AI enablement

AI adoption

Getting AI into how your team already works — finding where it genuinely helps, rolling out the right tools, and building the copilots and automations that fit, without a research project or a rebuild.

All services

Typically built with

  • ChatGPT / Claude / Copilot
  • RAG over your documents
  • n8n / Make automation
  • SSO & access controls

Scope

What this covers.

  • Where AI actually helps

    An honest look at how your team works and where a model saves real hours, so you start with the few workflows that pay off rather than a tool nobody opens.

  • Tool rollout & access

    Standing up the assistants your team will actually use, with the accounts, access and limits sorted, so it is ready on day one rather than a pilot that stalls.

  • Internal copilots & automation

    Building the AI tools specific to your work — a copilot over your own documents, an automation that clears a recurring task — and wiring them into what you already run.

  • Training your team

    Getting the people who do the work comfortable with it: the prompts that work, the ones that waste time, and where the model should not be trusted without a check.

  • Governance & guardrails

    A plain rule for what goes into a model and what never should, so moving fast on AI does not quietly turn into a data problem later.

Built here

We run on this ourselves.

The test of whether someone can get AI working inside a business is whether they have done it in their own. Two of the products we sell are AI we built and use day to day — the same tools and patterns we would set up for your team.

How the work runs

The order things happen in.

We start from the work your team already does, not from the technology — find the few places AI earns its keep, put something real in front of people early, and expand from what actually gets used.

  1. Map the real workflows and rank them by hours saved against effort to change

  2. Roll out the off-the-shelf tools first — most of the value needs no custom build

  3. Build only where a general tool falls short, over your own data and systems

  4. Train the team and agree what the model is, and is not, allowed to do

How it gets built

Start with the work, not the tool.

Most of this is watching how a team actually spends its day, then putting AI where it saves the most and leaving the rest alone.

Before you ask

Questions we get.

  • How is this different from building an AI product?

    This is about getting your existing team working with AI, not shipping an AI product to customers. If you want a customer-facing system built, that is our AI Agents & Data Systems work; here the goal is your own people doing their work faster and with less drudgery.

  • Do we need our own AI models or infrastructure?

    Usually not. Most of the value comes from off-the-shelf tools set up well and pointed at your own data. We build custom pieces only where a general tool genuinely falls short.

  • Is our data safe if we put it into these tools?

    That is the first thing we settle. We set a clear rule for what can go into which tool, pick accounts whose terms exclude training on your data and check that for each one, and keep anything sensitive on paths you control.

  • Is it too early for us to start?

    Rarely. The usual starting point is a team already pasting things into a chatbot ad hoc — the work is to make that deliberate, shared and safe, not to introduce AI from nothing. If your processes are still changing every week, we would wait until there is a workflow worth keeping.

Next to this

This work is usually not on its own.

Tell us what you are building.