Private AI for Secure Banking Chatbot

The problem

Customer-facing conversational AI is well-trodden ground until the customer is asking about their own account. At that point every exchange contains financial data that regulation says cannot be sent to a third party, which rules out essentially every hosted model API.

How it works

The entire stack runs inside the bank. A Llama model is served with vLLM on on-premise Kubernetes, giving the throughput needed for concurrent customer conversations on hardware the institution controls end to end. No inference call crosses the network boundary.

vLLM was the load-bearing choice: private deployment usually means accepting worse latency and concurrency than a hosted API, and continuous batching is what closed enough of that gap to make the experience acceptable for interactive use.

What shaped it

Regulation set the boundary before anything else. Data residency and audit requirements meant the model had to run where the bank could evidence exactly what happened to every request — which also made the system fully auditable, since every interaction stays within logging the bank already controls.

Banking answers also have to be correct or absent. The system was scoped to route rather than speculate when a question moved beyond what it could answer from authoritative sources.

Outcome

Customers got conversational self-service while sensitive financial data never left the institution's infrastructure, satisfying strict regulatory and audit requirements.