OCR vs LLM vs Document Intelligence: A Buyer's Guide

Most enterprise document automation journeys start with OCR. A scanned PDF needs text, OCR provides text, and the team hopes the rest will follow. Then the limitation appears: text alone is not the same as understanding. A page can be searchable and still fail to answer whether a supplier has the right insurance, whether a KYC pack is complete, or whether a contract redline complies with a policy.

LLMs changed expectations because they can summarise, classify, and reason over language. But an LLM prompt by itself is still not a governed document process. Regulated teams need extraction that is source-linked, reviewable, repeatable, and auditable. That is where document intelligence becomes a distinct category.

OCR: making documents machine-readable

OCR turns images into text. It is useful for scans, photographs, faxes, and PDFs without embedded text. Good OCR improves search and provides a foundation for later processing. It is often enough for simple transcription, invoice header capture, or keyword search.

OCR struggles when the business question depends on context. It does not know that an indemnity clause affects supplier risk, that a missing passport page affects onboarding, or that a contract date conflicts with a system record. OCR gives you text; it does not give you governed facts.

LLMs: interpreting language

LLMs can summarise, answer questions, rewrite text, compare clauses, and identify likely values. They are powerful for exploration in a workspace such as TextMine Workbench, where users need to compare documents, generate outputs, and inspect evidence.

The risk is that an LLM can produce fluent answers without reliable provenance. For regulated teams, the buyer question is not simply whether the answer sounds right. It is whether the answer can be verified, routed, approved, and replayed during an audit.

Document intelligence: extraction with control

Document intelligence combines OCR, parsing, LLMs, schemas, evidence links, confidence scoring, human review, and downstream activation. It is the operational layer between messy documents and trusted systems.

TextMine Vault extracts facts from complex documents with source evidence. TextMine Records turns reviewed facts into durable records based on user-defined schemas. TextMine Workflows route exceptions, approvals, and handoffs. TextMine Playbooks apply reusable review logic to contracts, policies, and master templates.

Buyer checklist

• Evidence: Can every extracted value link back to the source page, paragraph, table, or clause?
• Confidence: Does the platform expose uncertainty and route low-confidence results?
• Schemas: Can business users define the record structure they need?
• Review: Can humans approve, reject, comment, and correct extracted data?
• Playbooks: Can policies and expert criteria be reused across similar reviews?
• Integration: Can approved outputs sync to systems through APIs, MCP, exports, or native connectors?
• Auditability: Can the organisation show who reviewed what, why, and when?

When OCR is enough

OCR may be enough when the problem is simple digitisation: make a scan searchable, extract a few stable fields, or index documents for retrieval. It is rarely enough when the output affects compliance, payments, onboarding, risk, or contractual obligations.

When an LLM is enough

An LLM may be enough for exploratory analysis where a user is actively checking the answer and no downstream system is updated. It becomes weaker when teams need repeatable governance, exception routing, and structured records.

When document intelligence is required

Document intelligence is required when the outcome must be trusted outside the prompt window. If the answer updates a system, supports an audit, triggers a workflow, informs a risk decision, or becomes part of a client record, it needs evidence, schema, review, and audit trail.

For a practical evaluation framework, read How to Evaluate Evidence-Backed AI Extraction and The Document Intelligence Maturity Model.