The Document Processing Problem
Every organization has documents. Invoices, contracts, shipping manifests, customs forms, compliance reports, insurance claims, financial statements. Many of these documents contain critical business data that needs to flow into systems, databases, and workflows.
The problem: most of this data enters your systems through manual data entry. Humans reading documents, typing values into forms, copying data between systems. It’s slow, expensive, and error-prone — and it doesn’t scale.
Traditional OCR helps with clean, standardized documents. But real-world documents are messy: varying formats, mixed languages, degraded scans, handwritten annotations, tables within tables. Template-based extraction breaks every time a supplier changes their invoice layout.
How Our Pipeline Works
Stage 1: Ingestion & Pre-Processing
Documents arrive via API, email, file upload, or watched folders. The pipeline normalizes formats (PDF, image, email attachment), applies image enhancement for degraded scans, and prepares documents for processing. Volume spikes are handled automatically through auto-scaling infrastructure.
Stage 2: Classification
Before extraction begins, documents are automatically classified by type. Our classification models use both visual features (layout, logos, formatting) and semantic features (text content, header patterns) to determine document type with high confidence. This routes each document to the correct extraction pipeline.
Stage 3: OCR & Text Extraction
We select and combine OCR engines based on your specific document types and languages. For clean digital PDFs, native text extraction is fastest. For scanned documents, we use production-grade OCR with language-specific models. For handwritten text or degraded scans, we apply specialized preprocessing and enhanced OCR pipelines.
Stage 4: Semantic Extraction
This is where LLMs transform the process. Rather than relying on fixed field positions or regex patterns, we use large language models to understand the document’s content semantically. The LLM knows that “Total Due” and “Amount Payable” and “Gesamtbetrag” mean the same thing. It handles format variations, missing fields, and unusual layouts without retraining.
Extraction prompts are tuned per document type with structured output schemas, ensuring consistent data formats regardless of input variation.
Stage 5: Validation & Confidence Scoring
Every extracted value receives a confidence score. Cross-field validation checks logical consistency (do line items sum to the total? does the date format make sense?). Business rules flag anomalies. Documents passing all checks flow automatically to downstream systems. Low-confidence extractions are routed to human review.
Stage 6: Human-in-the-Loop Review
The review interface presents flagged documents with extracted data pre-filled and low-confidence fields highlighted. Reviewers correct errors quickly — they’re editing, not re-entering. Every correction is captured and used to improve extraction accuracy over time.
Multi-Language Processing
International businesses deal with documents in many languages — often within the same workflow. A logistics company might process invoices in English, customs forms in Greek, shipping manifests in Chinese, and compliance documents in German.
Our pipeline handles this natively. Language detection runs automatically, routing documents to the appropriate OCR model. Multilingual embeddings enable semantic extraction across languages. The LLM understands document structure regardless of the language the content is written in.
We have production experience with Latin, Cyrillic, CJK, Arabic, and Greek scripts — including mixed-language documents where headers are in one language and content in another.
Integration With Existing Systems
Document intelligence is only valuable if extracted data reaches the systems that need it. We build integration layers that connect directly to:
- ERP systems — SAP, Oracle, Microsoft Dynamics
- Document management — SharePoint, Google Drive, custom portals
- Databases — PostgreSQL, MySQL, MongoDB
- Workflow engines — Airflow, custom REST APIs
- Compliance platforms — audit logging, reporting dashboards
Data flows into your existing workflows automatically. No manual re-keying, no copy-paste, no CSV uploads.
Accuracy & Continuous Improvement
Our production systems typically achieve 93-98% extraction accuracy depending on document quality and complexity. But accuracy isn’t static — it improves continuously through the human feedback loop.
Every correction a reviewer makes is logged and analyzed. Common error patterns trigger prompt refinements. New document format variations are captured and added to evaluation test sets. The system gets better every week it runs.
We also monitor for drift: if accuracy drops on a particular document type (perhaps a supplier changed their invoice format), alerts fire and the extraction pipeline is updated before the problem compounds.