Ramp's Ian Macomber on Building Model-Agnostic Systems

What happens when you enable SQL engineers to build sophisticated AI systems with just 26 lines of code? At Ramp, it sparked a transformation in how they approach AI implementation: instead of relying on ML specialists, they're democratizing AI development across their organization.

But that's just one part of Ramp's unconventional story. As Head of Analytics Engineering & Data Science, Ian Macomber explains how processing financial data for 30,000 companies led them to reimagine enterprise AI architecture. By building model-agnostic systems that can switch seamlessly between foundation models, they're creating sustainable competitive advantages while maintaining cost efficiency.

In this episode of The AI Adoption Playbook, Ravin sits down with Ian to unpack how Ramp evolved from basic receipt matching to sophisticated cross-functional AI systems that are reshaping enterprise financial operations.

 

Topics discussed:

• How Ramp's decentralized approach to AI procurement enabled rapid experimentation while maintaining standards and leading to the discovery that most point-solution AI tools show surprisingly weak retention rates.
• Their data architecture strategy: managing complex databases with 133,000 columns by implementing semantic layers and guardrails that make enterprise-scale text-to-SQL accessible to non-specialists.
• The small team philosophy they implemented: breaking larger teams into units of 12 or fewer people, fostering rapid iteration while maintaining the velocity of a startup despite growing to over 1,000 employees.
• Their approach to foundation model selection: treating AI providers like ride-sharing services, constantly evaluating performance and cost metrics to switch between models based on specific use cases.
• Their strategy for building defensible AI products: focus on sophisticated integrations that combine organizational data, spending policies, and real-time market information in ways that point solutions can't replicate.

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Episode 2.