Evaluating AI for Enterprise Information Retrieval

The evaluation process for AI in enterprise information retrieval relies on internal teams, particularly an evaluation team that develops tools alongside engineers specializing in quality control. Third-party involvement is restricted due to the sensitivity of enterprise data, and as the organization scales, future considerations may arise. The focus on RAG (Retrieve and Generate) technology highlights its importance in extracting relevant information tailored to user authorization levels, effectively filtering data to ensure compliance with permissions. This approach avoids the pitfalls of generic LLMs, which lack understanding of specific enterprise data contexts. All data is indexed with a personalized search that considers the user's hierarchy and relationships within the organization. Personalization mechanisms ensure that queries yield context-specific results, such as relevant onboarding documents. The retrieval engine employs a sophisticated hybrid model that integrates both keyword searches and semantic vector-based embeddings to enhance search accuracy and relevance.