𝗟𝗲𝗴𝗮𝗹 𝗔𝗜 demands zero tolerance for 𝗵𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻𝘀. When an attorney asks an AI assistant about case precedents, "creative" answers aren't innovative—they're malpractice waiting to happen. How do you transform a 𝗚𝗲𝗺𝗶𝗻𝗶 𝗺𝗼𝗱𝗲𝗹 from an eloquent improviser into a rigorous legal expert? How do you build an AI system that doesn't just cite sources, but proves every claim with verifiable documentation?

This session reveals the architecture of a "𝗖𝗵𝗮𝗶𝗻 𝗼𝗳 𝗧𝗿𝘂𝘀𝘁"—a production-tested pipeline for building AI agents that earn credibility through verification. Drawing from a real-world legal AI project, we'll trace the complete journey of a fact-checked response, from document ingestion to the final Flutter interface.

You will learn how to:

  • 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗮 𝗴𝗿𝗼𝘂𝗻𝗱𝗲𝗱 𝗮𝗴𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝗚𝗼𝗼𝗴𝗹𝗲 𝗔𝗗𝗞, constraining a Gemini model to reason exclusively over your private legal corpus using Vertex AI Search, eliminating hallucinations at the source
  • 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁 𝗮 𝗵𝘆𝗯𝗿𝗶𝗱 𝗔𝗜 𝗯𝗮𝗰𝗸𝗲𝗻𝗱 that orchestrates lightweight Cloud Functions for rapid document classification alongside a powerful Cloud Run agent for complex multi-step legal analysis
  • 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗣𝘆𝘁𝗵𝗼𝗻 𝘃𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 that acts as an automated fact-checker, mapping AI outputs to canonical sources in Firestore and providing an audit trail for every claim
  • 𝗗𝗲𝘀𝗶𝗴𝗻 𝗮 𝘁𝗿𝘂𝘀𝘁-𝗳𝗶𝗿𝘀𝘁 𝗙𝗹𝘂𝘁𝘁𝗲𝗿 𝗨𝗜 that uses reactive services to asynchronously enrich responses with source verification, ensuring users see proof alongside every answer
  • 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗲 𝗯𝘂𝗹𝗹𝗲𝘁𝗽𝗿𝗼𝗼𝗳 𝗱𝗮𝘁𝗮 𝗳𝗹𝗼𝘄𝘀 across Firestore and Cloud Storage that maintain data integrity throughout the entire pipeline

This isn't academic theory—it's a battle-tested playbook from the legal trenches. Walk away with the architectural blueprint and practical knowledge to build AI applications that don't just answer questions, but earn institutional trust through verifiable proof.

𝗟𝗲𝗴𝗮𝗹 𝗔𝗜 can't afford to 𝗵𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗲. When legal professionals depend on AI for document research, "creative" answers become liability risks. How do you build AI agents that prove every claim with verifiable sources?

This session presents a production-tested "𝗖𝗵𝗮𝗶𝗻 𝗼𝗳 𝗧𝗿𝘂𝘀𝘁"—an architectural blueprint that transforms 𝗚𝗼𝗼𝗴𝗹𝗲'𝘀 𝗔𝗗𝗞 𝗮𝗻𝗱 𝗩𝗲𝗿𝘁𝗲𝘅 𝗔𝗜 𝗦𝗲𝗮𝗿𝗰𝗵 into a rigorous legal assistant. We'll explore how to ground Gemini models on private legal corpora, architect hybrid backends that balance speed with complexity, and implement Python validation layers that fact-check every AI response against canonical sources.

From document ingestion through 𝗙𝗹𝘂𝘁𝘁𝗲𝗿 𝗨𝗜, you'll see how a reactive architecture ensures users receive not just answers, but proof. We'll dive into real code, production patterns, and the hard-won lessons from building AI systems where accuracy isn't optional—it's legal.

Leave with a proven playbook for building AI applications that earn trust through transparency and verification.

𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀:

  • 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗳𝗼𝗿 𝗴𝗿𝗼𝘂𝗻𝗱𝗲𝗱 𝗔𝗜 agents using Google ADK
  • 𝗛𝘆𝗯𝗿𝗶𝗱 𝗯𝗮𝗰𝗸𝗲𝗻𝗱 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝗳𝗼𝗿 𝗔𝗜 𝘄𝗼𝗿𝗸𝗹𝗼𝗮𝗱𝘀 (Cloud Functions vs Cloud Run)
  • 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 that eliminate hallucinations through source verification
  • 𝗧𝗿𝘂𝘀𝘁-𝗳𝗶𝗿𝘀𝘁 𝗨𝗜 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 that display proof alongside AI responses
  • Real-world lessons from high-stakes 𝗔𝗜 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗶𝗻 𝗹𝗲𝗴𝗮𝗹 𝗱𝗼𝗺𝗮𝗶𝗻