Pillar 01

Your AI is only as good as the systems it can reach

The biggest promise of AI is not a standalone chatbot, but AI that knows your order history, sees your stock and understands your customer file. That requires access to systems never designed for it. We build that bridge, without destabilising your core system.

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What we do

  1. 01

    Landscape analysis

    What data does the AI application need, where does it live, and what are you legally allowed to do with it?

  2. 02

    An access layer

    APIs, event streams or MCP servers on top of legacy. The old system stays untouched.

  3. 03

    Data quality & contracts

    AI on dirty data is more dangerous than no AI. We handle validation, schemas and fallbacks.

  4. 04

    Phased rollout

    Shadow mode, then human-in-the-loop, then autonomy. Never big bang.

What the scan gives you

Two weeks, a fixed price, and concrete integration advice. Not a discussion document but a map of your landscape with the connection points, the risks and what it costs.

Who it is for

Organisations with an IT landscape that grew over time (ERP, CRM, custom software) who want to use AI without replacing everything.

Why a standalone chatbot isn't enough

A language model that knows nothing about your organisation gives answers that are the same for every company. Value appears when the AI can see your own data: which order is open, what is in stock, what was agreed with this customer last year.

That data sits in systems that are often ten or fifteen years old. They have no API, or an API that was never meant for this volume. They contain fields nobody remembers the meaning of. And they are too important to use as a testing ground.

The old system stays yours

We do not touch your core system. Instead we add a layer beside it that exposes the data: an API layer, an event stream, or an MCP server an AI agent can talk to directly. That layer is ours, with tests and monitoring. If it falls over, your ERP does not.

Shadow mode first

New AI never goes live in one step here. First it runs in shadow mode: the system does its work, but nobody sees the outcome except us. Then human-in-the-loop, where an employee confirms every action. Only when the numbers hold up over a longer period does the switch go to autonomous, and even then with an emergency stop.

That takes longer than going live immediately. It is also the reason our systems are still running.

Is your AI stuck between demo and production?

In two weeks you will know what it takes to make it run: concrete advice, a risk report and a price.

Book a landscape scan