Operationalizing Document AI: A Microservice Architecture for OCR and LLM Pipelines in Production
This paper presents a microservice architecture that encapsulates pipelines for classification, OCR, and LLM-based structured field extraction, sharing production experience handling thousands of multi-page documents per hour. Key design decisions include hybrid classification, separation of GPU-bound inference from CPU-bound orchestration, asynchronous processing, and independent horizontal scaling. Batch profiling reveals that OCR dominates end-to-end latency, and system saturation is determined by shared GPU-inference capacity rather than worker count.
[2605.18818] Operationalizing Document AI: A Microservice Architecture for OCR and LLM Pipelines in Production
[Submitted on 12 May 2026]
Title:Operationalizing Document AI: A Microservice Architecture for OCR and LLM Pipelines in Production
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. 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.
Subjects:
Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Software Engineering (cs.SE)
Cite as: arXiv:2605.18818 [cs.AI]
(or arXiv:2605.18818v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.18818
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yao Fehlis [view email] [v1] Tue, 12 May 2026 13:07:34 UTC (20 KB)
Full-text links:
Access Paper:
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)
TeX Source
view license
Current browse context:
cs.AI
new | recent | 2026-05
Change to browse by:
cs cs.LG cs.SE
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)