AI/LLM-driven development
Using models inside your engineering process — migration rigs, code conversion, review — with the output verified rather than trusted.
AI
LLMs and agents placed inside a stack that already exists, as a step in a process that runs without anyone watching it.
A demo proves a model can do something once. A process proves it does the same thing on Monday morning, on the messy input, with nobody in the room. The gap between those two is where almost all AI projects stall, and it is engineering work rather than model work: where the data comes from, what happens when the answer is wrong, and who finds out.
Two services here, one for teams building software and one for a business that wants specific hours of routine work to stop existing.
What you get
- A shortlist of the tasks in your business that are actually worth automating
- Model choice, cost per run and failure behaviour decided before anything is built
- An integration into the stack you already run, with a way to measure whether it holds
In this area
AI for SMB: cost saving, routine work automation, customer & sales management
Specific hours of routine work every week, identified and then automated — starting with the ones that pay back fastest.