Operationalizing Document AI: A Microservice Architecture for OCR and LLM Pipelines in Production

TL;DR

Academic research on document understanding models rarely addresses how to run them reliably at scale in production environments. The authors built a microservice architecture that chains together multiple specialized models—classification, OCR (optical character recognition, converting images to text), and LLMs (large language models that extract structured data from text)—into a single deployable pipeline tested on thousands of documents.

Key Takeaways

• Implement a microservice architecture for scalable document processing.
• Separate GPU-bound tasks from CPU-bound orchestration for efficiency.
• Utilize asynchronous processing to manage I/O operations effectively.
• Focus on optimizing OCR to reduce overall latency in document pipelines.