Build an isolation-native AI research system for corporate analysis
Develop an AI analyst that can process and ground its research in massive private document repositories while strictly adhering to granular access permissions.
online B2B Deep Tech / Infrastructure
From AI Engineer — Can Your AI Analyst Keep a Secret? - Aaron Linsky, Bridgewater & Nikhil Benesch, turbopuffer at 00:25
Problem: Standard vector search fails when applied to documents with complex permissions, either providing poor recall by discarding non-matching documents during post-filtering or suffering from excessive latency by scanning unauthorized documents during pre-filtering.
For: Organizations (e.g., financial institutions) that need AI agents to process 100 million permissioned private documents (internal and external research).
Products from this video
Examples
- Bridgewater: Uses the system to power its AI analyst, allowing it to search 100 million documents while respecting complex internal and external document permissions (00:21, 03:43).
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