Extracting the numbers from documents that arrive in every possible format used to need a template per supplier and broke whenever one redesigned their letterhead. It genuinely doesn’t any more — which makes this the clearest case for putting a language model inside an automation.
In short: documents arrive by email, portal or scanner. The system reads them whatever the layout, pulls out the fields you care about, checks them against your own records, and either files the document or puts it in front of a person with the discrepancy highlighted. Nothing is posted to your accounts on a guess.
What gets read, and what happens next
Supplier invoices
Supplier, date, invoice number, line items, tax, total, purchase order reference. Matched against the order and the delivery note, with the three-way match done automatically and only the mismatches surfacing.
Purchase orders and delivery notes
The other two sides of that match, often arriving as photographs of paper from a phone in a yard. Handled, provided the photo is legible — and rejected clearly when it isn’t.
Forms and applications
Filled-in PDFs, scanned paper, emailed spreadsheets that were supposed to be a form. Extracted into your system with the source document attached to the record.
Contracts and certificates
Pulling the dates that matter — renewal, expiry, notice period — into a calendar so an insurance certificate lapsing becomes a reminder rather than a discovery.
The confidence threshold is the whole design
Extraction is never perfect, and a system that pretends otherwise will eventually post a wrong figure into your accounts. So every extracted field carries a confidence score, and anything below the line goes to a person — with the document open, the uncertain field highlighted, and the rest already filled in.
Where you set that line is a business decision rather than a technical one. Set it high and more documents need a human glance but almost nothing wrong gets through. Set it low and throughput rises along with the risk. We start conservative, measure how often the human agreed with the machine, and move the line with evidence.
Totals get a harder rule regardless of confidence: if the line items don’t add up to the stated total, it goes to a person. That check catches more real problems than the extraction confidence does.
What we won’t do
Pay anything automatically
Extraction, matching and preparation, yes. Releasing money stays behind an approval, with a value threshold you set. An automation that pays four hundred invoices twice is a very efficient disaster.
Quote an accuracy figure before seeing your documents
Clean PDFs from a portal and phone photographs of crumpled delivery notes are different problems. We run a sample of your real documents and report what it actually achieved on those.
Throw away the original
The source document stays attached to the record it created. When a figure is queried eight months later, someone needs to see the piece of paper it came from.
How it starts
Send us ten to twenty real documents, including the two worst ones you receive. We run extraction against them and send back a field-by-field report of what it got right, what it got wrong and what it flagged as uncertain. That report is the basis of the quote, and occasionally it’s the basis of us saying the documents are too poor for this to be worth it.
What our clients say
The businesses we’ve built for, in their own words.
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“When I approached Abedin Tech with my land share selling plan, I wasn’t sure how it would work. But thanks to their precise strategy and powerful marketing, my business is now thriving. They truly understand their clients’ needs and go above and beyond.”
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Real Estate
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Isabella Scott
Send the worst documents you receive
Not the tidy ones. We’ll run extraction across them and report field by field what worked, so you’re buying against a measured number rather than a claim.
No, and that is what changed. Older document capture required a template per layout and broke whenever a supplier redesigned their letterhead. Language models read documents they have never seen before, which makes handling forty suppliers with forty formats practical rather than a maintenance burden.
How accurate is document extraction?
It depends entirely on your documents, which is why we will not quote a figure before seeing them. Clean PDFs from a supplier portal and phone photographs of crumpled delivery notes are different problems. Send ten to twenty real documents including the two worst you receive, and we report field by field what extraction actually achieved on them.
What happens when it is not sure about a field?
Every extracted field carries a confidence score, and anything below your threshold goes to a person with the document open, the uncertain field highlighted and everything else already filled in. Totals also get a hard rule regardless of confidence — if the line items do not add up to the stated total, a human looks at it. That check catches more real problems than the extraction confidence does.
Will it pay invoices automatically?
No. It extracts, matches against your purchase order and delivery note, and prepares the payment — but releasing money stays behind a human approval with a value threshold you set. Automating the payment step is how a duplicate-invoice problem becomes a four-hundred-payment problem.
