Case Study: Vectorize achieves real-time, production-ready RAG-powered LLM inference for fast, accurate Gen AI with Groq

Vectorize - Customer Case Study

Vectorize helps developers and enterprises build fast, accurate Gen AI applications by turning unstructured data into optimized vector search indexes for Retrieval Augmented Generation (RAG). Their challenge was closing the knowledge gap LLMs face when they lack domain-specific context and doing so quickly across data stored in disparate systems. Groq’s LPU™ Inference Engine (and Groq-powered LLMs) addresses the latency and performance side of that problem, enabling real-time inference needed for interactive RAG workflows.

Groq and Vectorize integrated Vectorize’s automated extraction, vectorization, and experimentation platform with Groq’s ultra-low-latency inference and RAG Sandbox, allowing teams to test Llama 3 or any Groq-powered LLM against multiple embedding and chunking strategies. The result: developer velocity compressed from days or weeks to minutes or hours, immediate end-to-end feedback for tuning retrieval quality, measurable improvements in relevancy/NDCG in experiments, and production-ready real-time use cases (e.g., call-center co-pilots, personalized content) powered by Groq.

Vectorize