Services / Document processing
Stop paying qualified people to retype PDFs.
Invoices, bank statements, loan files, contracts, client records. We build the system that reads them, pulls out what matters, checks it, and puts it where it belongs.
Build price and delivery timing follow the paid assessment and agreed acceptance tests. Fixed-scope builds start at $8,500.
The short version: Finance document intake is useful when the same document types arrive repeatedly and the downstream rules are clear. We design a pipeline that classifies each document, extracts only the fields the workflow consumes, validates them against existing records, and routes anything uncertain to a person. Accuracy is measured on your own representative test set before go-live.
The problem
The work nobody bills for and everybody does.
It rarely appears in a capacity plan because it's spread across everyone's week in twenty-minute slices.
A client emails a PDF containing a bank statement, two invoices and a credit card summary in one file. Someone splits it. Someone renames it. Someone types the numbers into a system that already holds most of that information. Multiply by every client, every month, and you have a full-time role nobody deliberately hired for.
The reason it persists is that each instance is small. No single document justifies a project, so the work never gets escalated. It just quietly sets the ceiling on how many clients your team can carry, and it's usually the reason a firm says it needs to hire.
mixed inputs are separated by document type before extraction.
Pipeline designfields are checked against totals, formats and approved records.
Control designuncertain items route to a reviewer with their source evidence.
Exception designaccuracy is established on the client-approved golden test set.
Acceptance designWhat we build
Intake, understood and routed.
- 1
Classify before extracting
The system identifies what each document is, splits multi-document PDFs, and sends each type down its own path. This step sounds trivial and it's where most naive extraction projects fall over, because an invoice and a bank statement need entirely different handling.
- 2
Extract the fields that actually matter
Not everything on the page. The specific fields your downstream process needs, mapped to your systems, with the formats you already use. We define this with your team rather than guessing from a template.
- 3
Validate against what you already know
Cross-check against existing records: does this vendor exist, does the total match the line items, is this within the range you'd expect from this client. Validation is what turns extraction from a demo into something you can rely on.
- 4
Route by confidence, not by hope
High-confidence records post automatically. Anything uncertain goes to a review queue with the source document alongside the extracted values, so checking takes seconds rather than reopening the file. Thresholds start conservative and loosen once we've measured real accuracy on your data.
Honest comparison
Why generic OCR disappoints.
What off-the-shelf extraction gives you
- Good accuracy on clean, standard documents
- A template per layout, which breaks when a client changes their format
- No validation against your existing records
- Everything dumped into one queue regardless of confidence
- No idea what to do with a multi-document PDF
What we build
- Classification first, so mixed packets and new layouts are handled
- Extraction scoped to the fields your process actually consumes
- Validation against your ledger, CRM or case system before anything posts
- Confidence routing, so people only look at what genuinely needs a person
- A system you own, documented, that we tune as your document mix changes
The goal isn't perfect extraction. It's knowing precisely which 5% a human should look at, and being right about it.
Questions
What people ask us first.
Our documents are messy and inconsistent.
That's the normal starting point and it's the argument for classification-first design rather than templates. Templates assume consistency you don't have. We'd rather see your worst month of documents than your cleanest, because the edge cases are what determine whether this holds up.
What accuracy can we expect?
It depends on your document mix and we won't quote a number before seeing it. What we will commit to is that accuracy is measured on your own documents before go-live, thresholds are set from that measurement, and anything below threshold goes to a person. You'll know the real figure rather than a vendor's benchmark.
Does this replace our data entry staff?
It replaces the typing. In most firms those people move to review, exception handling and client work, which is both more valuable and considerably less tedious. If you're planning headcount reductions we'd rather you know now that we don't sell this as a redundancy plan.
Can it work with the software we already have?
Usually yes. We build around your existing systems rather than asking you to replace them, and integration is generally less of a constraint than the state of your reference data.
Full pricing is on the pricing page, and how we scope work is on the method page.
Next step
Send us your messiest PDF.
Genuinely. Pick the document type that causes the most manual work and we'll tell you what a system would do with it and what it would take to build.