Accelerating Customer Response with GenAI for a Leading Global Bank

The client’s Personal Banking and Wealth Management (PBWM) division needed to transform how agents handle customer queries during calls.

Their existing tools offered limited contextual suggestions and failed to scale personalized insights at pace. There was a need to increase agent efficiency, response accuracy, and overall customer experience using Generative AI. A scalable framework was required to evaluate and implement multiple GenAI models securely and compliantly.


Designed and implemented a GenAI studio to evaluate and compare multiple LLMs in real-time using active transcripts.

Developed a model-agnostic pipeline to ingest call transcripts, extract context, and surface intelligent prompts for agent assistance.

Created a prompt engineering framework to manage and tune context-specific prompts across various models.

Deployed a secure, compliance-friendly sandbox to run model experiments and monitor performance (response accuracy, latency, hallucinations).

Developed 20+ use cases including call summaries, transfer detection, knowledge extraction, and guided response generation.

Used an Agentic Framework with guardrails, feedback loops, and continuous improvement via user testing.

Trusted by Clients. Respected by Partners.

Clients cite strong leadership, responsiveness, and reliable delivery. Partners point to engineering depth, SME coverage, and a steady focus on outcomes over activity.

Tech Stack:

Azure OpenAI, AWS Bedrock, Google PaLM, LLM Orchestration: LangChain, Prompt Engineering APIs

Business Impact

Up to 3x faster agent response time through real-time GenAI prompts.

Improved customer satisfaction scores by delivering context-aware, accurate replies.

Secure GenAI experimentation environment, allowing safe exploration across multiple models.

Enabled scalability of agent assist tools across business unit sand markets.

Created a repeatable GenAI integration model, setting a foundation for enterprise-wide GenAI adoption.

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