AI features and integration
Language models wired into real product workflows, with an evaluation set that tells you when a change made things worse.
You have a demo that impressed everyone and will not survive real users, or a manual process expensive enough to be worth automating. Either way the hard part is knowing whether the output is good enough to trust.
- Starts with
- A 30-minute call
- Timezone
- UTC+05:30 · overlaps EU mornings and US evenings
What you get
- The feature running in your product behind schema validation, with a defined fallback for when the model is wrong or the API is down.
- An evaluation set of real inputs and expected outputs, with a pass threshold agreed before any prompt was written.
- Cost and latency per request, traced in production, so nobody discovers the number at the end of the month.
How it runs
Check it should exist
Some of this work is telling you a database query would do the job better and cost nothing to run.
Build the evaluation first
A labelled set of real inputs before any prompt work, because without one every later change is a guess.
Ship it constrained
Retrieval over your own data, structured outputs, timeouts and a fallback path — then cost and latency instrumented from day one.
Questions
Our data cannot leave our network. Is this still possible?
Often yes. Open-weight models on your own hardware handle a good share of practical work: classification, extraction, summarising, routing. I maintain a code review tool that runs on a local model and never sends a line of source anywhere.
How do you stop it making things up?
You constrain the problem rather than trusting the model. Retrieval limits it to your source material, output schemas prevent free-form invention, and the evaluation set quantifies the error rate that remains. Anyone promising zero is selling something.
AI wrote most of our codebase and now it keeps breaking. Can you fix that?
Yes, and rarely by rewriting it — a rewrite restarts the clock on every bug you already found. The faster route is to pin the current behaviour with tests, restructure in place while you keep releasing, and leave your team driving these tools in a way that keeps the structure intact.
If this sounds like your situation, the next step is a call.