AI/LLM-driven development

Using models inside your engineering process — migration rigs, code conversion, review — with the output verified rather than trusted.

The productive use of an LLM in engineering is rarely “write this feature”. It is the repetitive, mechanical, high-volume work: converting a few hundred service endpoints, translating a legacy pattern into the current one, drafting the tests for code that never had any. The value comes from the verification step around the model, not from the model.

I built exactly this at GreenRoad — an AI-assisted rig that converted legacy CoreWCF services to REST APIs — and the lesson was that the harness matters more than the prompt.

What you get

The tasks in your pipeline where a model beats a person, and the ones where it does not
A conversion or generation harness with verification built into it
Cost per run and failure behaviour measured before it goes wide
Your team able to run it after I leave