Synopsis: Search engines are having a moment in the AI era, and the shift goes well beyond basic keyword matching. Mike Vizard and Bianca Lewis of the OpenSearch Foundation get into how hybrid search, combining traditional text retrieval with vector-based semantic understanding, is becoming essential for keeping generative AI grounded in reality. Without a search layer that can surface the right context accurately, AI models are far more likely to hallucinate and return answers that sound confident but have no basis in the actual data.

Lewis explains why vector databases alone are not enough. Pure vector search captures meaning but loses precision on exact matches, while keyword search handles specifics but misses intent. Hybrid approaches merge both, giving AI systems a more reliable foundation to work from. As organizations push deeper into retrieval-augmented generation and agentic AI workflows, the quality of what gets retrieved determines whether those systems produce useful output or expensive noise.

Data sovereignty adds another layer of complexity. Compliance requirements are forcing organizations to rethink where their data lives and how it gets processed. Lewis makes the case that bringing compute closer to the data, rather than shipping data to centralized cloud services, is becoming the practical path forward for organizations operating under strict regulatory constraints.

The OpenSearch Foundation's positioning as a community-driven, open source alternative gives organizations more control over their search infrastructure at a time when that control is increasingly difficult to maintain with proprietary platforms.