AI implementation

AI that runs inside production systems, with logging, fallbacks and a person in the loop where it matters.

Talk to an engineer

Typical duration: 2 to 5 months to production.

When to call us

For teams with an AI pilot that worked in a demo and now has to work every day, on real data, with someone accountable for it.

  • The pilot impressed everyone, and nobody can say how accurate it is.
  • There's no plan for what happens when the model gets one wrong.
  • Cost per task is unknown, and the usage bill is starting to get noticed.
  • Security or legal has questions about the data, and the answers aren't written down.

What we deliver

A demo that works on ten examples is the easy part. We build the rest: evaluation against your own data, guardrails, cost controls, and a fallback for the day the model gets it wrong.

  • A test set built from your real cases, so accuracy is a measured number.
  • Document processing, assistants and decision support built into the systems you already run.
  • Human review steps wherever a wrong answer costs money or trust.
  • Logging of every model decision, with cost per task tracked.
  • A model-swap path, so you are not tied to one vendor.
Yours at handover
Evaluation suite, prompts, pipelines, cost dashboard and an operating manual.
Typical duration
2 to 5 months to production

How it moves through the shop

The same four stages as every project, applied to AI implementation.

  1. 1

    Assay

    We build a test set from your real cases and measure what the pilot gets right today.

  2. 2

    Forge

    We build the production pipeline: inputs, guardrails, human review steps, logging and cost tracking.

  3. 3

    Temper

    We test against the hard cases, try to break it on purpose, and hold it to an accuracy bar agreed in advance.

  4. 4

    Service

    We watch accuracy and cost over time, and re-test whenever the model or your data changes.

Read the full process

Questions about AI implementation

Which models do you use?

The one that meets your accuracy, cost and data requirements. We build so it can be swapped, because the best option this year may not be the best one next year.

Where does our data go?

That is decided and written down in the assay: what may leave your environment, what may not, and which vendor terms apply. The system is then built to enforce it.

How do you measure accuracy?

Against a test set of your own real cases with agreed right answers, run before launch and again after every change.

Read all questions

Bring us the system you can't afford to get wrong.

Talk to an engineer

30 minutes. Bring the problem, and we'll tell you whether we're the right shop for it.