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AI Document Processing
AI for document processing: field extraction, classification and reconciliation as a step in a governed process with human control on contested cases — not a standalone "magic" product.
AI document processing is a frequent first step into automation: “we drown in invoices and forms, let’s recognize them”. But “load an image into a model and grab the fields” is a working demo and a poor production system. We build document processing as a step in a governed process: recognition, extraction, rule-based reconciliation and human control where the result is uncertain.
What “AI document processing” really means
It’s a stack of layers, not one model: pulling the file from a channel (mail, portal, messenger, scanner), text and table recognition, type-specific field extraction, classification (is this an invoice or an act?), reconciliation against masters and accounting systems, handoff of contested cases to a human. Every layer is testable and replaceable — unlike the “drop in, get out” black box.
OCR and LLM — the honest line
OCR turns an image into text: characters, coordinates, tables. On clean uniform forms of decent quality, that’s enough: add field-extraction rules and close the task without an LLM. Cheap, predictable, fast.
LLM extraction is needed when documents are heterogeneous: different invoice templates, free-form text in emails, atypical wording in contracts. The model relies on the recognized text and pulls fields by description rather than by coordinates. Higher cost, subtler errors, confidence thresholds required.
In production it’s a stack: OCR does the rough work on text, the LLM the fine work on meaning, deterministic reconciliation catches what shouldn’t be trusted. Trying to live on one layer alone is the common reason behind “our recognition works but accounting still gets errors”.
Extraction, classification, reconciliation
Field extraction — dates, amounts, tax IDs, numbers, counterparty, line items. Each field gets a confidence score; below threshold goes to the manual queue.
Classification — document type, status, route. The step that decides where the document goes next and which logic applies.
Reconciliation — cross-checks against rules and accounting systems: does the line-item sum match the total, does the counterparty exist in the registry, is the contract number valid. Reconciliation is what separates “recognized” from “safe to post”.
Human control isn’t an “optional feature”
Any AI document pipeline produces errors; the only question is whether you see them before they hit accounting. Hence the explicit confidence thresholds: below — to a manual confirmation queue, above — straight through using the same contract people work with. Each document shows which field was extracted how and from which line — honest tracing that removes the “what if AI missed something” fear.
Where it actually delivers
Accounting (invoices, acts, waybills, bank statements), HR (applications, corporate docs), insurance and medical forms, customer requests with attachments, scanned contracts. Effect is measured on two axes: processing time per document and the share of documents that go through with no human touch. Both are measured before and after — without that, “we deployed AI” is marketing, not a result.
Why this, not a boxed recognizer
A boxed recognizer handles typical forms but doesn’t know your reconciliation rules, routes and accounting integrations. We build document processing as a step of an operational agent with explicit contracts, observability and human control. More: AI adoption, business process automation and engineering cases.