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RAG and Voice Agent Applications

Session Track: AI Engineering

Session Time:

Session description

From Resumes to Conversations: Building Production RAG Systems for Voice-Driven Talent Matching

Discover how to build sophisticated RAG systems that go beyond simple document search to power intelligent voice agents. This session explores real-world challenges and solutions in creating production-grade RAG architectures for complex domains like talent recruitment.

What You’ll Learn:

Multi-stage RAG architecture: Moving from basic vector similarity to intelligent, domain-aware search with hard constraints, semantic matching, and business logic validation and graph databases

Voice-first data processing: Transforming conversational interviews into structured, searchable knowledge using advanced chunking strategies and multi-modal embeddings

Hybrid search patterns: Combining vector similarity, keyword matching, and structured data filtering for precision at scale

Production challenges: Handling 180k+ profiles with 8 types of embeddings, managing costs, and optimizing for real-time performance

Speaker

photo of Tony Xavier

Tony Xavier

Graduate Student, DePaul University

Tony Xavier is one of the founders at Talent Pluto and a graduate student at DePaul University studying CS with a concentration in AI/ML. His previous background includes physics and mechanical engineering from University of Michigan, and he has worked with companies such as Ford, Hyundai, and PG&E. He is currently a founder at TalentPluto, a Voice AI Career Agent that helps companies find the best GTM talent possible and helps candidates find their next job, using innovations in RAG and memory. Tony is a serial entrepreneur and has built a no-code editor and the smallest portable (foldable) electric skateboard, and he loves physics.