Navan

What if an AI assistant could recognize that it’s missing your goal — and change course before you have to ask for help? At @Navan, we’re exploring how signals inside Ava can reveal when a traveler’s need isn’t being met, so Ava can respond more thoughtfully or connect them with a person sooner. I wrote about how interpretability can make AI assistance more useful in real time and talked about it at Navigate. Read more at lnkd.in/dK98WBXQ

What does it take to build an agentic system for corporate travel that’s not just smart, but fast and reliable? It takes a lot of hard work beyond choosing an AI model. That’s why we have a dedicated team building tools to benchmark, fine-tune and continuously improve our platform. Our latest work with @NVIDIAAI Nemotron 3.5 Super VL is a great example of how we continuously develop our models. We managed to get early access to NVIDIA AI’s Nemotron 3.5 Super VL model and found it to be a powerful tool. It performed well on intelligence benchmarks and outperformed our production baseline. After fine-tuning, it beat or tied competing models on most measures while delivering the efficiency needed to run agentic systems at scale. It was also our most intelligent model with the shortest latency. We completed the process in a couple of weeks at an estimated cost of $3,000, demonstrating how efficiently we can build and train these tools. We’re sharing what we learned in case it’s helpful to other model builders exploring how to use AI to solve real-world problems, whether in travel, healthcare or another industry. Read more: navan.com/blog/nvidia-ai-tra…