Box taps Nvidia’s Nemotron for new agent-first content retrieval system

The system can answer queries four times faster, with 20% fewer errors and 90% fewer tokens used. At its developer conference on Thursday, Box announced a new feature that will build Nvidia’s Nemotron embedding system into the core of its product. Called Box Content Retrieval, the new feature allows agents to answer queries far more effectively, relying on the embedded metadata created by Nemotron. The upshot for users is faster, more accurate and less expensive queries. Nvidia’s VP of Generative AI software Kari Briski told TechCrunch that she expects queries to be answered four times faster, with 20% fewer errors and 90% fewer tokens used. The system will be available to all Enterprise Advance customers, rolling out early next year.

“This dramatically boosts both the relevance of our search technology for agents,” Box CEO Aaron Levie said. “Instead of an agent having to kind of read through each document, we can send back chunks from the documents that are most relevant to the user’s query or whatever the the task is that the agent is doing.” The feature is an implementation of Nvidia’s Nemotron Embed project, which offers open-weight embedding models to improve automated retrieval systems. Major labs have often overlooked embedding models, which use LLMs to pre-process data to work better with agents. While largely overlooked by frontier labs,