Building a Scalable AI Document Analysis Pipeline with Temporal

AI document analysis lets you automate data extraction and processing for more effective results. But you often have to deal with concurrency bottlenecks, expensive failures, and inconsistencies from LLMs.

In this webinar, we walk through how to orchestrate a reliable, scalable document analysis pipeline with Temporal, from Optical Character Recognition (OCR) and embedding generation to vector search and AI-powered recommendations. Learn how Temporal orchestrates every step as a durable workflow, making the system scalable, fault-tolerant, and provider-flexible.

Key takeaways
- End-to-end AI document analysis demo: Upload a grocery flyer PDF and see how to take it from OCR to AI-powered recommendations for grocery store selection based on sale items.
- Temporal orchestrates the entire workflow: See how Temporal creates a durable, multi-step workflow to make the pipeline reliable and production-ready.
- Temporal for RAG use cases: See how Temporal is a strong foundation for Retrieval-Augmented Generation to orchestrate document ingestion, embedding pipelines, and query-time AI interactions reliably at scale.

Git repo: https://github.com/anthonywong555/temporal-grocery-app

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