Most AI projects die between the demo and production. The prompt works in a notebook, then falls over on real user input, real latency budgets and a real invoice at the end of the month. I build the version that survives that — AI as a feature inside your product, with the same attention to cost, correctness and failure states as any other part of the stack.
What this covers
- LLM features — drafting, summarising, classification, extraction and conversational interfaces built into your existing app.
- Retrieval over your own data — RAG pipelines so answers come from your documents, catalogue or knowledge base instead of the model guessing.
- Workflow automation — the repetitive reading, sorting and writing your team currently does by hand.
- Evaluation and guardrails — test sets so you can prove a prompt change made things better, not just different.
- Cost and latency work — model selection, caching and prompt trimming to keep the per-request bill sane.
How a project runs
We start with the narrowest useful version — one workflow, one clear success measure. That gets built, measured against a real test set, and put in front of actual users before the scope widens. It is the fastest way to find out whether AI is the right answer for your problem, and it keeps you from paying to discover it is not.
Where it fits
This slots into the stacks I already work in — a Laravel or WordPress backend, a Flutter or React front end — so the AI part is not a separate system you have to maintain alongside everything else.