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 generative AI problem, an LLM application, a RAG system, an AI agent, a risk model, taken to production, with the numbers to show what it changed.

How we work

  1. The pattern. A data-science team proves something interesting, the demo lands well, and then the hard part starts. Most corporate machine learning dies right there, between the notebook and production. LLM applications, RAG systems, AI agents, risk models
  2. The claim. Machine learning work with us never starts with a platform. It starts with a claim: this model, wired into this process, moves this number. One measurable build, never a roadmap. one problem, fixed price, weeks
  3. The build. Retrieval-augmented generation (RAG) that holds up against real documents, evaluation that catches regressions, agent workflows that stay inside their guardrails, latency budgets, cost ceilings, the unglamorous plumbing that turns a model into a system. 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. Python, OpenAI, Anthropic Claude, LangChain, Hugging Face, PyTorch, FastAPI, PostgreSQL
  4. The verdict. The build proves the claim or kills it in weeks. Either result is worth more than another quarter of committee review, and the numbers come packaged so a CFO can read them. accuracy, latency, cost vs baseline

What we take on

  • LLM application development. A language-model product taken from idea to production: prompt architecture, tool use, evaluation, cost control.
  • RAG & enterprise search. Retrieval-augmented generation over your real documents, grounded, cited, and measured against wrong answers.
  • AI agents & workflows. Agentic systems that stay inside their guardrails and finish multi-step work a human used to shepherd.
  • Computer vision. Detection, OCR and inspection models wired into the operational process that acts on them.
  • Model productionisation & MLOps. The notebook model turned into a monitored, versioned, retrainable service your team can run.
  • Risk & prediction models. Churn, fraud, credit, demand, one prediction wired to one decision, with the lift measured.

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.

The way we engage

  • Senior engineers, no intermediaries. The people on the call are the people writing the code. Questions get same-day answers, not a follow-up meeting.
  • A working build every week. Progress arrives as running software, not status decks. Every drop is something you can put in front of a user.
  • Fixed price, never a meter. The price is agreed before we start. If we scoped it wrong, that is our cost of learning, not your overrun.
  • You own everything. Code, infrastructure and documentation transfer at hand-over. No licences back to us, no dependency by design.
  • Candour in week zero. If the problem needs rethinking, we say so before the build starts, not after the budget is spent.
  • Across your time zones. US, European, Gulf and ANZ coverage, with overlap hours agreed at kick-off.