Operationalizing Document AI: A Microservice Architecture for OCR and LLM Pipelines in Production
A new study proposes a microservice architecture to bridge the gap between academic models and production-scale Document AI systems. Surprisingly, real-world profiling reveals that OCR, rather than LLM parsing, is the primary latency bottleneck in the pipeline.
Computer Science > Artificial Intelligence
Title:Operationalizing Document AI: A Microservice Architecture for OCR and LLM Pipelines in Production
View PDF HTML (experimental)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.
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Source: arXiv cs.AI Recent
