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Documents & data

Document Processing AI Agent

AI document agent: extracts fields from invoices, contracts and acts, validates by rules and exports to your accounting system. Contested cases to a human, the rest automatically.

AI Document Processing

“We drown in invoices and forms, let’s recognize them” is a common entry into automation. But “load an image into a model and grab the fields” is a working demo and a poor production system. The document agent is built as a governed process: recognition, extraction, rule-based reconciliation and human control where the result is uncertain.

What it does

It receives a document from a channel (mail, portal, scanner), recognizes text and tables, extracts type-specific fields, classifies and reconciles against masters and accounting systems. Contested cases go to a manual queue, the rest is posted automatically. Every layer is testable and replaceable — unlike the “drop in, get out” black box.

Where the line is

Any automated pipeline produces errors; the question is whether you see them before they hit accounting. So human control isn’t an option but part of the contract: confidence thresholds, a manual review queue, per-field tracing. More on the engineering on the AI document processing page; extraction from unstructured text relies on vector search where meaning matters more than a template.

How the chain works

  1. 01
    Recognition (OCR) · OCR engine

    Turns a scan or photo into text with coordinates and tables. On clean forms this alone is enough.

  2. 02
    Field extraction · mid model

    Pulls dates, amounts, IDs and line items by description rather than a rigid template — works on heterogeneous documents.

  3. 03
    Rule-based reconciliation · deterministic code

    Checks sums, the counterparty against the registry, number validity. Catches what can't be trusted automatically.

Integrations

OpenAI YandexGPT Google Sheets DaData

+ any external API

Cost calculator

200
3
Tokens, ₽/mo
—
Development, ₽
—
Support, ₽/mo
—

Estimate at a blended per-token rate (input+output). Exact cost depends on context length, number of calls and the share of manual review — we scope it to your process.

related cases

AI Systems

Enterprise Knowledge & AI Context Platform

Infrastructure

Multi-Provider Data Integration Platform

faq

Straight answers

Which documents can the agent process?
Invoices, acts, contracts, forms, waybills, IDs, corporate documents. Harder cases are handwritten and poorly scanned ones; here it's not the model that matters most but the reconciliation process and the human-handoff point.
OCR or LLM — which is used?
A stack. OCR turns the image into text, the model extracts fields by meaning, deterministic reconciliation catches errors. On clean uniform forms OCR with rules is enough without a model — cheaper and more predictable.
How do you keep errors out of the accounting system?
Through explicit confidence thresholds: anything below goes to a manual queue, anything above flows into the system through the same contract people use. Each document shows which field was extracted how and from which line — honest tracing.
Where does it pay off?
Accounting (invoices, acts, statements), HR, insurance and medical forms, requests with attachments. The effect is measured by processing speed and the share of documents that go through with no human touch — before and after.

Next step

Let's design an AI-native automation layer for your operations.

DBCV