The Agentic Layer and the New Plumbing of Retail

Pegasus Spotlight: Clootrack and the Rise of the Voice-of-Customer Super Agent

Had a great conversation with Shameel Abdulla, CEO of Clootrack, this morning, and came away energized about what his team is building. Most brands have gotten very good at measuring customer experience but not at driving outcomes from it. Clootrack closes that gap. It's an AI Super Agent for Voice of the Customer that doesn't just tell retailers and CPG brands what happened, but why it happened and what to do next, connecting customer feedback with transactions, churn, returns, and operational data to actually move the needle. I'm also excited about their new MCP server, which brings this customer intelligence directly into Microsoft Copilot and other AI work fronts, so teams can ask questions in plain language and get governed, evidence-backed answers. Clootrack is a great example of the kind of Agentic AI company we love supporting through the Microsoft for Startups Pegasus Program.

Microsoft Foundry Labs: A Practical Guide for Retail Innovation

Most frontierAI research isn't built with retail in mind, but a handful of models in Microsoft Foundry Labs map onto real retail problems remarkably well. I went through the catalog and picked out the ones with a genuine use case, from product imagery and conversational commerce to search, optimization, and 3D shopping. I also dig into why in-house retail teams can treat these as building blocks rather than off-the-shelf products, and how pairing them with startups in the Microsoft for Startups portfolio can accelerate the work. If you're a retailer or brand thinking about where to build, I'd love to hear from you.

What a Month of Building Autonomous Projects Taught Me

I spent the last month building autonomous projects: a hashtag#retail operation that designs and sells t-shirts off trending phrases, a pipeline that turns photos into 3D-printed figurines, and a newsletter that translates retail news into insights and actions rather than just headlines. I learned a lot along the way. That you no longer need coding skills to build real things, and that you can build them in a fraction of the time. That debugging can be handed to hashtag#AI, and watching it explain its reasoning is genuinely educational. That the hard part has quietly moved from writing code to saying clearly what you actually want. But the biggest realization was this. All of this technology is democratizing the ability to create. The challenge no longer lives in the tools or the skill to code something. It lives in creativity, in coming up with ideas worth building that no one else has thought of. That is the new skill all of us need to develop, and it is what becomes our unique strength.

I Stopped Asking AI for the News. I Asked It What the News Means.

What if your news feed answered "𝒘𝒉𝒂𝒕 𝒅𝒐𝒆𝒔 𝒕𝒉𝒊𝒔 𝒎𝒆𝒂𝒏 𝒇𝒐𝒓 𝒎𝒆" instead of "what happened"? I wrote a job description for an #AI [https://www.linkedin.com/search/results/all/?keywords=%23ai&origin=HASH_TAG_FROM_FEED] agent using #MicrosoftScout [https://www.linkedin.com/search/results/all/?keywords=%23microsoftscout&origin=HASH_TAG_FROM_FEED], that reads 30+ #retail [https://www.linkedin.com/search/results/all/?keywords=%23retail&origin=HASH_TAG_FROM_FEED] news sources every week and delivers trends and takeaways rather than headlines. New post on how I built it, the exact instructions that power it, and a link to the first live issue.

Two Reliable Paths to Startup Innovation: Curated Portfolios and Independent Frameworks

Earlier today I had a great conversation with Georges F. Mirza, creator of the ARS² Framework and founder of ComTask, and it prompted me to write down something I keep seeing with the #retailers and #brands I work with. Around 70,000 B2B Tech #startups launch every year and most fail within two years. Yet the capabilities retailers need most right now, especially in agentic AI, live inside those young companies. The result is a familiar cycle: a promising demo, a quick assessment, a pilot that stalls, and the search starts over. In this article I lay out two parallel paths out of that cycle. The first is the curated route through Microsoft for Startups and the Pegasus program, where solution categories are defined through direct customer engagement, sourced from tier 1 #VC portfolios, and vetted for enterprise readiness. The second is for the startups you find yourself, at trade shows or through your own scouting, where two frameworks let you do the vetting independently: ARS² to confirm the solution delivers accurately, repeatedly, at scale, and with speed, and the Azure Well-Architected Framework to confirm the engineering underneath can carry it. Thanks to Georges for the conversation that sparked this. The full article is linked below, and I'd welcome perspectives from others who evaluate startups for enterprise deployment. What does your process look like?