Automation & AI work companion
Intelligent digitisation
Almost every business has that stock no one touches any more: files, scan folders, PDF collections, email attachments, Excel lists grown over years. All there – none of it usable.
Intelligent digitisation
01Intelligent digitisation
Opening it up used to mean: retyping. Today AI reads even difficult scans, recognises what's on a delivery note, sorts photos by site and turns a box of paper into a clean table. We set this up as batch processing: the stock runs through, a human checks samples, and in the end your data sits where you can work with it – in a table, your filing system or as the basis for a knowledge base. Dead stock becomes material you can calculate, search and work with.
Straight up: there's no 100% recognition – sample checking by a human is always part of it. What may go to the cloud and what stays local we settle BEFORE processing. And: we make stock usable – audit-proof archiving (GoBD) stays the job of your specialist systems.
02Demo
Three documents of the kind lying around in any business. Pick one – on the right you see what the recognition makes of it. Watch the flagged field: that's exactly what the spot-check is for.
The route behind it is a pipeline: recognition and rules work through the stack – and anything uncertain branches off to a human instead of quietly landing in the table. Only once the spot-check rate holds does the whole batch run through.
The dashed loop is the core: what's uncertain goes to the check and back – so the recognition gets better with each stack.
03How it works
- 01
Survey the stock
What sits where, in what format, in what quantity – and what should the data be good for afterwards? An afternoon of surveying saves weeks in the wrong direction.
- 02
Set the target structure
Where should the data go – table, filing system, CMS, knowledge base? And which fields are really needed: date, amount, project, client? The target structure decides what the recognition has to look for.
- 03
Pilot with a real batch
We take a representative slice – one folder type, one box – and set recognition and sorting on it. The pilot shows what the stock gives up.
- 04
Check quality and sharpen
You or someone from the team checks samples: are the recognised values right? Where the recognition misses, we sharpen rules and logic – until the rate holds.
- 05
Process the whole stock
Then the whole stock runs through and lands in the target system. On request the path stays in place: new scans and attachments are processed along automatically from now on.
04Stages
The entry point is deliberately cut small – a single stock is a perfect, measurable pilot.
Entry: one stock, opened up once
A clearly bounded stock – one document type, one archive segment – is processed once. The result: a structured table or filing.
Extended: the running path
The one-off action becomes a repeatable pipeline: new scans, email attachments and uploads land automatically structured in the right place.
Full build: the stock works along
The opened-up data flows into a knowledge base or an AI knowledge system – your stock becomes queryable and works along in everyday life.
05Technical implementation
- Recognition: OCR plus AI extraction – modern vision models read even skewed scans, stamps and handwriting portions.
- Classification: document types (invoice, delivery note, note …) are recognised automatically and sorted by your own rules; duplicates fly out.
- Processing as a pipeline: scripts or a small processing path – local or in the cloud, depending on the data-protection situation of your material.
- Review interface: a simple view for samples and corrections. The human stays in the loop – that's the concept, not a compromise.
- Output: structured to where you work – CSV/table, database, DMS, CMS or knowledge base.
- Input depending on the stock: a scanner or scan service for paper, a folder import for digital stock, a mailbox connection for email attachments.
for context · This shows how something like this usually gets built – not how it has to be built for you. What actually fits is worked out in the project: around your existing setup, your team, and what stays easy to maintain.