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Fewer hours on repetitive work — without losing control of a single decision.

Classification, extraction and routing of documents, email and requests — built by us, measured against your current process, with human review where the decision carries weight.

Classifying e-mails, extracting data from invoices, routing requests, separating attachments. Work that eats hours every day and rarely calls for human judgement — except when it genuinely does, and those cases have to be identified.

The point that decides whether this works

Automation that gets 92% of cases right looks good until you realise the other 8% went through unnoticed. What makes automation usable is not the accuracy rate: it is knowing, case by case, when it should not decide alone.

So every decision carries a confidence score and there is a threshold. Above it, the case flows automatically. Below it, it goes to a human review queue with the context needed to decide in seconds.

What we automate

  • Classification. Assigning type, category, priority or department to incoming documents, e-mails and requests.
  • Extraction. Pulling structured fields from invoices, delivery notes, contracts and forms — including scans and PDFs with no text layer.
  • Routing. Getting each case to the right person or queue, with the summary already written.
  • Assisted replies. Proposing an answer for a person to approve or correct, instead of writing from scratch.

What you get

  • The automated flow wired into the systems you already use, without forcing the team to change tools.
  • A review queue for low-confidence cases, with the reason for the doubt made explicit.
  • Metrics: volume handled, share fully automated, human correction rate and time saved — measured, not estimated.
  • Threshold tuning over time, as real data shows where the system is reliable.

Starting small is deliberate

We start with one document type or one channel, measure for a few weeks and only widen with numbers on the table. It is slower at the start and much faster at the end, because it avoids the rewrite that follows a big-bang launch.

Frequently asked questions

Does it work with scanned documents?

Yes. Documents with no text layer go through optical recognition before extraction. Scan quality affects the result, and that is reflected in the confidence score assigned.

How long until it is in production?

A first flow is usually running in limited production within four to eight weeks, depending on access to the systems involved.

Does the team become unnecessary?

That is neither the goal nor what happens. What changes is the nature of the work: instead of handling everything, the team handles the exceptions — which are the cases that justify having a person look at them.

What the sector reports

Published reference figures for projects of this kind. These are not our results — they are the order of magnitude that frames an investment decision before there is measurement of your own.

80–90%
reduction in factual errors when answers are anchored in documents (RAG) versus a model without retrieval
30–60%
reduction in average cost per interaction on AI-supported channels
>80%
of companies plan to integrate AI agents within one to three years

Sources: World Economic Forum and Capgemini (2025); published market references for enterprise assistants (2026). We always measure your case against your current baseline before claiming any gain.

Shall we talk about your case?

A first conversation is about working out whether there is work here worth doing. If there is not, we will say so.

Book 20 minutes

See our AI in action — this assistant was built by us, with the same technology we sell.

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