When your case handlers can no longer absorb the volume of documents and files, an AI layer is added to your application: it reads, extracts, classifies, prepares. A human keeps the decision. It is useful, measurable, and it only goes on top of a process that is already stable.
Talk about your volumeThis is not commercial caution: a model wired into a vague process produces errors faster, not fewer.
Enough documents a month for the time saved to be counted in days. Below that, good assisted entry is enough — and costs ten times less.
Data is entered once, rules are written down, approvals are logged. Otherwise AI amplifies the existing disorder.
A reviewed proposal costs little when it is wrong. We never let a model decide alone on an entitlement, a refusal or a payment.
A set of real cases annotated by your experts, a success rate measured before go-live, and tracked afterwards.
Five uses, all inside your application. Not a separate assistant, not a chat box on your website.
Invoices, certificates, statements, letters. Fields are extracted, attached to the right case, and checked against what the application already knows.
The case reaches the handler already prepared: documents checked, inconsistencies flagged, a proposed decision with the evidence behind it.
Incoming mail, requests, reports: classified by nature, priority and responsible department, on arrival.
Years of cases, decisions and correspondence, searchable in plain language — with the source cited for every answer.
Justified decisions, reply letters, case summaries: a first draft grounded in the case data and your templates.
Because it sells very well and it does not hold. You will hear it on the first call, not after signature.
An entitlement granted, a refusal justified, a payment committed: those are acts, not predictions. A human signs.
If nobody knows who approves what, start there. It sells less well and pays far better.
An assistant that answers your users vaguely saves your teams no hours at all. It moves the problem to the department that receives the complaints.
No named case files sent to a service that uses them to train its models. Hosting and processing are decided at scoping, in writing.
Four steps, six to ten weeks for the first use. Step 2 can conclude that it is not worth it — that is an acceptable outcome.
We time the real handling: how many documents, how many minutes each, where the time goes. We come out with the candidate use and the theoretical gain, in hours and in euros.
Your experts annotate a sample of real cases. We measure what the model gets right, what it misses, and at what cost per case. The figure is compared against the gain from step 1.
Possible outcome: it is not worth it. We tell you so, with the figures.
The layer is wired where the handler already works: confidence thresholds, human review queue, a log of what the AI proposed and what was decided.
The success rate is tracked over time, and your handlers' corrections are used to improve it. If quality drifts, the use is switched off and human handling resumes — without interruption of service.
The questions boards ask before committing to anything.
No. It is written into the scoping document: where the data is processed, by which provider, with what no-training clause, and what is retained.
For the most sensitive cases, processing hosted on your own infrastructure is possible — a cost/quality trade-off we put on the table openly.
It gets things wrong, regularly, and the application is built for that: confidence threshold, human review queue, a full log of what was proposed and what was decided.
A reviewed error costs a few minutes. An invisible error costs a dispute — hence the refusal of decision-making AI.
A cost per case handled, which we measure at step 2 and which you see every month.
It is an operating line, not an opaque package: if the cost per case exceeds the human time saved, the use should not exist.
That is not for us to say, but here is what we observe: the organisations that call us have a processing backlog, not surplus staff.
AI absorbs the keying and the sorting; assessment, judgement and contact stay human — and become the heart of the job again.
Rarely in a useful way. AI with nowhere to put its results produces files someone will have to re-key.
Start with custom development — often, simply tooling the process is enough to make the ceiling disappear.
A written reply within two business days. If your process is not ready for AI, the answer will say so — and propose the step before.