Background

Questions

1. One foundational technology that enables Ramp is card issuing infrastructure. Another (potentially) is OCR. How do you think about LLMs like GPT-4 as a foundational technology (or not) for Ramp and what does it enable Ramp to build that couldn't exist before?
2. Ramp recently announced a suite of GPT-4 powered services across expense management, vendor management and bookkeeping. Can you give us context on how this initiative came about? What's the core problem statement around incorporating AI into Ramp and what do customers want that AI can solve?
3. From Ramp's perspective, what did the world look like before GPT-4 and what does it look like after?
4. What's an example of a customer pain point that previously could only be solved expensively via mechanical turk that GPT-4 can now solve at a fraction of the cost?
5. In finance, many solutions exist as services rather than as software. For example, a Series A startup might have a controller, a fractional CFO and a software-enabled bookkeeping service like Pilot. What do you make of AI as the promise of replacing high-cost services with low-cost software, how far does that extend and what do you think some breakpoints will be? What does the finance team of the future look like given what you expect that AI will be able to accomplish?
6. The chatbot or copilot has become the main embodiment of AI from a product perspective. Ramp has both taken the approach of integrating AI throughout the product not only embodying it in a single product feature. It also has a copilot feature. Can you talk about Ramp's approach to interfaces into AI and how it sees chatbots specifically? Is integrating natural language interfaces into, e.g., its Slack integration important to making financial data more accessible?
7. How does the interface of software evolve with an extremely strong AI in the background?
8. Can you talk about your vision for autonomous agents in finance and what that might look like inside of Ramp? Let’s say Ramp gives you an AI controller who autonomously works with a bookkeeper to close the books every month. What needs to happen for this vision to become realized?
9. By using Ramp cards or Ramp's Gmail extension, Ramp aggregates a massive amount of data about customer spending that enables it to save customers money, incentivizing customers to give Ramp more access to data. How do you think about where AI sits in this flywheel and what it accelerates? Does the problem reduce to aggregating proprietary customer data assuming that AI deployment will get commoditized and become undifferentiated?
10. Vendor management appears to be a place where AI has the potential to turn services margins into software margins. Can you talk about how vendor management, particularly negotiations, worked pre-AI and how you envision it will work post? How can GPT-4 deliver on the promise of negotiation with an automated software approach vs a people driven and manual one?
11. Hallucinations and trust & safety are two major issues for LLM-powered products and nondeterministic output, especially for the highly sensitive use case of B2B finance. How has Ramp built on top of GPT-4 to deal with these issues? What has Ramp done to ensure a high quality, reliable, consistent experience for customers?
12. Ramp has been moving into enterprise. What do Fortune 500 or 50 companies think about AI-powered products, what are their concerns re sending data to OpenAI, and how does that change what you build?
13. Can you help us understand the underlying architecture of the AI service that powers the different services in the Ramp product? What reinforcement learning if any that happens? Any key vendors in the "AI stack"?
14. Ramp long has sought out and teamed up with best-in-class partners, e.g., Stripe. Ramp has partnered with OpenAI, Ramp's a customer of OpenAI's and OpenAI is also a customer of Ramp's. Can you talk about Ramp's partnership with OpenAI?
15. Tell us about Ramp's acquisition of Cohere.io and how it fits into Ramp's AI strategy.
16. Can you talk about whether Ramp evaluated other LLMs like Claude (Anthropic), and how Ramp thinks about designing for optionality versus building specifically for one LLM?
17. Ramp has emphasized speed in product development as its major competitive edge. How has GPT-4 helped Ramp build faster, i.e. as a generic API for turning unstructured data or semi-structured data into structured data?
18. Ramp has 500 employees, Brex has ~1,000 and Rippling has ~2,000. How has AI changed how Ramp hires and builds its team internally, and how it thinks about doing more with less?
19. "Software is eating the world" has become "AI is eating the world". How does AI become an accelerant to ambitious teams going multiproduct and eating up adjacent use cases? How does AI change the trajectory of company building?