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Build · AI Integration & Automation

AI features that do a job, not a demo

We add AI where a task can tolerate being wrong sometimes and a human can check the result — and we tell you plainly where it cannot.

Get a scoped estimate What you get

Typical timeline: 2-8 weeks for a first feature in production

Illustration: AI Integration & Automation

Deliverables

What you get

Concrete outputs, agreed in writing before work starts.

  • Workflow assessment: where AI helps and where it does not
  • LLM features inside your app or website
  • Document and data extraction pipelines
  • Assistants and search grounded in your own content
  • Automation between the tools you already use
  • Evaluation sets, cost monitoring and guardrails

Who it is for

Is this you?

  • Teams drowning in documents, emails or support tickets
  • Products that want an assistant or smart search built in
  • Operations with repetitive steps between systems

How we work

From first call to live product

  1. 01

    Discover

    We learn the problem, the users and the constraints, and tell you what we would and would not build.

  2. 02

    Design

    Flows and screens designed with the engineers in the room, so what you approve is what ships.

  3. 03

    Build

    Short cycles with a working build you can try every week, not a reveal at the end.

  4. 04

    Launch

    Store submission or deployment, monitoring switched on, and a rollback ready.

  5. 05

    Support

    OS upgrades, platform deadlines and new features, on a schedule rather than in a panic.

The useful question is not whether to use AI. It is whether a given task can tolerate being wrong sometimes. Summarising, drafting, classifying and extracting all can, because a person is reviewing the output anyway. Calculating an invoice total cannot.

We map which parts of a workflow fall on which side of that line before proposing anything. An AI feature in the wrong place does not just fail — it creates work, by producing plausible output nobody can trust.

What we actually do

Start with the task and its measure. Every AI feature we build has a named job — “draft a reply to this ticket”, “pull these twelve fields out of this invoice” — and a way to check it against real examples before it ships.

Ground it in your data. Assistants and search answer from your own documents, with sources shown, rather than from whatever a model happens to remember.

Automate the boring handoffs. A lot of the value is not a chatbot at all: it is moving data between the systems you already pay for, with a model handling the one messy step in the middle.

Keep cost and risk visible. Usage is metered, prompts are versioned, and personal data is handled deliberately — including where it is sent and what is kept. API keys stay on the server, never in an app or a web page.

Built to production standard

AI features fail in the same ways as the rest of your software, plus a few of their own. They get the same engineering: tests, monitoring, and a way to roll back. If you already have an AI-built prototype that needs that treatment, see MVP and vibe-code rescue.

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Let's talk

Tell us what you are building

Send us the scope and we will come back with an honest assessment: what it takes, roughly what it costs, and whether we are the right people for it.

Start the conversation See our work
  • A written reply from an engineer
  • No call required
  • No obligation