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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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.18818 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | D4FLqzGTwjDE |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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