The problem
Documents arrive by email, upload, and scan, and a person retypes them into the accounting system, the spreadsheet, and the tracker. The keying is slow, the errors surface weeks later, and nobody can say which document a number came from.
What goes wrong
- The extractor is trusted blind. It reads a total wrong on page two, nobody checks, and the error is found at month end.
- Everything goes to review. The queue is as much work as keying was, so the team stops reading it and approves on sight.
- Records post with no trail. A number lands in the ledger and nobody can find the page it came from or who said yes.
The approach
- Start with 50 to 200 of your real documents. Agree the fields, the checks, and the confidence thresholds in writing before anything is built.
- Validate every field before a person sees it: totals add up, the vendor is on your list, the PO exists, the dates make sense.
- Put a review queue in the tool your team already reads, with the source page beside the fields, and auto-approve only what passed every check.
- Post approved records to the systems you run and log every step per document.
When it works
When the same kinds of documents arrive every week, the fields they carry are known, and a named person is willing to own the review queue and the thresholds.
Why intake first
Most back offices run on retyping. Invoices, contracts, and forms arrive as PDFs, scans, and photos, and a person keys each one into the system that needs it. The errors are quiet. A wrong total posts, a vendor gets paid twice, and nobody notices until the close.
The real problem is that a person is the parser. The pipeline reads the document, pulls the fields, checks them, and queues the record for a yes. Your team reviews instead of types. The same pipeline fits customer POs and supplier invoices in manufacturing, loan document review in banking, and submissions and first notice of loss (FNOL) in insurance.
What the pipeline should do
Read
Documents arrive from a watched inbox, a shared folder, or an upload link, as PDFs, scans, or photos. The pipeline recognizes the type, splits multi-document PDFs, and pulls the fields. Each field carries a confidence score your team can see.
Queue
Everything starts in review, in Slack, Teams, or a web queue. Once the shadow run shows which records arrive clean, you can let the ones that pass every check and clear the threshold post on their own. Every correction is logged and used to tune the checks.
Post
Approved records land in the accounting system, the spreadsheet, or the project tool, by API where one exists and through the same screens your team uses where it does not. We deploy and operate directly within your existing security perimeter, implementing NIST AI RMF and SOC 2-aligned controls. The security page covers how we handle your data.
How we build it
First comes sample and schema. You send 50 to 200 real documents. We agree the fields, the checks, and the thresholds in writing, and we get read access to the destination system.
Next is extraction on your sample, with accuracy measured per field. The queue goes live in shadow mode. Your team sees records and corrects them. Nothing posts.
Then approved records post to the live system while your current process runs beside it, so you can compare. Last is switch-over. Your team stops keying, the thresholds are set from the corrections, and you get the runbook.
When it does not work
It does not work for documents that are different every time, or for a team that will not read the exceptions. The pipeline gets better because someone corrects what it missed.
Where to start
If keying is the first thing to fix, start with Document Intake. If you are not sure, ask Roo, Webaroo's AI: Could AI read our invoices and forms so my team only reviews them?
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