Your engineering work.
A new source of value.
Code reviews, migrations and resolved incidents capture how your team solves problems.
The work you know.
A new source of value.
Engineering records your business owns could help AI learn from real development work. The opportunity depends on buyer requirements and the rights to the code, documentation and other material involved.
How it worksThe knowledge
behind the work.
A few examples to explore.
Review histories
Authorised review discussions and decision records showing how a change is improved.
Incident resolution
Owned migration plans and implementation notes connecting a problem with a working solution.
Migration playbooks
Permitted incident reviews and troubleshooting records explaining how a failure was resolved.
Share knowledge with a defined scope.
Client repositories, credentials, production logs and customer information are not part of an initial enquiry. Confirm ownership, employee and contractor arrangements, open-source obligations and any client restrictions before proposing a collection.
Your questions.
Answered.
Do you need access to our GitHub organisation?
No. Start with a high-level description of the engineering records available. Any later access or sample would need a specific agreement and a review of the material.
Can open-source code be included?
It depends on the applicable licences and the proposed use. Your ability to view or use a repository does not by itself establish the right to offer its contents under a new licence.
Is proprietary source code required?
No. Engineering knowledge can also be captured in internal playbooks, review processes and incident records. The collection still needs to meet a buyer’s requirements.
How is the value of engineering data assessed?
The task coverage, clarity of the examples, rights available, preparation required and licence terms all matter. An offer requires actual buyer interest; a repository’s size alone is not a valuation.
