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Document Intelligence

Configurable Document
Extraction Pipeline

An enterprise data intelligence platform needed a scalable, production-ready document extraction system capable of processing diverse document types across multiple business domains. The goal was to transform unstructured data into reliable, queryable intelligence.

Section 01

The
challenge.

Building a robust extraction system required solving several complex problems at once — none of them solvable in isolation.

  • —High variability in document formats (invoices, reports, PDFs, semi-structured data)
  • —Inconsistent structure and layout, making rule-based extraction ineffective
  • —Multi-tier entity recognition and resolution across datasets
  • —Need for human-in-the-loop validation to ensure enterprise-grade accuracy
  • —A system that could scale without constant re-engineering
Section 02

How we
built it.

A modular, AI-first architecture focused on flexibility, accuracy, and long-term scalability. Five components, each one existing because something would have broken without it.

— 01

Modular LLM-powered extraction pipeline

  • ·Built a configurable pipeline leveraging LLMs for contextual understanding
  • ·Enabled dynamic adaptation to new document formats
  • ·Reduced dependency on rigid templates and manual rule updates
— 02

Multi-tier entity resolution system (proprietary IP)

  • ·Developed a layered approach to entity matching and disambiguation
  • ·Handled duplicates, inconsistencies, and cross-document relationships
  • ·Improved data reliability for downstream analytics and decision-making
— 03

Natural language to Cypher interface

  • ·Designed an intuitive query layer for business users
  • ·Enabled exploration of structured data using natural language queries
  • ·Bridged the gap between technical systems and non-technical stakeholders
— 04

Human-in-the-loop validation framework

  • ·Integrated validation workflows for critical data points
  • ·Created feedback loops for continuous model improvement
  • ·Ensured transparency and trust in AI-driven outputs
— 05

Ground truth management system

  • ·Established a centralized system for validation datasets
  • ·Supported ongoing benchmarking and accuracy tracking
  • ·Enabled continuous refinement of extraction models
Solution highlights
  • —Rapid onboarding of new document types without system redesign
  • —Transparent quality assurance through human validation layers
  • —Configurable architecture adaptable across industries and use cases
  • —Seamless integration with a knowledge graph for structured insights
Impact & results
  • —Achieved high extraction accuracy across diverse document sets during testing
  • —Reduced manual data processing efforts significantly
  • —Enabled streamlined knowledge discovery via a Neo4j-powered knowledge graph
  • —Delivered a scalable, production-ready system aligned with long-term enterprise needs

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