---
title: "Document Intelligence"
canonical_url: "https://cognilium.ai/services/document-intelligence"
slug: "services/document-intelligence"
section: "services"
page_type: "pillar"
date_modified: "2026-09-29"
description: "Documents read, classified, extracted and validated, with confidence you can audit. Every value carries the quote and page it came from."
publisher: "Cognilium AI"
author: "Mudassir Marwat"
person_same_as:
  - "https://www.linkedin.com/in/mudassir-marwat/"
  - "https://orcid.org/0009-0008-1927-2598"
  - "https://github.com/mudassirmarwat"
  - "https://medium.com/@mudassir-marwat"
  - "https://www.youtube.com/@mudassir-marwat"
  - "https://dev.to/mudassirmarwat"
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  - "https://huggingface.co/mudassirmarwat"
  - "https://bsky.app/profile/mudassir-marwat.bsky.social"
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  - "https://www.facebook.com/mudassir.marwat"
  - "https://www.reddit.com/user/mudassirmarwat/"
  - "https://www.f6s.com/member/mudassir-marwat"
  - "https://cognilium.ai/founder"
answers:
  - "Twelve steps, eight of them shown"
  - "What confidence actually means here"
  - "Contract intelligence, for legal teams"
  - "The same pipeline, different documents"
entities: []
related:
---

# Document Intelligence

On one engagement, scanned financial statements extracted at roughly 12% accuracy with text-only parsing; a multimodal change to the parse stage took the same documents to roughly 94%. Same model, different pipeline.


## In short

On one engagement, scanned financial statements extracted at roughly 12% accuracy with text-only parsing; a multimodal change to the parse stage took the same documents to roughly 94%. Same model, different pipeline.

**Upload and parse-quality detection** PDF, DOCX, spreadsheet or scan — and the first decision is what kind of parsing this file actually needs: native text, mixed, or full OCR.

**Classification across 25 types** In three tiers, because a contract and an invoice should not be read with the same expectations, and neither should two kinds of contract.

**Schema-driven extraction** A registry holds a schema per document type, and extraction is driven by it rather than by a prompt that hopes for the right shape.

**Field alias mapping** The same field is called four things across four issuers. That mapping is data, not a special case in code.

**Cross-field validation** Twelve rules checking that the values agree with each other — sums, date ranges, and lookups that should reconcile.

**Cross-document validation** Sixteen more rules and six edge types linking documents to each other, so an amendment finds its contract.

**Evidence-based confidence** High, medium or low, with the source quote attached — and up to three alternative readings per field where the document is genuinely ambiguous.

**Graph and document storage** Entities resolved and stored so the next document about the same party is recognised rather than recreated.

Contracts, financial documents, forms and scans turned into structured data a business can act on.

With confidence you can audit, because every value is tied to the exact place in the document it came from.

cross-field rules, plus 16 across documents

values stored without a traceable source


## Twelve steps, eight of them shown

Built for a family-office platform and generalised since. The validation stages are the ones that decide whether the output can go near a system of record.


## What confidence actually means here

Confidence comes from verifying the evidence exists, not from the model grading itself.

Every extracted value carries its source quote and page. A value without a traceable source is not an answer; it is a guess. And the buyer's real question — what happens when it is wrong — is answered by the design: it says so, per field, with alternatives queued for a person.


## Contract intelligence, for legal teams

We build systems that change how legal teams review, negotiate and manage contracts — cutting the reading, never the judgement, while making the standard applied consistent from the first contract to the fortieth.

**Read against your own playbook** A firm's legal playbook is parsed into 80 to 150 enriched terms, and every clause is checked against those positions rather than against general legal knowledge.

**Findings, not raw text** Risk flagged per clause with replacement language already drafted, so a lawyer reviews findings instead of reading pages to locate them.

**Cited to source, always** Every finding points at the clause that produced it. A conclusion a partner cannot trace is a conclusion they will not sign.

This is not a proposal: it is Paralegent AI, our own product, in production today — 23 agents reviewing a contract inside Word in 5 to 10 minutes.


## The same pipeline, different documents

Including one place where determinism beat the model: an Excel playbook is parsed with zero model calls, because the structure was already there.

**Contract review** A company's legal playbook parsed into 80 to 150 enriched terms, then applied clause by clause inside Word.

**The full eight-stage pipeline** Parse, classify, extract, validate, score, graph, link, output — each stage replaceable without rewriting the rest.

**Retrieval over what you extracted** Once documents are structured, the questions people actually ask become answerable.

Send us the documents nobody wants to key in

One honest screenshot beats every paragraph here: we run your own files and show you a single extracted field with its quote, its page and its confidence. Then you decide which fields are ready to automate and which still need a person.


---

Canonical HTML: https://cognilium.ai/services/document-intelligence