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Don't build what you haven't measured.

We map the work, the systems, the data and the real cost of one process. You leave with a recommendation and a plan you can start building.

There will be no demo.

A demo runs on hand-picked data, with no access rules and no exceptions. It proves a model can write, not that a workflow will hold up in your business. We measure your real process, then build the smallest version that can be judged on your own numbers.

How do we start?

Every stage ends in a decision: continue or stop. You can walk away after any of them.

  1. Diagnostic

    Free · 20 minutes

    We tell you whether this is a case for AI, a simpler automation, or something that should stay human.

  2. Process audit

    Fixed scope

    We map the work, measure what it costs, and design the smallest version worth building.

  3. Production pilot

    Scoped after the audit

    One workflow built and integrated, with a measured baseline and a decision at the end.

  4. Managed operations

    Ongoing

    We monitor the workflow in production, handle exceptions and extend it as your rules change.

The audit fee comes off the build. Setup starts at €2,900, then €590 a month.

What's in each stage?

The diagnostic decides whether there is anything here. The audit says what to build, at what scope and at what price.

Free · 20 minutes

The diagnostic

  • One operational problem, explained in your own words
  • Questions about volumes, systems and consequences
  • An honest view on whether AI belongs here at all
  • A shared decision: is the audit worth doing

It's not an audit, not an architecture, and not free consulting.

Paid · fixed scope

The process audit

  • Interviews with the people who actually do the work
  • A map of the current process, manual hand-offs included
  • A measured baseline: volume, time, cost, errors
  • An assessment of the data, the integrations and the risks
  • The target workflow, the pilot scope and a fixed price

The audit may conclude that AI is not the answer. That's a useful result.

Why first projects fail

Companies start with the idea that impresses, not with the process that best balances value and risk. Six causes come up almost every time.

  1. 01

    Starting from the technology

    The tool was chosen before anyone agreed on the problem.

  2. 02

    No baseline

    Nobody measured the current process, so nobody can prove the new one does better.

  3. 03

    No owner

    Nobody inside the business owns the workflow once it's in production.

  4. 04

    No exception handling

    It works on the normal case and collapses on the remaining twenty per cent.

  5. 05

    No approval path

    Either a human checks everything forever, or nothing gets checked at all.

  6. 06

    No plan after go-live

    The system drifts, nobody maintains it, and by month three the team is back to the old way.

How do we rank an opportunity?

On six axes, never on how impressive a demo looked. The highest-value process is rarely the right one to start with: a big gain on poor data is exactly how a first project fails.

Axis What we look at
Business value Time tied up, the delay the customer feels, the risk created.
Frequency How often it happens, and whether there is enough repetition to justify building.
Data readiness Whether the information exists, whether access is reliable, and whether it is accurate enough to trust.
Actionability Whether the system can take a real action, or only produce text that a person then rekeys.
Measurability Whether quality can be measured, and whether we can test before widening autonomy.
Risk What a mistake costs, and whether it can be undone.

What people ask us about delivery

What happens when the system isn't sure?
It asks for clarification, returns nothing, or hands the case to a person, depending on confidence and risk. Saying nothing is often the correct behaviour.
How do you prove the value?
Before building, we record the starting position and agree the metrics. The pilot is then judged against that baseline, not against how impressive a demo looked.
How do you handle sensitive data?
The architecture follows the use case: private instances, European hosting, self-hosting, access controls, audit logs. Nothing you hand over trains a third party's model.

Bring a process, not an AI idea.

Twenty minutes is enough to know whether an audit is worth doing. If the answer is no, you'll have heard it before you pay anything.

Book the diagnostic What we automate