Document Intelligence
From raw documents to structured data. OCR, layout analysis, entity extraction, and validation — end-to-end intelligent processing.
The document challenge
Enterprise data lives in documents — and most of it is locked in formats machines struggle to read.
of enterprise data is unstructured
bytes of data created daily worldwide
of worker time spent on document tasks
faster processing with intelligent automation
Format Diversity
PDFs, scanned images, photographed pages, handwritten forms, faxes, and emails — each requires different extraction strategies.
Layout Complexity
Multi-column layouts, nested tables, merged cells, headers/footers, watermarks, and overlapping elements break naive parsers.
Quality Variance
Skewed scans, low-resolution photos, blurred text, coffee stains, and faded ink demand robust pre-processing pipelines.
Scale Requirements
Millions of documents per month with sub-second latency targets, requiring distributed processing and intelligent batching.
Processing pipeline
Six stages transform a raw document into validated, structured data — each powered by specialised AI models.
Document Ingestion
Accept documents from any source — file uploads, email attachments, API submissions, S3 buckets, or scanner feeds. Normalize to a consistent internal format.
Tech Stack
Key Metric
Core technologies
Each stage in the pipeline relies on specialised models — from classical OCR engines to multimodal transformers.
Optical Character Recognition
Modern OCR engines combine CNN feature extraction with LSTM/Transformer sequence decoders. Pre-trained on millions of font/language combos, fine-tuned per domain.
Layout Models
Jointly model text, position, and visual features. LayoutLMv3 uses a unified text-image pre-training objective to understand 2D document structure without templates.
Table Extraction
Detect table boundaries, identify rows/columns/headers, handle merged cells and spanning, then reconstruct into structured tabular data.
Named Entity Recognition
Token-level classifiers fine-tuned on domain-specific corpora extract invoice numbers, dates, amounts, vendor names, and custom entity types.
Classification Models
Zero-shot classifiers handle unseen document types; fine-tuned models achieve production accuracy. Ensemble voting boosts reliability across edge cases.
LLM Post-Processing
Large language models handle ambiguous extraction, cross-field reasoning, and format normalization that rule-based systems miss — especially on novel document layouts.
Document types
Pre-trained extraction models for the most common enterprise document types — each continuously refined with production feedback.
Invoices
Contracts
Forms
Receipts
IDs / Passports
Medical Records
Financial Statements
Shipping Documents
Accuracy & confidence routing
Every extracted field carries a confidence score. Configurable thresholds route documents through the right validation path.
Confidence Distribution
High Confidence
Fields extracted with high certainty are committed directly to the output — no human review needed.
Medium Confidence
Flagged for rapid human validation — the system highlights uncertain fields and suggests corrections.
Low Confidence
Routed to a specialist for full manual extraction — typically degraded scans, handwritten notes, or novel layouts.
End-to-end accuracy with HITL
Per-page processing latency
Documents auto-processed (no review)
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