LLM-Powered Relationship Intelligence Built on AWS Enabled a Commercial Bank to Deliver Actionable Client Insights

Key Highlights: What This Case Study Covers

• Best practices in unified client intelligence and semantic layer architecture for commercial banking.
• Implementation of domain-tuned LLMs and agentic workflows to interpret complex Relationship Manager (RM) queries across structured and unstructured data.
• How to build a scalable AI-for-BI platform that consolidates data across 15+ internal systems while maintaining strict governance, security, and role-based controls.
• Real-world application of AWS Bedrock, Snowflake, OpenAI, and Vector Search technologies for context-aware alerts, document intelligence, and cross-sell opportunity identification.

Client Overview

Our client is a major commercial bank where Relationship Managers (RMs) are responsible for driving client growth, retention, and managing ongoing commercial banking relationships. They are known for their large-scale banking operations, and they serve commercial clients needing timely, tailored, and contextual insights across products and engagements.

The Ask

The client wanted to unify disparate, siloed data from across 15+ internal systems into an AI-powered Business Intelligence (AI for BI) solution that delivers intelligent, actionable insights to enhance RM productivity and client engagement quality.

Challenges

• Customer intelligence and historical data resided across more than 15+ internal systems.
• RMs sought to streamline the gathering of client context as the current process was largely manual, aiming to optimize meeting preparation and ensure consistently personalized client engagement.

Our Solution: LLM-Powered Relationship Intelligence Solution

Unified Client Intelligence via Semantic Layer

Integrated data from 15+ fragmented systems using Snowflake Data Platform and standardized KPIs via a YAML-based semantic layer and SQL Semantic Views to deliver a 360° client view on personalized dashboards.

Implemented Domain-Tuned LLM & Agentic Workflows

Deployed an AI for BI LLM engine with specialized agents (KPI Selector Agent, Persona Context Agents, Action Lever Agents, Analyzer Agent) to interpret natural language queries, generate SQL dynamically, and visualize metrics.

Integrated Conversational & Document Intelligence

Combined conversational intelligence and document-level querying to generate contextual alerts and action recommendations.

Established Governance, Observability & Security

Enforced strict access controls and compliance through OKTA authentication, AWS WAF, AWS IAM, Secrets Manager, AI Agent Guardrails, and OpenTelemetry with CloudWatch dashboards.

Impact Delivered

• Integrated data across 15+ siloed internal systems into role-based personalized dashboards for a 360° view.
• Reduced manual research effort by enabling RMs to query structured data and uploaded documents via natural language conversation.
• Automatically surfaced critical growth areas, churn risks, and cross-sell opportunities for commercial client portfolios.
• Generated timely, context-aware alerts and action recommendations from meeting notes and operational interactions, improving client engagement.