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How to Connect AI to Billions of Legal Documents

Conference Context

Session Description

Legora’s foundational engineering challenge is connecting frontier LLMs to billions of legal documents so the models can efficiently solve end-to-end legal workflows without burning extra tokens. We’ll share the retrieval architecture we built with turbopuffer that achieves: 1. Strict data isolation across millions of legal cases in a very security-conscious domain 2. Predictable search performance (<100ms p90 latency) on large contexts 3. High retrieval quality (95%+ recall@10) with fewer agent loops We’ll retrospect on two architectures that failed to achieve all 3 (and why), and the key design factors that make the current solution work at our scale. Practical takeaways include: - How to evaluate per-tenant vs shared-index retrieval under strict data isolation - How to efficiently index and retrieve context to maximize relevance per input token - How to build a highly intelligent AI application when your inference budget is constrained

Media Evidence

Agents need more than a chat - Jacob Lauritzen, CTO Legora (speaker-match related prior/adjacent AI Engineer video; captions: English auto-captions).

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Synthesized Breakdown

How to Connect AI to Billions of Legal Documents ## Conference Context - Date/time: 2026-06-29 · 2:25pm-2:45pm - Track/room: Search & Retrieval · Track 3 - Speaker(s): Simon Eskildsen, Jacob Lauritzen - Session type/status: session · confirmed - Track: Search & Retrieval - Room: Track 3 - Session type: session - Status: confirmed ## Session Description Legora’s foundational engineering challenge is connecting frontier LLMs to billions of legal documents so the models can efficiently solve end-to-end legal workflows without burning extra tokens. We’ll share the retrieval architecture we built with turbopuffer that achieves: 1. Strict data isolation across millions of legal cases in a very security-conscious domain 2. Predictable search performance (<100ms p90 latency) on large contexts 3.

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