Abstract

Generalist RAG systems fail in high-stakes domains like finance. They can't handle the intricate cross-references and conditional logic of regulatory text, leading to hallucinations and making them untrustworthy. In this talk, I will share our practical journey building a domain-specific AI for regulatory compliance. We'll cover:
1. Architecture Evolution: Moving from standard vector RAG to a Graph RAG architecture that uses a regulatory ontology to understand relationships, not just retrieve text.

1. SLM Advantage: How combining Graph RAG with specialized Small Language Models (SLMs) yields higher accuracy, 80x lower cost, and drastically improved output stability compared to generalist LLMs .
2. Ensuring Reliability: A framework for synthetic data generation, benchmarking, and continuous evaluation to make inherently probabilistic AI systems auditable and reliable for production. We will include a practical architecture walkthrough and a live demo of our approach.

Bio

Karthigeyan R J is the Founder of Comply2Reg, building domain-specific AI for banks and financial institutes in the regulatory and compliance space. He formerly worked as a GenAI Solution Expert at 4CRisk.ai (acquired by CUBE, UK - market leader in RegTech) and has hands-on experience fine-tuning models and building agentic workflows for regulated environments.