How to Build an Agentic Data Pipeline for Receipt Processing | Unstract Document Series

In this video, we demonstrate how to build an end-to-end agentic pipeline for receipt data extraction using Unstract. We move past basic OCR to show how LLMs can interpret context, handle "noisy" data like crumpled or handwritten receipts, and output structured JSON ready for your database. What you’ll learn in this video: The Challenges: Why standard OCR struggles with modern business expense management. LLMWhisperer: Extracting raw text while preserving complex layouts and building LLM-ready documents. Agentic Prompt Studio: Automating the creation of output schemas and production-grade prompts in minutes. Validation & Accuracy: Setting up "Golden Datasets" to track accuracy scores and manage mismatches. Deployment: Turning your extraction project into a live ETL pipeline or an API. Start for FREE: https://unstract.com/start-for-free/ Schedule a Demo: https://unstract.com/schedule-a-demo/ TRY FOR FREE: Unstract Playground (No signup required): https://playground.unstract.com/ TRY FOR FREE: LLMWhisperer Playground (No signup required): https://pg.llmwhisperer.unstract.com/ Unstract Opensource: https://github.com/Zipstack/unstract #Unstract #documentprocessing #ai #llm #agenticworkflows #receipt