Raymond James 如何用 Neo4j 知识图谱为智能体 AI 提供依据

UPDATED 12:42 EDT / OCTOBER 16 2026

Knowledge graphs fuel AI innovation for financial services at Raymond James Financial

by Mark Albertson

Knowledge graphs are becoming a critical context and reasoning layer for agentic AI, helping enterprises connect data and ground agents in key areas of business expertise.

For diversified financial services companies such as Raymond James Financial Inc., graph technologies have become an important part of developing trustworthy infrastructure to support AI. The technology has been particularly helpful in building unique solutions, according to Jeff Griffith (pictured, left), vice president of AI engineering and data science at Raymond James.

“Using something like a data graph lets us harness the power of AI in dynamic ways,” Griffith said. “People already know how to go get the things that they’re getting every day. But it’s when you want to cross those domains or do some unique thing that this has a lot of value.”

Griffith spoke with John Furrier, executive analyst for theCUBE Research, at the recent GraphSummit event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. He was joined by Naveen Mahankali (right), vice president of technology at Raymond James, and they discussed how knowledge graphs and AI are changing the business landscape. (* Disclosure below.)

Knowledge graphs for data reliability

As a Neo4j customer, Raymond James uses graph technologies that give developers a way to represent the relationships, dependencies and business context that AI applications need to reason effectively. Accuracy is a critical requirement in the financial world, and Raymond James uses its knowledge graph to help agents work with data from trusted systems and evaluate results against established sources.

“We have golden sources, we have systems that we can rely on today, and we’re able to use agents to build and compare what we are building against the golden sources,” Mahankali told theCUBE. “With the knowledge graph, even when we pull the data from different sources, it sits in the Neo4j and we run evals compared to the golden sources and make sure they’re 100% accurate before launching.”

Raymond James’ work with AI is creating new efficiencies within the organization. Looking ahead, Mahankali sees opportunities for AI to reduce the time and effort financial advisors spend sourcing information for clients.

“One of the things we’re trying to do is we’re letting AI query the data dynamically so that our financial advisors get what they want without clicking on screens and without looking at a fixed dashboard,” he said. “They can do more analysis rather than just looking at the data that they already see in other applications. They can do product analysis versus how accounts are balanced and things like that by asking a question.”

The implementation of AI agents is also changing everyday processes within Raymond James itself. In January, the company launched Raimond, its proprietary digital AI operations agent.

“We have an AI assistant … it’s gotten huge adoption, and it’s just going to get smarter,” Griffith said. “That’s part of the core evolution of what we’re doing in terms of engineering. We’re changing how people, how everyone works with a desktop that gives people the best AI tools to move from doing all the menial tasks to managing agents that are doing those tasks. In terms of skills and what we are looking for, it is shifting just from people who can do tasks to people who have a drive to solve a meaningful problem.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of GraphSummit:

(* Disclosure: TheCUBE is a paid media partner for GraphSummit. Neither Neo4j, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE

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Raymond James 如何用 Neo4j 知识图谱为智能体 AI 提供依据

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