Similar layouts, high volume
The same kind of invoice or delivery note, day after day. The difference is the numbers and the name.
Related note Which fields to define before document data capture
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A document in, fields outCaptured fields, then a person checks
Capture, checking, and export for forms, contracts, and documents that repeat — less copying by hand.
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Amount
Name
Which documents are worth it
Enough volume, and a stable layout
If every day is the same kind of PDF and only the numbers change, copying fields often takes longer than the judgment.
Copying takes longer than deciding
The hard part is the exception, not typing a number into a sheet.
The next system wants structured fields
Finance or the warehouse receives a date, an amount, and a name — not the whole image.
Fields and tolerance come from real documents
We start from real samples, including unclear scans, and then decide which fields to pull and which errors must stop the run.
Scan quality, handwriting, and a layout that changes all affect the result. Captured fields go to a review queue, and a person checks them before anything moves downstream.
A scan with date, amount, and name marked
Fields out. Exceptions into a queue.
The model proposes fields. Posting, filing, and any statutory use stay with a person.
Agreed fields for each document type
Date, amount, name, and the rest are taken from your samples, not from a generic template.
A first check against master data or a rule
A supplier or an amount that does not match goes straight to review.
Low confidence and conflicts go to a queue
Handwriting, damage, and a sudden layout change are not treated as already correct.
Export into the sheet or system you name
The format follows the import you already use, so the fields are not copied again.
How samples are collected
Set the fields from real samples
Capture trained only on clean samples fails on a dirty scan.
Extracted rows enter a review queue. They are not posted.
Collect samples
Real documents, including unclear ones, to set the fields and the tolerance.
Capture and check
A capture layer, and a written list of errors that must stop the run.
A review queue
The colleague in charge can correct a field, send it back, or confirm it.
Review
Amounts, tax identifiers, and signatures are sampled
Amounts, tax identifiers, signatures, and legal clauses need a person to sample them. The model proposes fields. It does not decide to post or to file.
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Related work
Internal knowledge base and assistant
Policies and files scattered across shared drives and chat, turned into answers a colleague can search — and a person still reviews.
Workflow agents
Repeated steps turned into an agent that can run, pause, and be traced. A person still approves the points that matter.
Customer, sales, and voice assistants
Incoming messages are sorted and a reply is drafted. An agent decides whether to send it.
Analysis and decision dashboards
Sources, rules, and the questions still to answer, on one board, so the figures you already have can be read together.
Custom software, websites, and applications
A workbench people actually open, built on top of the systems already in place.
Market and competitor research
Faster collection and sorting of public material. A conclusion counts only after a researcher has read it.
Multilingual content and localisation
Source, glossary, translation, and a local edit, on one repeatable line.
Marketing analysis and creative revision
Last round’s creative and where it went, arranged so the next round’s headlines, outlines, and landing pages can be rewritten.
Cross-border operations
Repeated order, document, and reconciliation steps across two places, joined into a flow that can pause for review.
Assessment, prototypes, and integration
Assess the current work, test it on a small sample, and only then connect it to the systems you already use.
Talk about the work first
Start with the step that is stuck
The consultation is free. Describe the problem, and we will reply and confirm the scope with you.