Private Document Intelligence Platform

Overview

A production-ready document intelligence platform for organizations that can't use cloud-based APIs. Built on Kubernetes with GPU support for OCR. Handles invoices, contracts, and forms with measurable accuracy and compliance-ready audit trails.

GitHub Repository: github.com/yrgenkuci/private-doc-intelligence-platform

The Challenge

Regulated organizations face a tough problem: they need to digitize and extract data from thousands of documents, but compliance stops them from using services like AWS Textract, Google Document AI, or Azure Form Recognizer.

The constraints are strict:

• Documents contain PII, financial data, or trade secrets
• GDPR/HIPAA requires data stay in specific jurisdictions
• Audit trails must prove data never left the controlled environment
• Processing must be fast enough to replace manual entry
• Costs must be predictable (not per-API-call pricing)

Solution Architecture

Built a self-hosted document intelligence pipeline with three core components:

1. Document Ingestion & Queue

• S3-compatible object storage (MinIO) for document staging
• Queue system for asynchronous processing
• Support for PDF, JPEG, PNG, TIFF formats

2. OCR & Extraction Engine

• Tesseract + PaddleOCR for multilingual text recognition
• GPU-accelerated processing (NVIDIA T4 or better)
• Layout analysis for structured extraction
• Custom models for specific document types

3. Evaluation & Monitoring

• Gold-set based accuracy measurement
• Per-document confidence scores
• Grafana dashboards for throughput, latency, cost
• Drift detection for model performance

Technical Stack

• Orchestration: Kubernetes (1.28+)
• OCR Engines: Tesseract 5.x, PaddleOCR
• GPUs: NVIDIA with CUDA 12.x
• Storage: MinIO (S3-compatible)
• Observability: Prometheus + Grafana
• Deployment: Helm charts with configurable SLOs

Implementation Highlights

GPU Scheduling & Cost Control

Kubernetes GPU scheduling with resource limits kept costs predictable:

resources:
  limits:
    nvidia.com/gpu: 1
  requests:
    memory: "4Gi"
    cpu: "2000m"

Cost per document stayed under £0.05 by batching documents and using efficient scheduling.

Evaluation Pipeline

Every processed document gets:

• Confidence score from OCR engine
• Field-level extraction validation
• Comparison against gold-set (when available)
• Logged to metrics system for SLO tracking

Compliance & Audit

• All processing happens in-cluster
• No external API calls
• Complete audit logs of document flow
• Encryption at rest and in transit

Results

Deployed for financial services client processing 5,000+ invoices/day:

Metric	Before	After	Improvement
Processing Time	5-10 min/doc	3-8 sec/doc	98% faster
Accuracy	80-85% (manual)	92-96% (automated)	+12% accuracy
Daily Throughput	100 docs	10,000+ docs	100x increase
Cost per Document	£8-12	£0.02-0.05	99% cost reduction

Key Learnings

1. GPU utilization matters: Batching multiple documents per GPU invocation reduced costs by 60%
2. Evaluation is non-negotiable: Without a gold-set, accuracy claims are meaningless
3. Layout analysis is critical: OCR alone isn't enough; understanding document structure dramatically improves extraction
4. Compliance is a feature: Audit logs and data isolation aren't overhead—they're what makes this valuable

How This Can Work for You

This reference implementation forms the basis of the "Private Doc-Intelligence Pilot" service:

• 2-week engagement deploying in your environment
• Your document types (we build/tune extraction for your formats)
• Your SLOs (we agree on accuracy, latency, cost targets upfront)
• Delivered as Helm charts so you own and operate the system

The code, models, and deployment scripts are modular and reusable. Not building from scratch each time, which is how the fixed-scope pricing works.

Interested? Book an architecture review to discuss your specific document types and requirements.