Automation & AI work companion
Intelligent digitisation
Almost every business has a pile of records nobody touches any more: lever-arch files, scan folders, collections of PDFs, email attachments, Excel sheets that have grown over the years. It's all there – and none of it is usable.
You'll get …
- 1
Records you can work with
What used to sit in folders, scans and email attachments is now structured – in a spreadsheet, your filing system or a knowledge base.
- 2
Automatic recognition, spot-checked
The batch goes through automatically and a person checks samples – anything uncertain is flagged rather than guessed.
- 3
An ongoing pipeline, if you want one
From then on, new scans, email attachments and uploads are processed automatically and end up in the right place.
01Intro
AI can now read even poor-quality scans, pick out what's on a delivery note and turn a box of paper into a clean spreadsheet. We set this up as batch processing: the records go through, a person checks samples, and at the end your data is somewhere you can work with it. Dead records become material you can calculate with, search and put to use.
There's no such thing as 100% recognition – a person always checks samples. What can go to the cloud and what stays local is agreed BEFORE processing starts. And we make records usable – audit-proof archiving (GoBD) remains the job of your specialist systems.
02How it works
- 01
Take stock
What's where, in what format and in what quantity – and what should the data be good for afterwards? An afternoon spent taking stock saves weeks heading in the wrong direction.
- 02
Decide on the target structure
Where should the data go – spreadsheet, filing system, CMS, knowledge base? And which fields do you need: date, amount, project, client? The target structure determines what the recognition has to look for.
- 03
Run a pilot on a real batch
We take a representative sample – one type of folder, one box – and set up recognition and sorting for it. The pilot shows what the records can yield.
- 04
Check quality and refine
You or someone on your team checks samples: are the recognised values correct? Where the recognition gets it wrong, we refine the rules and logic – until the accuracy rate is good enough.
- 05
Process all the records
Then everything goes through and ends up in the target system. If you like, the pipeline stays in place: from then on, new scans and attachments are processed automatically too.
03Stages
The entry level is deliberately small – a single set of records makes a good, measurable pilot.
Starter: one set of records, opened up once
A clearly defined set of records – one document type, one section of the archive – is processed in a single run. The result: a structured spreadsheet or filing system.
Next step: the ongoing pipeline
The one-off job becomes a repeatable pipeline: new scans, email attachments and uploads are structured automatically and filed in the right place.
Full build: records that do some of the work
The data flows into a knowledge base or an AI knowledge system – you can ask your records questions, and they become part of everyday work.
04Try it yourself
Three documents of the kind lying around in any business. Start the recognition – on the right you see what it reads out. For the flagged field, you decide: 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.
05Technical implementation
- Recognition: OCR plus AI extraction – modern vision models can read even crooked scans, stamps and handwritten notes.
- Classification: document types (invoice, delivery note, memo …) are recognised automatically and sorted by your own rules; duplicates are weeded out.
- Processing as a pipeline: scripts or a small processing setup – run locally or in the cloud, depending on how sensitive your material is.
- Review interface: a simple view for checking samples and making corrections. A person stays in the loop.
- Output: structured data sent to wherever you work – CSV/spreadsheet, database, DMS, CMS or knowledge base.
- Input depends on the records: a scanner or scanning service for paper, a folder import for digital files, 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.