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Introducing Ramp Applied AI Solutions

Introducing Ramp Applied AI Solutions

AI Ramp
Fintech company Ramp is launching an 'Applied AI Solutions' team to embed its engineers directly into client finance departments for custom AI integration.
What: Led by former Palantir executive Ori Daniel, the unit aims to bridge the gap between high-level AI spend and measurable CFO-level outcomes by creating custom 'Finance Intelligence' layers tailored to a company's specific ERP and data workflows.
Why it matters: This reflects a broader trend of moving away from generic AI tools toward 'last-mile' implementation services, where the value lies in mapping AI agents to an enterprise's unique, often fragmented, internal data structures.
Deep dive
  • Service Model: Embeds engineers within the customer's finance team to build bespoke agents.
  • Platform: Ramp is model-agnostic, routing tasks to various frontier models based on performance and cost benchmarks.
  • Finance Intelligence Layer: Creates a semantic map between a client's GL (General Ledger) accounts, vendor coding rules, and operational context.
  • Deployment: Focused on production-grade agentic workflows for capital planning, board reporting, and month-end closes.
Decoder
  • GL (General Ledger): The central repository for an organization's accounting data, containing all financial transactions.
  • ERP (Enterprise Resource Planning): Software used by organizations to manage day-to-day business activities such as accounting, procurement, and project management.
Original article

Introducing Ramp Applied AI Solutions

AI spend within firms is accelerating. Across Ramp’s 70,000+ customers, AI token spend has increased 13x since January 2025. While 87% of CFOs say AI is critical, only 21% report it has delivered measurable results. For large enterprises, this is no longer an experiment. AI tokens have become a major expense, and the pressure to show returns is mounting.

The companies that are pulling ahead are not just spending more. They’re fundamentally changing the way they deploy AI to close the gap between tools and operational impact. That's what we built Applied AI Solutions to do.

How we got here

Ramp’s own finance team is highly reliant on AI agents in production. What we now call “Finance Intelligence” is a semantic layer mapped to how Ramp operates: GL accounts translated into operational meaning, policies connected to their sources, and reporting logic that our finance leaders now rely on to make high-stakes calls.

That layer powers agents that handle capital planning, variance analysis, board reporting, and financial close: work that previously required senior finance judgment and manual reconciliation across multiple systems. As a result, our finance team runs with a fraction of the headcount you'd expect for a company operating at our level of scale and complexity.

We heard the same issues when talking to our customers. The most important context in every finance org was never unified in one place. A chart of accounts inherited through acquisitions, approval thresholds no one updated after the last reorganization, or vendor coding rules from 2021 that lived in two people's heads. It was in the people, the exception logs, the email thread that quietly became policy. Solving that requires more than clean data. It requires understanding the business from the inside.

What we built internally and what customers were asking for pointed to the same conclusion, which is why we started Ramp’s Applied AI Solutions team. Since then, we’ve watched the AI labs arrive at the same place, investing heavily in implementation: templates, connectors, partner ecosystems. That shift isn’t a surprise. The bottleneck was never the model but the painstaking upfront work that has to happen to make data and business context legible to agents.

What Applied AI Solutions is

Ramp engineers embed inside your finance team to build bespoke solutions on top of the Ramp platform. Our team deploys these solutions on production-grade infrastructure and brings codified best practices to drive lasting adoption across your organization. Processing $200B+ annually across 70,000+ businesses, we’ve seen where finance consistently breaks, where month-end stalls, and where the real leverage hides. That domain expertise shapes every engagement:

  • We run a structured discovery process with teams to understand operations, systems, pain points, and desired outcomes.
  • We connect data wherever it lives: ERPs, data warehouses, cloud storage, paper-based workflows, and more.
  • We create the Finance Intelligence Layer that semantically describes how your business actually runs. Fragmented data becomes a set of organized objects and characteristics.
  • We build and deploy agentic workflows tied to your business KPIs, reading and writing into the business’s existing ecosystem.

Ramp is model-agnostic. We continuously benchmark models against real finance tasks, routing each production workflow to the best one based on performance, cost, and what we’ve seen work at scale. You’re never locked into a single model or provider, and as the frontier moves, what we deploy on your behalf moves with it.

Our aim is to always stay agile: we pick one workflow, go deep, and ship something that runs in production in weeks. Your team takes ownership of what we built together, and that first deployment becomes the foundation for everything that follows.

The end result looks different for every company. Some teams want an AI-native interface; others want agents running inside the tools they already use. Some teams want full ownership after handoff, others want us close as the system grows: we’re built for both.

Want to learn more?

Ramp spent years building that capability for itself. Applied AI Solutions is how we bring it to you. Let’s talk about how to move the needle with AI in your org.

Learn more about Applied AI Solutions, or get in touch directly: [email protected]

1. Deloitte, "The Year Ahead: North American CFOs Reveal Their Top 6 Expectations for 2026," CFO Signals Q4 2025, January 13, 2026.

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