I started my career in AI Automation, but I found myself increasing drawn to systems, problem-solving and understanding why things work the way they do. I enjoy breaking complex problems into smaller parts, identifying the variables that matter and designing systems that produce predictable outcomes.

As I explored SaaS, I became increasingly interested in the gap between the customer signals companies collect and the decisions they make with them. Product analytics, behavioral events, support conversations, billing activity and CRM data often contain the earliest indicators of customer health, revenue risk and growth potential. Yet those signals frequently remain fragmented across tools and teams.

Revenue Intelligence sits at the intersection of data, automation and decision-making. That’s what makes the space compelling to me. The challenge isn’t simply collecting information. It’s designing systems that transform information into actions that create, protect and grow revenue. That’s eventually what led me to focus on AI tools and usage-based SaaS specifically; businesses where consumption patterns, not just logins, are the clearest early signal of customer health and revenue risk