Service
From OCR to
Agentic Document Processing
Yesterday: scan documents, extract fields, match templates. Today: an AI agent that understands the entire document, captures context, fills in missing information, makes decisions, and triggers downstream processes. No templates. No rework. No human intervention.
The Reality
Traditional document processing was progress, but not a breakthrough
Template OCR was generation 1. Machine learning IDP was
generation 2. Both share the same problem: they extract data
from documents. But they don’t understand what they read and
they can’t act on it.
Extraction is not understanding
ML-based IDP recognizes “€14,500.00” as an amount. But does it understand that the amount deviates from the last invoice, that the early payment discount expires in 3 days, and that the supplier is on a watchlist? No. It extracts, nothing more.
Classification is not context
ML can classify a document as “invoice.” But can it recognize that this invoice belongs to a framework agreement expiring next week and that procurement should be notified? That requires context beyond the document itself.
Recognition without action is half-automation
Even the best IDP solution ultimately delivers only structured data. A human still has to decide: approve? Reject? Query? Escalate? The most expensive part of the process – the decision – remains manual.
The Evolution
Three Generations of Document Processing
From rigid templates to machine learning to AI agents that don’t just read documents, but understand, evaluate, and act on them.
Template OCR
Generation 1: Fixed rules, fixed positions. “Amount is in row 12, column 3.” One template per supplier. Breaks on new layouts. Recognition rate: 60–70%. Obsolete today.
ML-Based IDP
Generation 2: Machine learning recognises fields regardless of layout. Classifies document types. Confidence scores. Human-in-the-loop. Recognition rate: 85–92%. Better, but still just extraction.
Agentic Document Processing
Generation 3: An AI agent understands the entire document in context. Identifies missing information and retrieves it. Makes decisions. Triggers downstream processes. Learns continuously. End-to-end, not just extraction.
The Difference
Yesterday: Extraction. Today: Understanding + Action.
| ML-Based IDP Yesterday | Agentic Document Processing Today · with Lunatec | |
|---|---|---|
| Core Capability | Extract data from documents | Understand, evaluate, and act on documents |
| Context | The document itself only | Document + ERP + contracts + history + policies |
| Missing Info | Field empty → Human-in-the-loop Human must follow up | Agent identifies missing field Retrieves info from other systems |
| Decision | None — delivers data only | Approve, reject, escalate, send query Autonomously |
| Downstream Process | Manual or separate RPA bot | Agent triggers posting, notification, workflow directly |
| Learning | Re-training required Periodic, labor-intensive | Learns continuously From every interaction and correction |
| Outcome | 85–92% extraction + manual decision | 95%+ end-to-end processing Incl. decision and action |
In Practice
Same Invoice - Two Worlds
Incoming invoice from a new supplier. PDF via email. PO number missing from the document.
- PDF received, classified as invoice
- ML model extracts: amount, date, supplier
- PO number missing → field empty
- System stops → human-in-the-loop
- Operator manually searches SAP for matching PO
- Price deviates 3% → operator checks framework agreement
- Early payment discount expires in 2 days → nobody notices
- Manual approval after 45 minutes
- PDF received — agent captures full content
- Agent identifies: PO number missing
- Agent searches SAP for open POs from this supplier
- Agent matches amount + delivery date → finds PO
- Agent checks: price deviates 3% → checks framework agreement → 5% tolerance → OK
- Agent identifies: discount deadline in 2 days → prioritizes payment run
- Agent posts in SAP, initiates payment approval, archives
Capabilities
What a Document Processing Agent can do and IDP cannot
The critical difference: the agent doesn’t just extract data. It understands, decides, and acts.
Understand & Enrich
- Capture the full context of the document – not just individual fields
- Identify missing information and retrieve it from other systems
- Validate documents against contracts, purchase orders, and policies
- Detect contradictions and anomalies (price, quantity, terms)
- Understand relationships across multiple documents
- Meaningfully interpret unstructured content (free text, comments)
Decide & Act
- Approve, reject, or escalate: autonomously based on rules
- Formulate and send queries to the right contact person
- Trigger postings, workflows, and notifications directly
- Recognise deadlines and act proactively (discounts, expiry, SLAs)
- Classify exceptions and route to the appropriate specialist
- Learn continuously from every correction and interaction
Typical Results
What Our Clients Achieve with Agentic Document Processing
Case Study: Invoice Processing
Incoming invoices were transferred manually between isolated systems — with no direct integration between the document inbox and the ERP. Staff extracted document data by hand, performed manual matching, and routed each order individually through the approval workflow. This resulted in high time expenditure, error-prone data entry, and delays across the entire accounts order process.
UiPath Intelligent Document Processing (IXP) with Document Understanding and LLM integration for automatic extraction and classification of invoice documents — with direct ERP connectivity. Rule-based matching, automatic exception handling, and seamless routing through the approval workflow. No manual intervention between inbox and posting.
Technology
The Platform Behind Agentic Document Processing
Agentic Document Processing combines OCR engines, large language models, and RPA orchestration into an agent that doesn’t just read documents, but processes them.
Proud Diamond-Partner of UiPath
Ready
Your IDP extracts data.
But who makes the decision?
Send us 10 sample documents. We’ll show you in 30 minutes what an agent can do with them: not just extraction, but understanding, decision, and action.
No sales pitch. Just an honest assessment.
120+ Clients. 100% Satisfaction. 7 months to Profitability.
WHAT YOU GET IN THE DISCOVERY CALL
Identify your best use cases
Based on your industry and process landscape
Calculate concrete ROI
In Euros, FTE equivalents and time savings
Show examples from your industry
Real results of comparable companies
Define timeline and next steps
Concrete roadmap, no vague promises