments to repeat use it to solve "failed or null experiments are rerun without a structured account of alternative explanations"?
- 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 quality and outcome thresholds before the pilot using this measure: Information gained against predeclared questions per experiment cost, with negative outcomes retained.
- Measure, then decide. Track information gained against predeclared questions per experiment cost and with negative outcomes retained; 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: Scientists approve methods and statistical interpretation; never invent results or suppress null findings. Implement one approved input format, a bounded representative case set and the first two task modules: separate execution failures from interpretable null outcomes; list testable alternative explanations. Support the third module with operator review: compare bounded follow-up experiments. 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 scientist-reviewed replication decision record and experiment proposal. Retain the explicit scope boundary: Scientists approve methods and statistical interpretation; never invent results or suppress null findings.
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 cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: Scientists approve methods and statistical interpretation; never invent results or suppress null findings.
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: separate execution failures from interpretable null outcomes; list testable alternative explanations. 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$50,000about 5 weeks of creation time · start with the MVP from $19,500
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 groups deciding which inconclusive experiments to repeat run it inside the business: consented experiment records, predefined hypotheses, instrument logs and uncertainty estimates in, scientist-reviewed replication decision record and experiment proposal 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
#91273a - accent
#54c9c3 - surface
#f1e4e7 - ink
#22201e
- Headings
- Fraunces
- Text
- Inter
- 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. Package the initial sale as one bounded scientist-reviewed replication decision record and experiment proposal. 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
Spend follow-up effort on informative tests instead of unstructured repeats. Demonstrate a concrete scientist-reviewed replication decision record and experiment proposal using the buyer's approved example and show the baseline, corrections and actual delivery effort.
Where to find buyers
Research groups deciding which inconclusive experiments to repeat professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.
Lead magnet
A reviewed sample scientist-reviewed replication decision record and experiment proposal from a small authorized input set, with a transparent calculation of information gained against predeclared questions per experiment cost, with negative outcomes retained and no promised savings.
The first 30 days
- Week 1: interview five research groups deciding which inconclusive experiments to repeat and inspect a recent example of failed or null experiments are rerun without a structured account of alternative explanations.
- Week 2: prepare a consented or synthetic demonstration of the three task modules.
- Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
- Week 4: measure information gained against predeclared questions per experiment cost, with negative outcomes retained, 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: Information gained against predeclared questions per experiment cost, with negative outcomes retained. 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
Information gained against predeclared questions per experiment cost, with negative outcomes retained; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.
Retention and expansion
Repeat the workflow when the buyer again needs scientist-reviewed replication decision record and experiment proposal. 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
Niche research protocols, credible researcher relationships and a rights-cleared evidence archive with consistent interpretation methods. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for research groups deciding which inconclusive experiments to repeat. Repeatable delivery and useful integrations matter more than access to a base model.
Alternatives and positioning
Research consultants, internal analysts, literature databases and general search or summarization tools. Compare this product with the buyer's present method on information gained against predeclared questions per experiment cost, with negative outcomes retained. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.
Main delivery costs
Researcher time, source access, participant recruitment, transcription, evidence coding, expert review and report revisions. Additional initial validation requires representative authorized sample preparation, buyer interviews, qualified domain review and bounded validation of scientist-reviewed replication decision record and experiment proposal. Track cost per accepted output, including correction work, unsuccessful cases and support.
Safeguards
Preserve original data, methods, citations and research limitations. Use researcher review and document every substantive transformation. Scientists approve methods and statistical interpretation; never invent results or suppress null findings. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.