Ramp rolls out AI agents

Finextra

11-07-2025

Ramp AI Finance Automation officially launches with a new set of AI agents that are meant to make the work of finance teams easier.
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Ramp's smart automation helps businesses of all sizes cut down on manual tasks, increase productivity, and make decisions about money faster. It does everything from approving expenses to analysing spending in real time.
Finance teams are being asked to do more with less, yet the function remains largely manual. Teams using legacy platforms today spend up to 70% of their time on tasks like expense review, policy enforcement, and compliance audits. As a result, 59% of professionals in controllership roles report making several errors each month.
Ramp's agents for controllers solve these problems by eliminating redundant tasks, working autonomously to review expenses and enforce policy.
Built on Ramp Intelligence, powered by OpenAI's reasoning models
Ramp's agents for controllers apply context-aware, human-like reasoning to manage entire workflows independently and proactively. Unlike traditional automation that relies on basic rules and conditional logic, these agents reason and act on behalf of the finance team, working independently to enforce spend policies at scale, immediately prevent violations, and continuously improve company spending guidelines. Ramp agents are meticulous, auditable, and consistent, escalating issues when needed and providing a clear audit trail for every decision. Early customers reported 99% accuracy in expense approvals1.
Ramp's AI agents act on data instantly, giving finance teams a co-pilot for real-time operations by suggesting actions and pointing out problems.
'Before Ramp agents, we manually reviewed 100% of transactions. Now, Ramp agents take the first pass and flag what actually needs our attention. Every decision Ramp makes is logged with a clear audit trail. Accuracy matters, and Ramp consistently gets it right. We've seen fewer errors, faster reviews, and stronger policy enforcement across the board,' said Richard Gobea, Finance Manager at Quora.
Ramp agents are powered by Ramp Intelligence, Ramp's AI platform that automates expense reporting, data entry, contract review, and accounting accuracy. Agents learn and adapt directly from company policies and feedback from users to:
Approve low-risk expenses or provide a recommendation with rationale to the approver
Alert of suspicious receipts and invoices
Answer employee questions about spend policy
Uncover trends that signal fraud or careless spend
Suggest edits to company expense policies based on usage and feedback
'Ramp agents have complete knowledge of your accounting rules and expense policies that employees don't carry in their heads, plus instant access to transaction details that finance teams would need time to gather. This lets them act faster and more accurately on every transaction,' said Karim Atiyeh, co-founder and CTO at Ramp. 'This isn't just automation. It's intelligent reasoning that handles complex financial decisions to reduce errors, strengthen policy enforcement, and stop fraud.'
'It's amazing to see what Ramp has built with our newest reasoning models,' said Olivier Godement, head of platform product at OpenAI. 'These agents are taking care of key financial processes and, most importantly, getting them right – letting teams focus on deeper strategic work.'
The AI advantage for finance
Ramp agents put best-in-class AI into the hands of finance teams and provide a new layer of engineering support to resource-constrained teams. Ramp invests 50% of its payroll in research and development so every finance team, no matter the size, can benefit from the latest breakthroughs in AI automation and reasoning.
'Ramp takes the manual work off our plate and gives us the confidence that we're ahead of emerging AI fraud threats before they hit us,' said Lawrence Dann-Fenwick, Head of Strategic Finance at Hex.
Finance teams at leading AI companies like Notion, Hex, Sierra, and Quora already use Ramp to move faster, operate smarter, and stretch every dollar further.