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

TL;DR

This paper presents a microservice architecture for deploying document understanding models at scale, emphasizing practical deployment strategies and insights from real-world production use.

Contribution

It introduces a scalable, production-ready architecture for document AI pipelines, bridging the gap between research models and operational systems.

Findings

OCR dominates end-to-end latency in production

System saturation is limited by GPU inference capacity

Asynchronous processing improves pipeline throughput

Abstract

Academic research tends to focus on new models for document understanding creating a wide gap in the literature between model definition and running models at production scale. To close that gap, we present a microservice architecture that encapsulates pipelines of multiple models for classification, optical character recognition (OCR), and large language model structured field extraction as well as our experience running this pipeline on thousands of multi-page documents per hour. We describe our primary design decisions, including a hybrid classification, separation of GPU-bound inference from CPU-bound orchestration, use of asynchronous processing for the many IO-bound operations in the pipeline, and an independent, horizontal scaling strategy. Using batch profiling, we identified two surprising qualitative findings that shape production deployments: OCR, not language-model parsing, dominates end-to-end latency, and the system saturates at a concurrency determined by shared GPU-inference capacity rather than worker count. Our goal is to provide practitioners with concrete architectural patterns for building document understanding systems that work beyond the benchmark; effectively operationalizing models in production.