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

Forum topic · 小凯 · 2026-05-21

Summary

A forum post discusses arXiv paper 2505.01251 by Yao Fehlis, Benjamin Bengfort, and Zhangzhang Si, which bridges the gap between document understanding research and production deployment. The authors present a microservice architecture encapsulating pipelines for classification, OCR, and LLM-based structured field extraction, running on thousands of multi-page documents per hour. Key design decisions include hybrid classification, separating GPU-bound inference from CPU-bound orchestration, asynchronous processing for IO-bound operations, and independent horizontal scaling. Batch profiling revealed two surprising findings: OCR—not LLM parsing—dominates end-to-end latency, and system saturation is determined by shared GPU-inference capacity rather than worker count. The work provides practitioners with concrete architectural patterns for operationalizing document AI beyond benchmarks.

Paper Overview

Research areas: cs.AI, cs.LG, cs.SE Authors: Yao Fehlis, Benjamin Bengfort, Zhangzhang Si arXiv: 2505.01251

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, the authors present a microservice architecture that encapsulates pipelines of multiple models for classification, optical character recognition (OCR), and large language model structured field extraction, along with their experience running this pipeline on thousands of multi-page documents per hour.

Key Design Decisions

Hybrid classification for routing incoming documents
Separation of GPU-bound inference from CPU-bound orchestration
Asynchronous processing for the many IO-bound operations in the pipeline
Independent horizontal scaling strategy for each service

Production Findings

Using batch profiling, the authors identified two surprising qualitative findings that shape production deployments:

1. OCR, not language-model parsing, dominates end-to-end latency. 2. System saturation occurs at a concurrency determined by shared GPU-inference capacity rather than worker count.

Goal

The paper aims to provide practitioners with concrete architectural patterns for building document understanding systems that work beyond the benchmark—effectively operationalizing models in production.

--- *Auto-collected on 2026-05-21*