Operationalizing Document AI: A Microservice Architecture for OCR and LLM Pipelines in Production
The article discusses a microservice architecture designed for operationalizing Document AI, focusing on OCR and large language model pipelines. It highlights the gap between model development and production deployment, proposing solutions to enhance efficiency. Key findings include the dominance of OCR in latency and the influence of GPU capacity on system performance.
- ▪The proposed architecture encapsulates pipelines for classification, OCR, and structured field extraction.
- ▪The authors emphasize the importance of asynchronous processing and independent scaling strategies.
- ▪Surprising findings indicate that OCR significantly impacts end-to-end latency.
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Computer Science > Artificial Intelligence arXiv:2605.18818 (cs) [Submitted on 12 May 2026] Title:Operationalizing Document AI: A Microservice Architecture for OCR and LLM Pipelines in Production Authors:Yao Fehlis, Benjamin Bengfort, Zhangzhang Si, Vahid Eyorokon, Prema Roman, Patrick Deziel, Devon Slonaker, Steve Veldman, Ben Johnson, Joyce Rigelo, Michael Wharton, Steve Kramer View a PDF of the paper titled Operationalizing Document AI: A Microservice Architecture for OCR and LLM Pipelines in Production, by Yao Fehlis and 11 other authors 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.
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