END-TO-END AI SOFTWARE DEVELOPMENT
IN DEVELOPMENTFrom a clear brief.
To reviewable software.
An AI-first software development system intended to connect requirements, planning, implementation, testing, and delivery into one end-to-end workflow.
Talk about Software FactoryTHE PROBLEM
What gets
in the way?
Code generation is one step in software development. A useful change also needs understood requirements, a coherent implementation, tests, review evidence, and a safe route to release.
Coordinate those steps as one traceable delivery run. AI prepares the work and iterates against defined checks; people decide scope, review the result, and authorize merge and release.
Requirements into a plan
Turn a brief into acceptance criteria, a work breakdown, and questions to resolve before implementation.
AI implementation
Generate and revise code against repository context and the agreed scope, with bounded tool permissions.
Tests beside the change
Prepare test evidence, evaluation results, and an explanation of what remains uncertain or incomplete.
Delivery with review gates
Assemble a reviewable pull request and delivery workflow. Keep merge and production release behind explicit authorization.
PEOPLE IN CONTROL
Useful autonomy.
Clear boundaries.
End-to-end automation does not remove accountability. Isolated execution, scoped permissions, test evidence, and human review before merge and release are intended foundations of the product.
How we think about AIA conversation worth having if you’re…
- Engineering leaders exploring AI-assisted delivery
- Product teams with a growing implementation backlog
- Platform teams standardizing how AI-generated changes are reviewed
THE DETAILS
Before the
conversation.
Is the factory available now?
Software Factory is in development alongside Portal and Respond. The website demonstration is a local concept, not a live code-generation service.
Does it replace the engineering team?
The intended role is to automate repeatable development work and prepare reviewable changes. People own product decisions, architecture tradeoffs, and release authority.
How will you know the generated code is good?
The planned approach combines acceptance criteria, repository tests, evaluation cases, and human review. Passing checks supplies evidence; it is not a guarantee that a change is correct.