Ariel Cohen1:59
So from day one we started the company 11 years ago. Recently took our public. Always, always thought about how can we make it super efficient, super fast, super easy to use. So from day one it actually meant machine learning because you really, really need to understand the user. You need to understand what they want. You need to kind of organize the search correctly. How do you think about cost? Saving money. All of these things. We actually save money for companies that are using us. On average, it's 15% and it's for the entire travel budget. So the companies that will spend $100 million a year on travel, we're going to basically make it 85, and it's all machine learning. So this is kind of the early days. But then, you know, as we thought more and more on the service component, you know, stuck in an airport, we started to develop probably six years ago kind of early stages of conversational bots to do that, but they were kind of covering, I don't know, 10% of the problem with the satisfaction of 60%, right? So kind of a good experiment but definitely something that doesn't really work. When GenAI came, obviously it was early, so we couldn't just deploy it, because if you think about travel, travel is super important. I cannot send you to the wrong flight or I cannot. So hallucination was very, very serious back then. So we had to develop our own platform. So we basically developed something that we call internally Navan Cognition, which is an orchestration platform. It's basically an agentic platform, but what's so unique about it is that it's actually orchestrated between AI agents and actually travel agents, and both are super, super important. I know that everybody has this opinion like in five years nobody will be employed, maybe nobody will ever need travel agents, nobody, whatever. I actually think it's bullshit. I think travel is so, so complex, so fragmented, so personal, that the only way to make it work is really to start with an AI agent that can book your flight, change your flight, I don't know, take care of a receipt, all of the things that if you're using Navan, you're actually seeing in the platform, but then knows when somebody else needs to intervene. An example: in the 24 hours before the flight is happening, there is no way, API, AI to do anything. You can try to call with some bot to the airline, good luck. So you need to really know how to orchestrate it, start a discussion here, move it there, and create a perfect, perfect experience. So just to give a statistic here, today 60% of all of the support interaction — this means I'm stuck in the airport, I want to change my trip, I want to do complex stuff — are actually done by Eva, our chatbot. A big number of the transactions are based on our model. We are also using frontier models. But the way that it works, we brought satisfaction to almost the level of a human agent. But it's because of this orchestration that I've talked about, because of the fact that when it fails, we actually know how to move it, continue the discussion, finishing it. So again, the human element here is super important.