AI where it creates real value: document recognition, classification, data extraction – on-premise, on your own hardware.
Most of the information reaching a company arrives as a document: invoice, delivery note, order confirmation, certificate – as a PDF, a scan, a photo in a mail attachment. People read that well, systems do not. Text recognition alone does not solve it: it reads the characters but does not say which of them is the invoice number. Fixed templates do that — and they break as soon as a supplier changes their layout; every sender needs one of its own, kept up to date. So someone retypes it. At the same time, the obvious route via a cloud service is off the table for many of these documents: they carry prices, personal data or design details that should not leave the building. Between “not machine-readable” and “not cloud-eligible”, the work is left standing.
We start with one document type, not with a platform – and with real documents from your inbox, not with sample files. Against those we measure what a model actually recognises, compared with what is produced manually today. Only once that rate is established do we build. The models run on your own hardware where the data demands it; the choice follows the task, not the vendor's name. Recognised values are matched against your own master data – suppliers, cost centres, articles – because matching against known values is more reliable than free guessing. Cases below the agreed confidence go to a person, visibly flagged. In operation the recognition rate stays measurable, so a decline is noticed.
Invoices are recognised, assigned to the correct supplier account and cost centre, and passed on indexed to the archive or to approval. Matching runs against the company's local master data, processing is entirely on-premise, with no cloud connection. One such system is running in production today.
Delivery notes, order confirmations, certificates: vision models read reliably even when the layout changes or the scan sits crooked – where rule-based text recognition would need its own template for every sender. The extracted fields carry on as structured data into the existing route.
Where a case is not unambiguous, the system formulates a plain-language query and sends it to the responsible person – by e-mail or messenger. That is the difference between an AI that takes work off your hands and one that quietly produces wrong data.
From a comparison we run before rollout: a stack of real documents is processed once manually and once by machine, then held against each other field by field. That yields a rate per document type and the threshold above which a case runs through automatically. Everything below it goes to a person. We keep measuring in live operation, because document formats change – a falling rate should be noticed before someone in the invoice run notices it.
Only if the data demands it. For documents carrying personal data, prices or design details we recommend local models – processing then runs on hardware in your building. What that hardware has to be depends on document volume and required response time and is determined during the analysis, not estimated up front. For uncritical tasks a cloud model can be the faster route. Existing servers can often be used as well; a new purchase is not necessarily required.