Capital One builds agentic AI modeled after its own org chart to supercharge auto sales

This same structure applies to agents that Capital One wants to monitor. They created an agent that evaluates existing agents, which was trained on Capital One’s policies and regulations. This evaluator agent can kick back the process if it detects a problem. Naphade said to think of it as “a team of experts where each of them has a different expertise and comes together to solve a problem.”

Financial services organizations recognize the potential of agents to provide their human agents with information to resolve customer issues, manage customer service, and attract more people to their products. Other banks like BNY have deployed agents this year. 

Auto dealership agents

Capital One deployed agents to its auto business to assist the bank’s dealership clients in helping their customers find the right car and car loan. Consumers can look at the vehicle inventories of dealerships that are ready for test drives. Naphade said their dealership customers reported a 55% improvement in metrics such as engagement and serious sales leads.

“They’re able to generate much better serious leads through this more conversational, natural conversation,” he said. “They can have 24/7 agents working, and if the car breaks down at midnight, the chat is there for you.”

Naphade said Capital One would love to bring this type of agent to its travel business, especially for its customer-facing engagements. Capital One, which opened a new lounge in New York’s JFK Airport, offers a very popular credit card for travel points. However, Naphade pointed out that the bank needs to conduct extensive internal testing.

Data and models for bank agents

Like many enterprises, Capital One has a lot of data for its AI systems, but it has to figure out the best way to bring that context to its agents. It also has to experiment with the best model architecture for its agents. 

Naphade and Capital One’s team of applied researchers, engineers and data scientists used methods like model distillation for more efficient architectures.

“The understanding agent is the bulk of our cost because that’s the one that has to disambiguate,” he said. “It’s a bigger model, so we try to distribute it down and get a lot of bang for our buck. Then there’s also multi-token prediction and aggregated pre-fill, a lot of interesting ways we can optimize this.”

In terms of data, Naphade said his team had undergone several “iterations of experimentation, testing, evaluation, human in the loop and all the right guardrails” before releasing its AI applications. 

“But one of the biggest challenges we faced was that we didn’t have any precedents. We couldn’t go and say, oh somebody else did it this way, so we couldn’t ask how it worked out for them?” Naphade said. 

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