Building Semantic Enterprise Search for AI Agents

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Rakshith Venkatesh, Director of Product Management; Calvin Nieh, Technical Alliances Product Marketing Manager; and Derrick Harris, Technology Storyteller With the agentic AI era kicking into high gear, data management is more important than ever. AI agents need more than just data, no matter how well it’s annotated or curated. What AI agents need is data that’s organized, contextual, and easy to parse. They need to know how data from different sources (both structured and unstructured) relate to each other and the broader world through a shared ontology. In short: AI agents need semantic data. Inside large organizations, they need enterprise search that not only works, but also is supercharged with the ability to choose the best sources to serve and then to reason over them. Working with NVIDIA Agent Knowledge Foundation, including libraries like NVIDIA Auto-Ontology, we’ve developed a framework for implementing these capabilities without the heavy manual effort it previously would have required. By combining theVAST AI OS with NVIDIA’s growing collection of open models, accelerated libraries, and data-processing technologies, we’re able to automate an entire semantic workflow from data ingestion through 10x more accurate predictive results. All human experts need to do is ask their questions.

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