Research data cleaning
Reversible, documented transformations with researcher-approved rules.
- For
- Research groups with messy observational datasets
- Solves
- Undocumented cleaning steps undermine subsequent analysis.
- Delivers
- Cleaned dataset and transformation script
- Built in
- about 5 weeks of creation time, MVP in 5 days
- Investment
- $10,000 for the MVP, $39,500 for the full product
- Run it
- Inside your business, or as part of your offer to clients
What it does
For research groups with messy observational datasets, turn authorized datasets, data dictionaries and cleaning rules into cleaned dataset and transformation script.
- Profile missing values.
- Detect inconsistent coding.
- Propose transformations.
- Preserve raw data.
- Generate reproducible scripts.
- Validate reviewed outputs.
What goes in, what comes out
- Authorized datasets
- Data dictionaries
- Cleaning rules
AI drafts, people review. Technical delivery workspace with managed implementation.
- Cleaned dataset
- Transformation script
How it works
The workflow
- InStart with
Authorized datasets, data dictionaries and cleaning rules
- 1
Scope one technical task
- 2
Inspect authorized material
- 3
Propose an implementation
- 4
Build in a controlled environment
- 5
Run relevant checks
- 6
Obtain the required change approval
- 7
Deliver with recovery instructions
- 8
Monitor the agreed operating scope
- OutFinish with
Cleaned dataset and transformation script
AI does the heavy lifting, people stay in charge
Explain code or configuration, draft transformations and propose technical changes. Execute deterministic validation and meaningful tests. Engineers review correctness, access handling and failure behavior before deployment.
What your team sees
Key screens: Data profile, transformation preview, provenance log. Show a work backlog, proposed changes and verification results. Link each item to its source configuration, code or data mapping. Provide execution logs and an owner-facing health view. Keep environments and approval states clearly separated so a draft cannot be mistaken for a live change. In this product, the first view is data profile, followed by transformation preview and provenance log.
Accounts and administration
Project access, environment separation, versioned changes, test evidence, owner approvals, execution logs, rollback instructions and incident handling.
Integrations and data access
Authorized datasets, papers, protocols, code and research records. Approved repositories, application APIs, execution platforms and monitoring systems. Validate current API access and behavior during discovery before promising compatibility. These are candidate integration categories, not verified supported connectors.
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: profile missing values; detect inconsistent coding. 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 weeksRemaining modules: preserve raw data; generate reproducible scripts; validate reviewed outputs. Self-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 with messy observational datasets use it to solve "undocumented cleaning steps undermine subsequent analysis"?
- 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. Implement one bounded task in a safe test environment.
- Measure, then decide. Track verified transformations and reproducibility. Then expand, change course or stop, with evidence instead of opinions.
MVP scope for this solution. Begin with research groups with messy observational datasets and one recurring use case. Build the first two modules: profile missing values; detect inconsistent coding. Provide operator assistance for the third module: propose transformations. Deliver cleaned dataset and transformation script 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.
After the MVP. After paid pilots establish value, automate the remaining modules: preserve raw data; generate reproducible scripts; validate reviewed outputs. 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.
What the build depends on. Authorized technical access, suitable test environments, documented APIs or schemas, secrets management, meaningful checks and recovery procedures.
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: profile missing values; detect inconsistent coding. 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
Remaining modules: preserve raw data; generate reproducible scripts; validate reviewed outputs. Self-serve onboarding, billing, monitoring and the wider integration set.
Indicative total, MVP to full product$39,500about 5 weeks of creation time · start with the MVP from $10,000
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 | $60–$120 | $90–$180 |
| Full productabout 50 customers | $110–$210 | $530–$1,050 | $640–$1,260 |
Run it or resell it
For your own team
Research groups with messy observational datasets run it inside the business: authorized datasets, data dictionaries and cleaning rules in, cleaned dataset and transformation script 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
#912751 - accent
#54c981 - surface
#f1e4e9 - ink
#22201e
- Headings
- Playfair Display
- Text
- Source Sans 3
- Voice
- Rigorous, transparent, cited
Selling it to your own clients: the go-to-market playbook
Pricing to test
Test USD 1,000-4,000 for one bounded implementation or technical review, then USD 200-1,000 monthly for defined maintenance. Hosting, vendor fees and major feature changes are separate. Prices are hypotheses.
Message to test
Research data cleaning for research groups with messy observational datasets. Reversible, documented transformations with researcher-approved rules. Demonstrate the claim through a reproducible cleaning report on a sample dataset.
Where to find buyers
Research data management services
Lead magnet
A reproducible cleaning report on a sample dataset
The first 30 days
- Week 1: interview five prospective buyers in this segment: research groups with messy observational datasets. Ask to see a recent example of the problem and their current process.
- Week 2: prepare this demonstration using authorized or synthetic material: a reproducible cleaning report on a sample dataset.
- Week 3: present it through research data management services and seek one narrowly scoped paid pilot.
- Week 4: review verified transformations, reproducibility, total delivery effort and a concrete renewal decision before increasing scope.
Paid pilot
Implement one bounded task in a safe test environment. Demonstrate normal operation, failure handling and recovery with representative inputs. Have the responsible technical owner review the results. For this solution, use authorized datasets, data dictionaries and cleaning rules and evaluate cleaned dataset and transformation script. Agree success thresholds with the buyer before starting; collect a baseline for verified transformations, reproducibility. A positive signal is payment and repeat use with acceptable quality and delivery cost, not a favorable demo reaction alone.
Success metrics
Verified transformations, reproducibility
Retention and expansion
Maintain agreed integrations or technical assets, review failures and upstream changes, and sell additional scoped work only after the first implementation is stable.
Why clients would pick it
Reliable niche implementations, integration knowledge, representative tests and ongoing operational responsibility. For this solution, build around reversible, documented transformations with researcher-approved rules. This advantage requires execution and accumulated customer trust; the base model alone is not a defensible asset.
Alternatives and positioning
Developers, system integrators, existing automation products and internal engineering work. Differentiate on this specific proposed advantage: reversible, documented transformations with researcher-approved rules. Test it against the buyer's current method on the same task. Competitor coverage and uniqueness have not been established.
Main delivery costs
Engineering, testing, cloud execution, third-party API fees, monitoring, incident response and vendor-change maintenance.
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.