Case Study: Docugami achieves AI-powered document accuracy and real-time scale with Redis

Redis Enterprise Powers Docugami’s Llm Transforming Documents Into Actionable Data

Docugami, an AI-powered document intelligence company, needed a distributed services architecture that could process large volumes of long-form business documents with high accuracy, low latency, and affordable cost. After running into performance issues with its Apache Spark-based pipeline, Docugami turned to Redis Enterprise and Redis vector capabilities to speed up processing, support semantic search, and power retrieval-augmented generation (RAG) for real-time document interaction.

Redis helped Docugami replace parts of its Spark workflow, store and search embeddings at scale, and support document indexing, chat-based retrieval, and up-to-date context for its models. The result was sub-second response times, real-time user interactions, significant cost savings, and improved scalability, reliability, and accuracy across Docugami’s document processing and ML pipelines.

Docugami

Mike Palmer

Co-Founder and Head of Technologies