Automated Document Processing

What we automate most

Our most common document automation projects are invoice intake (extracting vendor, total, VAT, line items and pushing to QuickBooks/Xero/Priority/Hashavshevet), contract review (pulling key clauses and flagging deviations from a template), ID and KYC verification (reading passports, driver licenses and national IDs, cross-checking against watchlists), medical and insurance claim forms, and shipping/customs documentation. Anything with a predictable field layout is a good candidate, and even free-form documents work well with modern vision-language models.

How modern document AI beats old OCR

Traditional OCR gave you text. That's not enough — you also need to know which piece of text is the total, which is the vendor name, and whether the date is European or American format. Modern document AI solves this with layout-aware vision models that read the whole page the way a human would: headers, tables, handwriting, stamps, multiple languages on the same page. In practice we see 95–99% accuracy on structured fields after a few days of targeted fine-tuning with your own document samples.

The confidence-based review workflow

Even a great model gets things wrong sometimes — and for high-stakes numbers you want those errors caught. Every pipeline we ship attaches a confidence score to each field. Fields above a threshold flow through untouched. Fields below the threshold land in a small review queue where an operator sees the original document and the proposed extraction side by side and approves or edits it in seconds. Over time the queue shrinks as the model learns your documents, and the operator spends most of their time on genuine exceptions instead of every invoice.

Data, privacy and auditability

Document pipelines usually touch sensitive data — invoices, IDs, medical records. We design every pipeline so original files are stored in your own storage (typically S3 or GCS in a region you choose), PII can be redacted before it ever reaches the model, every extraction is logged with user, timestamp and confidence score, and the whole flow is auditable end-to-end for compliance reviews. For regulated industries we also run extraction inside your own cloud account with no data leaving your perimeter.

Document automation — frequently asked questions

How accurate is automated document extraction in practice?

Out of the box, modern vision-language models reach 90–95% field accuracy on common layouts like invoices and IDs. With a small round of fine-tuning on 20–50 of your own real documents, that typically rises to 97–99% on structured fields. Fields below a confidence threshold are routed to a quick human review queue, so final accuracy reaches effectively 100% without a human typing every document.

How fast does a document extraction pipeline run?

A single-page document typically takes 2–6 seconds end-to-end — that includes OCR, layout analysis, field extraction, validation, and writing the result to your system. Batch processing of thousands of documents runs in parallel and scales with whatever throughput you need.

Can the system handle scanned or handwritten documents?

Yes. Modern models handle scans, photos of paper documents and even handwritten fields with very good accuracy. Quality drops if the image is badly rotated or very low resolution, so we also build in an image-preparation step (deskew, contrast enhancement) before extraction.

What document types work best?

Documents with a repeatable layout (invoices, receipts, purchase orders, IDs, insurance forms, bank statements, shipping manifests) work exceptionally well. Free-form documents like emails and contracts also work — we just design the pipeline differently, pulling out named entities and key clauses rather than fixed-position fields.

How do you integrate the extracted data into our accounting or ERP system?

We write directly into the destination system via its API — QuickBooks, Xero, Hashavshevet, Priority, SAP, Odoo, NetSuite, and most custom ERPs have one. For systems without a good API we use scheduled file drops or RPA. Either way, the person who used to retype invoices stops retyping them.

What does it cost to run at scale?

Per-document cost usually lands between $0.02 and $0.15 depending on complexity and whether we're using a general model or a fine-tuned one. For 10,000 invoices a month that works out to roughly $200–$1,500, versus the thousands of dollars a human would cost to type them. Exact pricing is part of the discovery phase.