THE FLAGSHIP
AI agents on one concrete process
We deploy an agent that does one bounded job — answering routine questions, extracting invoices, or finding answers in internal documents. Always as a pilot with a metric agreed upfront, never as a “digital transformation”.
What it usually looks like today
Support answers the same thing all day
Where is my parcel, how do I return this, what is the delivery time. Dozens of e-mails a day that have exactly one right answer.
Invoices are retyped by hand
A document arrives by e-mail and someone copies eight fields into the system. At a hundred documents a month that is a day of work nobody enjoys.
Knowledge lives in ten folders
The answer is in a policy document, but finding it takes half an hour. So people ask a colleague, who is also guessing.
How an agent gets built
One process, not a department
We take the process with the best return from the analysis and draw a boundary around it. Anything outside that boundary goes to a person.
Rules you approve
The agent does not decide for itself. What it may answer and what it must escalate is written down and signed off before it goes live.
A pilot before scaling
One process and measurement first. Only once the numbers hold do we add another — not the other way round.
Oversight and retraining
An agent without oversight drifts. We watch accuracy, correct it and retrain — which is why operations is part of the deal, not an add-on.
What we measure during the pilot
%
of queries resolved without a person
s
time to the first answer a customer sees
%
accuracy of the data extracted from a document
When we will tell you not to build an agent
An agent does not make sense everywhere. Three cases where we say so plainly, even though we would earn from the work:
Every case is different
If each case is handled individually and no rule can be written down, there is nothing for an agent to repeat. Order in the data helps more than a model here.
The volume is too small
Ten documents a month is not worth automating. We work out the return in advance; if it does not add up, there is no point starting.
The knowledge exists only in people’s heads
If nowhere says how a question should be answered, an agent has nothing to learn from. Writing it down comes first — and that is your work, not ours.
Frequently asked
Will an agent replace our support people?
No. It takes the repetitive questions so your people have time for the ones that need judgement. Anyone wanting to replace a department has come to the wrong firm.
Do we need everything in the cloud first?
Not necessarily. Sometimes connecting one system and tidying one folder is enough, and we can run the language model on-premise on your own hardware. We will show you where the current state of your data limits reliability.
Where does our data go?
In the cloud, processing follows the GDPR and data stays in the EU. If it may not leave your network at all, the model runs on-premise at your site and nothing goes out. Exactly what the agent can see is part of the scope you approve.
What if the agent answers wrongly?
That is why it is deployed on one bounded process with escalation, and why we measure accuracy from day one — without measurement you learn about the mistake from a customer. What the measurements expose we then fix with prompt and context engineering, or by fine-tuning the model on your data.