Ramp Wants to Apply AI to Complex Financial Workflows

Financial operations platform Ramp has introduced its Ramp Applied AI Solutions offering.

The new tool, announced Wednesday (June 10), is designed to help larger enterprises deploy artificial intelligence (AI) agents for complex financial workflows.

“In finance, every decision depends on buried layers of context: the policy, the vendor, the contract, the approval chain, and the exception history,” Ori Daniel, head of AI solutions at Ramp, said in a news release. “Applied AI Solutions helps enterprises capture that context and turn it into agents that can complete work safely, with the controls finance teams need.”

According to the release, the new tool is designed for workflows that “off-the-shelf tools” have trouble automating. That includes processes that involve multiple systems, are dependent on company-specific policies, and require judgment when exceptions occur.

“Ramp combines finance workflow expertise, product judgment and dedicated FDEs to identify high-impact opportunities, extract the context agents need, and deploy AI safely into production,” the release added.

The rollout of Applied AI Solutions follows last week’s release of Ramp Stack, the company’s AI-powered offering for the accounting sector.

“Accounting firms are under more pressure today than at any point in history,” said Geoff Charles, Ramp’s chief product officer. “The firms we work with aren’t asking for another AI tool to prompt. They need something that actually does the work, with every decision reviewable and auditable. That’s what Stack is built to do, starting with the close, and expanding into every workflow that stands between their team and higher-value client work.”

In other agentic AI news, PYMNTS wrote Wednesday about the role the technology is playing in the checkout process.

“The question is no longer simply whether consumers like a buying experience. Increasingly, it is whether AI systems can complete it efficiently,” that report said. “As digital commerce becomes automated, checkout is evolving into infrastructure. It is becoming the connective layer between intent and fulfillment, with implications extending beyond payment processing.”

An AI purchasing assistant that compares two merchants might favor the one with lower authentication barriers, standardized payment credentials and more predictable fulfillment systems, even in situations where price differences are miniscule, the report added.

“Every additional prompt, redirect or verification request introduces computational and experiential friction that can influence machine-driven decision-making,” PYMNTS added.

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