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AI engineering for production

A demo answers the questions it was built for. Production gets thousands it was not. We build RAG, agents and LLM features that are measured, cost-capped and maintainable by your team.

  • Built onYour data, your cloud
  • Measured byAn eval set you agree
  • Starts withA 2-week PoC Sprint
Book a technical consultSee the PoC Sprint

What we build

Four kinds of AI work, each with its own page: how it is built, where it breaks, and when it is the wrong choice.

  • RAG on your documents

    Answers from your contracts, policies, tickets and docs, each citing its source.

  • AI agents

    Agents that use the tools you approve and wait for a person before changing data.

  • Evals, cost and latency

    Know whether your AI is getting better or worse before your users do.

  • AI in existing systems

    Copilots, document automation and search, added to the systems you already run.

What production means here

Five things a demo can skip and a launch cannot. Every build is held to all five.

  1. Measured

    An eval set of real questions and accepted answers, run again on every change, so a fix never quietly breaks something else.

  2. Cost-capped

    A budget per answer, met with caching, smaller models where they score as well, and limits on what one request can spend.

  3. Fast enough

    A latency target your users agree with, with streaming where the answer is long.

  4. Safe to be wrong

    Sources on every answer, a refusal when the system is unsure, and a person approving anything that changes your data.

  5. Yours to run

    Code in your repository, docs your team can follow, and the eval harness that keeps it honest after we leave.

Questions about AI builds

The ones that come up on almost every consult.

Can you stop it from making things up?

Not to zero. We measure how often it happens on your eval set, make it cite sources, and make it refuse when it is unsure. You see the rate before launch.

Does our data go to OpenAI or Anthropic?

Only if you choose a provider that way. Models can also run through your cloud account or on your own servers. How we handle data.

RAG or fine-tuning?

Usually RAG first: it is cheaper to change and every answer can show its source. Fine-tuning is worth it when the eval set shows RAG cannot reach the target.

What will it cost to run?

The PoC Sprint measures cost per answer on your data, and the go/no-go report turns that into a monthly estimate for your volume.

Which model will you use?

The one that meets your targets for the lowest cost on your eval set. We test more than one.