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Production-grade, Privacy-preserving Document AI System

📑 10 slides 👁 44 views 📅 1/23/2026
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Title Slide

Production-grade, Privacy-preserving Document AI System

Title Slide
2

Problem Context & Objectives

  • Business problem: Handling sensitive documents across industries
  • Why scalability, privacy, and performance are critical
  • Multi-tenant support for Health, Finance, and Legal sectors
Problem Context & Objectives
3

High-Level Architecture Overview

  • End-to-end document processing pipeline
  • Multi-layered privacy and security measures
  • Scalable cloud-native infrastructure design
High-Level Architecture Overview
4

Multi-Tenant Design

  • Namespace isolation: Health/Finance/Legal
  • Role-based access control implementation
  • Resource quota management per tenant
Multi-Tenant Design
5

End-to-End Processing Pipeline

  • Document ingestion with validation checks
  • Preprocessing with OCR and format normalization
  • NLP inference with GPU acceleration
  • Post-processing for output generation
End-to-End Processing Pipeline
6

ML & GPU Inference Strategy

  • NER and classification models
  • GPU cluster with auto-scaling
  • Mixed precision training and inference
  • Dynamic batching for efficiency
ML & GPU Inference Strategy
7

Privacy & Security by Design

  • Automatic data anonymization pipeline
  • Differential privacy for sensitive data
  • Vault integration for secret management
  • End-to-end encryption implementation
Privacy & Security by Design
8

Observability & Monitoring

  • Prometheus for metrics collection
  • Grafana dashboards for visualization
  • Centralized logging with audit trails
  • Alerting system for anomalies
Observability & Monitoring
9

Scalability & Performance

  • Horizontal scaling for throughput
  • P95/P99 latency optimization
  • Cost-aware resource allocation
  • Cold start mitigation strategies
Scalability & Performance
10

Conclusion & Key Takeaways

  • Production-ready architecture with full lifecycle support
  • Proven engineering maturity in complex domains
  • Balanced trade-offs between privacy, cost, and performance
  • Ready for enterprise deployment at scale
Conclusion & Key Takeaways
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