Private Document Intelligence Platform

Overview

A production-ready reference implementation demonstrating self-hosted document intelligence for regulated environments. Built on Kubernetes with GPU support for OCR. Designed to handle invoices, contracts, and forms with measurable accuracy and compliance-ready audit trails.

This is a reference architecture I built to demonstrate capabilities for organizations that can't use cloud-based APIs due to compliance requirements.

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:

What I Built

I designed and implemented the complete reference system:

• Architected the Kubernetes infrastructure with GPU scheduling (NVIDIA T4 configuration)
• Wrote the OCR and extraction services in Python (Tesseract + PaddleOCR integration, layout analysis)
• Implemented the document processing pipeline (ingestion queue, batch processing, result storage)
• Built the evaluation framework (gold-set validation, confidence scoring, accuracy tracking)
• Created Helm charts for deployment with configurable SLOs
• Developed Grafana dashboards for throughput, latency, cost-per-document, and accuracy metrics
• Set up CI/CD pipeline for testing and deployment automation

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

Demonstrated Capabilities

Reference implementation validated on invoice extraction benchmarks:

Metric	Manual Baseline	Platform Capability	Improvement
Processing Time	5-10 min/doc	3-8 sec/doc	98% faster
Accuracy	80-85% (manual)	92-96% (on test set)	+12% accuracy
Daily Throughput	100 docs	10,000+ docs	100x capacity
Cost per Document	£8-12 (labor)	£0.02-0.05 (infra)	99% cost reduction

Metrics based on architectural capacity calculations and evaluation on 100-sample gold dataset.

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.