BankUnited uses GenAI to deliver faster, accurate answers, improving customer service and reducing internal inefficiencies.
BankUnited applied GenAI to fix a critical service bottleneck. Employees struggled to access policy information quickly. This caused long response times and inconsistent answers for customers. And thus, the bank introduced SAVI, an internal GenAI assistant, to solve this challenge at scale.
The main issue was fragmented knowledge. Policies were stored across hundreds of documents. Employees had to search manually, slowing service delivery. SAVI uses GenAI to unify this knowledge base. Built on Amazon Web Services Bedrock and Claude models, it enables natural language queries across 400 internal documents. Employees now receive answers in seconds instead of minutes.
The benefits are immediate and measurable. SAVI delivers responses in under 10 seconds with 95 percent accuracy. Back-office support calls dropped by 40 percent. Employees spend less time searching and more time engaging customers. This shift improves decision-making, customer satisfaction, and employee confidence. New hires also ramp faster with guided support.
However, deploying GenAI required strong governance. The bank addressed risks around data privacy and compliance. Guardrails were built for data access, validation, and human oversight. This ensured safe and reliable outputs. Over time, SAVI evolved from a support tool into a core service layer, enabling proactive customer engagement and scalable operations.
Why it matters
GenAI is transforming internal knowledge access into real-time decision support.
• Solves knowledge fragmentation across large enterprise document systems
• Enables consistent, fast, and accurate responses in customer-facing workflows
• Establishes governance frameworks for safe GenAI deployment in regulated industries
This case reflects a broader enterprise problem: unlocking institutional knowledge while maintaining compliance, speed, and consistency in customer interactions.
