Enterprise AI deployments in banking and insurance often struggle with challenges that traditional RAG architectures do not adequately address, including PII leakage across retrieved documents, hallucinated responses in compliance-sensitive workflows, inconsistent schemas across legacy systems, and limited auditability for downstream decisions.
This session presents an intent-aware retrieval framework developed from real-world financial services deployments, where user queries are classified before retrieval and routed through specialized pipelines based on business intent, risk level, and compliance requirements. The architecture incorporates guardrails, confidence scoring with human escalation, and multi-agent orchestration patterns to improve reliability in regulated environments.
The framework introduces a six-stage pipeline: multi-format field extraction using LLMs for unstructured documents and deterministic parsers for structured formats; LLM-driven field confirmation through interactive user validation; hybrid semantic retrieval via OpenSearch VectorDB combining dense and sparse retrieval for accurate field-level similarity; context-enriched LLM mapping that compares source and target schemas using retrieved semantic chunks; and structured output generation for downstream migration and validation workflows. Human-in-the-loop checkpoints at critical stages ensure that only validated field mappings progress through the pipeline, directly addressing the reliability gap in automated schema translation systems.