Case Study: Contextual AI achieves 95% RAG accuracy with Elastic

Contextual AI achieves 95% RAG accuracy with Elastic

Contextual AI, an enterprise AI company, faced the challenge of pushing beyond the limitations of traditional Retrieval Augmented Generation (RAG) systems, which were prone to inaccuracies and hallucinations. They needed a robust solution to power their advanced RAG and context engineering platform, requiring superior retrieval capabilities, massive scalability, and multi-cloud flexibility. They turned to vendor Elastic, utilizing its search technology and vector database within the Elasticsearch platform.

By implementing Elastic's unified platform, Contextual AI gained a powerful foundation for its RAG agents. Elastic provided highly effective semantic and hybrid search capabilities through its vector database and multi search API. This solution enabled Contextual AI to achieve a RAG accuracy of over 95%, scale to handle repositories with 22 million document chunks, and seamlessly deploy across multiple cloud environments to meet diverse customer requirements.

Contextual AI