Production AI

AI agent development, RAG and data systems

Agents, retrieval, and the data infrastructure underneath them — embeddings, vector search, model hosting and the databases that feed them — built to run unattended, with evaluation and monitoring from the start.

All services

Typically built with

  • LangChain / LlamaIndex
  • Vector DBs (Pinecone, Milvus)
  • OpenAI / Anthropic APIs
  • Postgres / MongoDB
  • RAG & Embeddings

Scope

What this covers.

  • AI agents

    An agent that reads a request, uses your tools to act on it, and hands back a result — with hard limits on what it is allowed to do.

  • RAG & retrieval

    Answers pulled from your own documents and data instead of the model’s guesswork, so it actually knows your products, policies and history.

  • Accuracy & guardrails

    Every answer cites where it came from and is checked against a set of real questions, so when it does not know something it says so rather than inventing.

  • Data systems

    The databases and pipelines underneath, keeping the model fed with current data instead of a snapshot that went stale last month.

  • Model hosting & integration

    The model runs where it has to: your environment or ours, wired into the app you already have.

Running unattended

A demo answers once. A system answers on Tuesday at 3am.

The distance between the two is evaluation sets, retries, fallbacks, cost ceilings and somewhere for a failure to show up other than a customer noticing. That is the part of an agent that is ordinary software engineering, and it is most of the build.

agent run · sample
plan → 4 steps, budget 8k tokens
tool  invoices.search        412ms
tool  ledger.fetch           1.2s
check schema mismatch on 2 rows
retry 1/3 with corrected mapping
check passed — 0 rows unmatched
wrote run log, 3 events emitted

Retrieve, act, check, log — and do it again without being asked.

Built here

We build this for clients, and for ourselves.

Two of the products we sell are ones we built and run in-house. They are the shortest answer to whether we actually ship AI, rather than only talk about it.

How the work runs

The order things happen in.

We treat AI like software — grounded in your data, with evaluation, observability, and a cost budget — not a one-off prompt. We build a thin vertical slice first, measure it against real questions, then harden and ship.

  1. Define success metrics before building anything

  2. Get retrieval right: chunking, embeddings, and search tuned to your content

  3. Ground answers in sources with citations, and evaluate against real data

  4. Harden for production: monitoring, cost controls, and fallbacks

How it gets built

Two or three people, one problem.

Agent work is mostly evaluation and iteration — running the thing against real data, watching where it goes wrong, and narrowing it.

Before you ask

Questions we get.

  • Will the AI actually be reliable in production?

    That is the whole point — we build with retrieval grounded in your data, evaluation sets, guardrails, and monitoring so behaviour is measured, not hoped for.

  • What is RAG and do I need it?

    RAG (retrieval-augmented generation) makes an LLM answer from your own documents and data instead of just its training. If you need AI that knows your products, policies, or knowledge base accurately, you need RAG.

  • How do you stop the AI from hallucinating?

    We ground answers in retrieved sources with citations, add guardrails, and re-check against real questions as we go — so when it does not know something, it says so instead of filling the gap.

  • Which models do you use?

    We are model-agnostic — latest Claude, OpenAI, or open-weights — chosen per task on quality, latency, and cost.

  • Can you work with our private data?

    Yes — RAG over your documents with access controls, and self-hosted options when data cannot leave your environment.

  • Is an agent overkill for what I need?

    Often, yes — if you want a chatbot for common questions or a one-off script, an off-the-shelf tool is cheaper and we will say so. This is for AI that has to be right, runs without someone watching it, and answers from data that is yours.

Next to this

This work is usually not on its own.

Tell us what you are building.