Skan AI buying intent

Folks, I'll be at the 2026 Citi AI Summit next week. Looking forward to a robust discussion on "Agents in the Enterprise" with Umesh Sachdev (Uniphore) , João (Joe) Moura (Crew AI) , Rob Bearden ( Sema4.ai ) and Vibhor Rastogi On this emergent mosaic of human and AI agents we will discuss context , control planes , observability , orchestration, pricing and ROI. Stay tuned. cc Manish Garg

I've written a lot of words about Skan AI this year. Positioning docs, campaign briefs, website copy, analyst materials, etc. But the #Nasdaq tower in Times Square just said it better than any of them. This is what it looks like when the narrative catches up to the moment! #SkanAI #AgenticAI #ContextGraph

Monday Musings: The Deal We Walked Away From Sometimes the hardest sales decision isn't closing; it’s choosing not to. In the early years of Skan, we were in conversations with a large enterprise prospect. Good brand name. Decent budget. The kind of logo that makes your investor deck look impressive. After months of conversations, demos, and discussions of deal parameters, we walked away. Not because the deal fell apart. Because we chose to let it go. Here's what happened. As you all know, Skan’s core promise and premise are to use computer vision and machine learning to observe how work actually gets done inside organizations. This is not how the PowerPoint or Visio says it is done. How it actually gets done. That distinction matters more than most people realize. This prospect came to the table, treating our platform like a widget. Something to plug into a gap in their tech stack, check a box, and move on. Their procurement team ran the conversation. Not the operations leaders who'd actually use it. Not the people who understood why observing real work patterns could reshape how they thought about their processes. That was the first red flag. When procurement drives the strategic technology conversation, you end up negotiating line items rather than discussing outcomes. Then came the feature requests. They wanted customizations that would have essentially turned us into a services shop building bespoke tooling for their environment. Every call added another "could you also..." to the list. They would have pulled our engineering team off our product roadmap for months. There was also a cultural mismatch that's harder to quantify but easy to feel. They wanted a vendor they could manage. We needed a partner willing to rethink how they understood their own operations. Walking away from revenue when you're a startup feels counterintuitive. Every founder knows the pressure to extend their runway, show traction, and hit the numbers they told their board they'd hit. Saying no to real money requires a kind of discipline that doesn't come naturally when you're watching your burn rate. Avinash and I agonized over the decision but ultimately turned down the deal. But here's what I've learned: the wrong deal costs more than the revenue it brings in. It costs you focus. It costs you engineering cycles. It costs you the identity of what you're building. We found customers who got it. Who saw process intelligence not as a widget but as a new way to understand their operations. Those relationships shaped our platform in meaningful ways. And today, we are delivering exponential value by extending our capabilities into Agentic AI for process automation and enterprise autonomy. Not every opportunity is your opportunity. Learning to tell the difference is one of the most expensive lessons in building a company. But it's worth every dollar you leave on the table.