AI training data partnerships for software companies
Years of shipping software leave behind private repositories, pull request debates, failing builds and the tickets that explain why each change was made. That record of real engineering work is exactly what coding models struggle to find on the public internet. Momentra helps software companies assess whether that history could become a licensing or sale opportunity, without exposing anything before an NDA is in place.
Can software companies license or sell their data for AI training?
Yes. Software companies can license or sell qualifying engineering history as AI training data once it is anonymized. The most valuable assets are private repositories with full git history, pull request review threads and issues linked to the commits that closed them. Secrets buried in old commits are scrubbed, and core trade-secret modules can be excluded.
No files needed upfront. We sign an NDA before reviewing any sample.
Data software companies hold that AI can learn from
Private repositories with full history
Production codebases with their git history show how a system grew, which abstractions survived and which were refactored away. Mature, actively maintained repos in less common languages or domains tend to be scarcer than generic CRUD apps.
Pull requests and review threads
Review comments capture a senior engineer explaining why a change is risky, slow or hard to maintain. Paired with the final merged diff, they show both the critique and the fix.
Linked issues and bug reports
A Jira or Linear ticket that links to the commit that closed it gives a model the objective, the reproduction steps and the verified outcome in one chain.
Test suites and CI results
Unit, integration and end-to-end tests, along with CI logs showing which runs failed and why, give models a way to check whether code actually works rather than just looks plausible.
Design docs and RFCs
Architecture decision records and internal RFCs explain trade-offs that never appear in the code itself, such as why a team chose a queue over a cron job.
What this data helps AI models learn
Coding agents that fix real bugs
Issue-to-commit pairs with passing tests are useful for teaching agents to read a bug report, locate the faulty code and submit a change that a test suite can verify.
Code review assistants
Review threads help models learn what experienced reviewers flag: missing error handling, race conditions, unclear naming, and changes that break backward compatibility.
Large-scale refactoring
Commit sequences that migrate a framework version or split a monolith show how multi-file changes are planned and staged across weeks, not just single edits.
Privacy and compliance, handled first
You decide what is in scope, nothing moves before an NDA, and data is anonymized before any further use.
Secrets committed to history
API keys, database passwords and tokens often hide in old commits even after removal from the current branch. Anonymization scans the full history, not just HEAD, and strips or replaces credentials before any further use.
Customer names and data in code
Seed files, fixtures, logs and comments sometimes contain real customer names or emails. These are detected and replaced as part of de-identification, and you decide which repos or folders stay out of scope entirely.
Third-party and open-source licenses
Vendored libraries and copied snippets carry their own license terms. Scoping should separate code your company owns from code it only uses, and the signed agreement defines what is transferred.
Competitive and trade-secret concerns
Some modules embody your core advantage. You can exclude them, or limit permitted use and retention in the contract, before any sample leaves your control.
How a partnership begins
Share Basic Information
Share only your contact, company, industry, and partnership interests—no raw data required.
Initial Value Assessment
We discuss the data type, completeness, scarcity, and the tasks it could support.
Define the NDA and Boundaries
Before reviewing any sample, we define confidentiality, permitted use, retention, and security requirements.
Receive an Offer and Begin the Partnership
We provide a purchase proposal and contract, then support de-identification and secure data transfer.
Frequently asked questions
- Does our product need to still be in active development?
- No. Retired products and older versions can still be useful if the history is intact and the code is reasonably complete. What matters most is depth of history, linked context like tickets and reviews, and whether the work is verifiable through tests.
- Will selling our code let a competitor rebuild our product?
- Data is used for model training under the permitted use defined in the agreement, not handed to competitors as a product. You also choose the scope, so sensitive modules can be excluded or restricted.
- Do we need to send code to get an initial assessment?
- No. The first conversation covers languages, repo size, age, history depth and linked tooling. Samples are only reviewed after an NDA defines confidentiality, retention and security.
- What if some of our code was written for clients?
- Code built under client contracts may belong to the client or carry restrictions. That code may need the client's permission, and it is usually simplest to start with internally owned repositories.
Related
Tell us about the real workflows behind your business.
Share only the essential details. No files are required at this stage.
Contact the Momentra team