Ramp, a rapidly growing fintech company valued at $7.6 billion, has embedded AI and machine learning deeply into their operations since their founding in 2019. Their journey represents an interesting case study in how to systematically integrate AI across an organization while maintaining a focus on practical business outcomes. **Evolution of ML/AI Usage** The company's use of AI evolved from traditional machine learning applications to more sophisticated generative AI implementations: Early ML Applications: * Underwriting and Risk Assessment: Using bank transaction data and credit bureau information for decisioning * Fraud Detection: Implementing real-time fraud prevention using Materialize and ClickHouse for rapid response * Receipt Matching and OCR: Automating expense management through document processing * Customer Support Automation: Handling routine queries and ticket routing Infrastructure and Technical Stack: * Core Infrastructure: AWS for storage and core applications * Data Warehousing: Snowflake for data storage and analytics * Real-time Processing: ClickHouse and Materialize for fraud detection and real-time analytics * AI Services: OpenAI and Azure for AI capabilities * Vector Databases: For semantic search and similarity matching * Analytics: Looker for business intelligence and data visualization The company maintains a horizontal Applied AI team that works across all departments, focusing on identifying and implementing AI opportunities throughout the organization. This team has unusual latitude to embed with any department to optimize processes and implement AI solutions. **Ramp Intelligence Suite** In 2023, Ramp launched their Intelligence suite, which includes several AI-powered capabilities: * Price Intelligence: Analyzes and compares vendor pricing across their customer base * Expense Intelligence: Automated categorization and compliance checking of expenses * Accounting Intelligence: Provides "autocomplete" functionality for accounting categorization Rather than following the trend of implementing generic chatbots, Ramp focused on specific use cases where AI could provide concrete value. They've been particularly successful with: * Sales Development Automation: Their SDRs book 3-4x more meetings than competitors through AI augmentation * Marketing Content Generation: Using Midjourney for ad campaigns * Internal Knowledge Management: An AI system named "Toby" that can analyze over 100,000 customer calls to provide insights about customer sentiment and sales strategies **Experimental AI Agent Development** Ramp is currently experimenting with more advanced AI agents, including: * A multimodal GPT-4 powered system that can see and interact with the user interface * Guided tour capabilities where the AI can demonstrate how to use platform features * Automated task completion for common operations like issuing cards or booking travel The company reports 60-90% success rates with these experimental features, though they maintain strict control over financial operations due to the sensitive nature of their business. **Organizational Structure and Development Philosophy** Ramp's success with AI implementation is supported by their organizational structure: * Small, autonomous teams (typically 13-14 people) with clear ownership * Centralized data team under the CTO to maintain consistency * Customer support reporting to product to ensure feedback loops * Integration of AI capabilities into existing workflows rather than creating separate AI products **Data Privacy and Security** Given their role in financial services, Ramp maintains strict data privacy controls: * Aggregated data sharing only on an opt-in basis * No direct sale of customer data * Strict data privacy agreements for third-party AI services * Regular auditing of data usage and access **Key Lessons and Best Practices** * Focus on augmenting human capabilities rather than full automation * Maintain small, focused teams with clear ownership * Integrate AI capabilities directly into existing workflows * Prioritize practical business outcomes over technological sophistication * Build strong data infrastructure before attempting advanced AI applications * Maintain strict privacy controls while still leveraging aggregate insights The case study demonstrates how a modern fintech company can successfully integrate AI throughout its operations while maintaining a balance between innovation and practical business value. Their approach of focusing on specific, high-value use cases rather than generic AI applications has proven particularly successful in their domain.