Invoices, orders and inboxes, turned into rows in your system.
Structured data out of documents that were never structured to begin with, with a person in the loop on the ones the model should not decide alone.
What this is
A stream of documents arrives in whatever format the sender used: a PDF invoice from one vendor, a scanned paper delivery note from another, an order forwarded as an email with three attachments. The job is turning each one into a record your system expects: a purchase order number, a set of line items, a total, a date, matched to the schema your ERP or accounting software already runs on.
The document type has to be decided before a single field gets read. Running the same extraction path on an invoice and a delivery note produces answers that look coherent and are wrong, because the model will happily fill in a field it was asked for even when the document does not contain it. Classification first, extraction second, is the order that keeps the output honest.
The number that matters most is also the one a model will confidently get wrong: a total that looks plausible but was never printed on the page. Extraction has to be grounded and checked, line items summed against the printed total, currency and date formats checked against what the target system expects, so a mismatch gets flagged instead of posted.
That checking is also why a person stays in the loop. Every extracted document gets a confidence score, and only the ones the model is unsure about land in front of somebody, rather than either an inbox nobody automated or a pipeline nobody is allowed to question. If what you actually need is answering questions across an archive of contracts or policies rather than pulling fields out of a flow of new documents, that is knowledge search over your own documents, not this.
What you get
Document classifier
Routes each incoming file (invoice, purchase order, delivery note, contract, plain email) to the right extraction path before any field is read.
Layout-aware field extraction
Tuned per document template so a line item in a table stays attached to its row, rather than a flat OCR text dump that loses the structure.
Arithmetic and schema validation
Totals checked against summed line items, dates and currency codes checked against the target system, mismatches flagged instead of posted.
Confidence-scored review queue
A person sees the documents the model is unsure about, not the whole inbox and not nothing.
Integration into the system you already run
Extracted data lands as records in your ERP, accounting software or CRM, not as a spreadsheet somebody re-keys.
Email intake
The inbox itself as the entry point: attachments pulled and forwarded threads handled, no separate upload portal to remember.
Audit trail
Every extracted field kept with a pointer to the source document and the confidence score at the time, for the invoice somebody questions six months later.
When this fits, and when it does not
A good fit
- You process enough of one type of document, invoices, purchase orders, claims, applications, that a person retyping them is the actual cost you are trying to cut.
- The documents arrive in a mess of formats, PDF, scanned paper, forwarded email, and no two vendors use the same template.
- A wrong number landing in the accounting system unreviewed is expensive enough that you need a review queue, not just a demo that looked accurate.
- The destination is a system you already run and the data needs to land there as records, not in a spreadsheet somebody maintains by hand.
Not a good fit
- You get a handful of these documents a month. A person doing it by hand is cheaper than building and maintaining a pipeline, and will stay cheaper for a while.
- The documents already arrive as structured data, EDI, an API feed, a portal export. You do not need extraction, you need integration; talk to us about system integration instead.
- You want zero human review anywhere in the loop. No extraction system, ours included, gets to zero on real-world documents, and pretending otherwise is how a wrong total gets posted.
- What you actually want is to ask questions across a pile of existing contracts or policies, not extract fields from a stream of incoming documents. That is knowledge search over your own documents.
- You do not yet know if this is worth doing at your volume. Start with an AI feasibility study, which finds the break-even point before anyone builds anything.
How it runs
- 01
Sample the real documents
A few hundred actual invoices or emails, not a clean set prepared for the meeting, because the vendor with the odd template is the one that breaks a generic pipeline.
- 02
Build classification and extraction
Per document type, measured against a hand-checked sample, so an accuracy number is real before it becomes a claim.
- 03
Wire in validation
Arithmetic checks, schema checks against the target system, and the confidence threshold that decides what a person sees.
- 04
Connect and shadow-run
Wired to the destination system and the review queue, run alongside the current manual process before anyone trusts it alone.
- 05
Hand over
Including the runbook for adding a new vendor template when one shows up, because one always does.
Questions we get
What accuracy can we expect?
It depends on document quality, and we will not promise a fixed percentage before seeing your actual documents. What we build in is the gap: a confidence score on every field, so the ones worth trusting are separated from the ones a person should check.
Does this replace the person doing data entry?
It changes the job from retyping everything to reviewing the cases the model flags. On messy real-world inputs, the role usually shrinks rather than disappears.
Can it read handwriting or scanned paper?
Yes, with meaningfully lower accuracy than a clean digital PDF. Worth testing against your actual samples before committing to it as the input format.
What happens when a vendor changes their invoice template?
Extraction for that vendor may need re-tuning. The classifier should catch the change as lower confidence and route it to the review queue rather than silently producing a wrong answer, and the runbook covers adding the new template.
Have a pile of documents that need to become data?
Send a real sample, the messier the better. An engineer reads it and gives you a straight answer on what is achievable and what it will take.