Production-ready legal AI within 24 hours. Here's the technical breakdown of how we did it for our ...

Here's the technical breakdown of how we did it for our own finance team.

When our finance team asked for help navigating internal contracts, we used Weaviate's 𝗤𝘂𝗲𝗿𝘆 𝗔𝗴𝗲𝗻𝘁 to build a production-ready legal assistant in just https://t.co/8J81wyeoQs" / X

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Production-ready legal AI within 24 hours. Here's the technical breakdown of how we did it for our own finance team. When our finance team asked for help navigating internal contracts, we used Weaviate's 𝗤𝘂𝗲𝗿𝘆 𝗔𝗴𝗲𝗻𝘁 to build a production-ready legal assistant in just a day and a half. Not a demo - a real application with advanced embeddings, agentic search, streaming responses, and cited sources. Legal research demands extreme precision. Without a reasoning layer, traditional systems lack the ability to apply proper filters before searching. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝘀𝗲𝗮𝗿𝗰𝗵 changes this entirely. The Query Agent treats your database as a set of tools rather than a static data store. It inspects your schema, constructs structured queries with the right filters, reranks results for actual relevance, and synthesizes grounded answers with citations. The architecture we used: 𝗗𝗮𝘁𝗮 𝗹𝗮𝘆𝗲𝗿: PDFs embedded with a multivector model that encodes each page as visual tokens, preserving layout and tables. Muvera compression then reduces memory and latency while maintaining retrieval quality. 𝗦𝗰𝗵𝗲𝗺𝗮 𝗱𝗲𝘀𝗶𝗴𝗻: Three collections instead of one - Commercial Agreements, Corporate & IP Agreements, and Operational Agreements. This gives the agent explicit structure to reason about and route questions effectively. 𝗤𝘂𝗲𝗿𝘆 𝗔𝗴𝗲𝗻𝘁: Operates in Search Mode for discovery and retrieval, or Ask Mode for synthesized answers. Every response includes cited source passages to reduce hallucinations. With 𝗪𝗲𝗮𝘃𝗶𝗮𝘁𝗲 𝗔𝗴𝗲𝗻𝘁 𝗦𝗸𝗶𝗹𝗹𝘀, you can now build this entire application with a single prompt in Claude Code or Cursor. Install Agent Skills: npx skills add weaviate/agent-skills Then run the prompt (detailed in our blog post) that instructs the coding agent to download the CUAD legal contract dataset, set up the three collections, configure multivector embeddings, and build the frontend interface with source citations. The agent handles everything: data processing, schema creation, embedding configuration, Query Agent setup, and frontend development. From my tests, it takes roughly 12 minutes and about 20k tokens on Sonnet 4.6. This same approach works for any document-heavy domain requiring precision: compliance documentation, medical records, technical specifications, and financial analysis. Read the full technical breakdown: weaviate.io/blog/legal-rag Dive deeper into Weaviate Agent Skills: weaviate.io/blog/weaviate-