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Document Processing at Scale

Definition

Processing millions of document verifications per day requires optimized infrastructure — GPU batching, async pipelines, caching, and load balancing.

Architecture for Scale

graph TD
    A[API Gateway<br/>Rate limiting, auth] --> B[Queue<br/>Kafka / SQS]
    B --> C[Worker Pool<br/>Auto-scaling]

    C --> D[GPU Workers<br/>OCR + Classification + Forensics]
    C --> E[CPU Workers<br/>MRZ parsing, barcode, validation]
    C --> F[API Workers<br/>Database verification calls]

    D & E & F --> G[Result Aggregation]
    G --> H[Decision Engine]
    H --> I[Response + Webhook]

    style D fill:#4051B5,color:#fff

Optimization Techniques

Technique Impact
GPU batching Process 8-32 documents simultaneously — 5-10x throughput
Model optimization TensorRT, ONNX Runtime — 2-4x faster inference
Pipeline parallelism Run OCR, forensics, classification concurrently
Caching Cache document templates, model artifacts
Auto-scaling Scale GPU workers based on queue depth
Regional deployment Process close to user for lower latency

Performance Targets

Scale Infrastructure Processing Time
1K/day Single GPU server < 3 seconds
100K/day 4-8 GPU servers < 3 seconds
1M/day 20-50 GPU servers, auto-scaling < 5 seconds
10M/day Distributed GPU cluster < 5 seconds

Key Takeaways

Summary

  • GPU batching is the single biggest optimization for document processing throughput
  • Pipeline parallelism (concurrent OCR + forensics + classification) reduces end-to-end time
  • TensorRT / ONNX Runtime provide 2-4x inference speedup over raw PyTorch
  • Auto-scaling based on queue depth handles traffic spikes (typical in eKYC: morning/evening peaks)