AI that leaves the notebook.

Most corporate AI dies as a proof of concept: a model that works in a notebook and a slide that promises the rest. We build the rest. One AI problem, taken to production, with the numbers to show what it changed.

The pattern is familiar by now. A data-science team proves something interesting, the demo lands well, and then the hard part starts: retrieval that holds up against real documents, evaluation that catches regressions, latency budgets, cost ceilings, and the unglamorous plumbing that turns a model into a system. That is the part we do. We work in Python across the modern stack: LLM APIs from OpenAI and Anthropic, LangChain and Hugging Face where they earn their keep, PyTorch when the problem needs training rather than prompting, FastAPI and PostgreSQL underneath.

Because every engagement is one atomic technology problem, AI work with us never starts with a platform. It starts with a claim: this model, wired into this process, moves this number. The build proves the claim or kills it in weeks. Either result is worth more than another quarter of committee review.

Technologies

For corporates

An AI problem that is stuck between the data team and the roadmap. We take it outside and return it working.

For startups

An AI feature that could be the wedge. We cut it to the provable core and ship it.

The evidence

Accuracy, latency, and cost measured against the baseline, packaged so a CFO can read it.