Unveiled at the company's developer conference, Box Content Retrieval relies on open-weight embedding technology to pre-process enterprise files and generate structural metadata. Rather than requiring software agents to scan complete documents, the framework returns only the most relevant text segments for a given query. The system maps underlying concepts across different terminologies, which is especially targeted at streamlining workflows like legal precedent and clause research. Enterprise Advance tier subscribers will gain full access to the new retrieval capability starting early next year.
Key points
The integration maps conceptual relationships to allow automated software agents to disco…: The integration maps conceptual relationships to allow automated software agents to discover relevant data across differing terminology.
Box bundles the freely available model into a single unified API layered over its preexis…: Box bundles the freely available model into a single unified API layered over its preexisting permission and metadata controls.
The platform is positioning the technology to accelerate complex research workflows, expl…: The platform is positioning the technology to accelerate complex research workflows, explicitly targeting the discovery of legal clauses and precedents.
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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…
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