Reads the Document, Extracts What Matters, Files It Correctly
Invoices, forms and unstructured documents go in. Structured data comes out, checked against what it should reconcile with, and filed where your team already looks for it — not dumped into a spreadsheet for someone to clean up.
Three Steps, Every Document
The same pattern regardless of document type: read it accurately, decide what to do with what it found, and act — or flag it for a person when the extraction isn't confident enough to act on.
Reads
Extracts the fields that matter — line items, totals, dates, names, reference numbers — from scans, PDFs, photos or email attachments.
Checks
Reconciles what it read against what it should match — a purchase order, an expected total, a known vendor — before treating it as correct.
Acts
Files the data where your team already works, updates the related record, or routes the document itself onward — a completed step, not a suggestion.
What We Connect It To
On the intake side: wherever documents already arrive — a shared inbox, an upload folder, a scanner's output directory. On the output side: your accounting system, document management tool, or CRM — whatever currently holds this data once a person finishes typing it in by hand today.
Where documents need to be checked against another source — a purchase order, an existing customer record — the agent gets read access to that system too, scoped to exactly the lookup it needs.
Typically Reads
Incoming documents from an inbox or folder, reference records to reconcile against.
Typically Writes
Structured records in accounting, document management or CRM systems, and flags on anything low-confidence.
When It Flags Instead of Files
The extraction confidence is low
A smudged scan, unusual formatting, or handwriting that doesn't parse cleanly gets a human check instead of a guess.
Reconciliation doesn't match
An invoice total that doesn't tie to the purchase order, or a vendor that's not in your records, goes to a person to resolve.
The document type is unfamiliar
Something outside the shapes it was built and tested against is routed for review rather than forced through.
The action has financial consequence above a threshold
High-value invoices or unusual amounts get a sign-off step before anything is filed as final.
What a Good Brief Looks Like
The single most useful thing you can bring is real documents — not a description of them.
Bring These
- A sample set of real documents, including the messy ones
- What fields you need extracted, and what "correct" means for each
- Where this data lives today, and who currently types it in
We'll Work Out Together
- What confidence threshold triggers human review
- Which reconciliation checks actually matter for your process
- Whether a dollar threshold needs a sign-off step before filing
Document Processing Questions
What kinds of documents can it handle?
Invoices, purchase orders, application forms, contracts, receipts — anything with a reasonably consistent structure or a predictable set of fields, whether it arrives as a scan, a PDF, a photo, or an email attachment.
What about documents that don't follow a template?
That's the harder, more common case, and the honest answer is it depends on how unstructured 'unstructured' really is. A handwritten note with wildly inconsistent formatting is a different scoping conversation from a form that varies only in a few fields — we'll test against real samples before committing to a number.
Does it act on the data, or just extract it?
Both, within the scope you set. Extraction alone still saves manual re-typing. Acting on it — filing an invoice against a purchase order, updating a record, flagging a mismatch — is what makes it an agent rather than an OCR tool with extra steps.
What happens when it misreads something?
It flags low-confidence extractions for human review instead of filing a guess as fact — a smudged total, an unfamiliar layout, or a number that doesn't reconcile against an expected value all fall into this bucket by design.
Where does the extracted data end up?
Wherever your team already looks for it — your accounting system, a document management tool, or a CRM record — rather than a spreadsheet export someone has to import by hand.
Send Us a Sample of What You're Processing by Hand
We'll tell you plainly whether it's a straightforward extraction job or a harder one, and why.