Three levers get you there.
Business and AI leaders, passionate about driving end-to-end transformation in the sector.
Not incremental tweaks. Not one more dashboard. Not one more point solution.
Dry summer, less sun than last year. What does that mean for one ice cream brand — shrink the space, grow it, add flavors, or hand the space to something else? That question touches weather, demand forecasting, planogram, assortment and store execution. No merchandising copilot can answer it, because it can't see four of those five systems.
The cost doesn't appear as a line item. It shows up as the trend you caught a quarter late, the shrink in a category nobody was watching, the markdown you took because you found the problem in week nine instead of week three.
The first agentic fabric for retail.
An ontology and semantic layer built for the industry, running on world-class model abstraction. End-to-end agentic workflows on top of it. And senior retail leaders who handle the integration, execution, transformation and value capture.
This is what we do. For more detail, contact us.
Not by pushing our platform. We don't sell a platform — we sell one workload with a number attached.
Performance and margin reporting, competitive read, assortment gaps, promo and markdown recommendations, vendor scorecard and category review deck — generated overnight, sourced, ready to accept, reject or amend.
Measured onMerchant hours returned per category per week · time-to-CBR · negotiated terms
Daily recap, the variances that actually matter, shrink and cash exceptions reconciled, premium-pay and compliance leakage closed before it becomes a penalty, and a prioritized task list pushed into your existing system before the manager arrives.
Measured onManager hours returned per store per week · shrink variance closure · penalty avoidance
Every vendor agreement audited against what was billed, earned and collected — co-op, markdown money, slotting, chargebacks, cost-file mismatch. Every deduction engine on the market is supplier-side, built to recover money from retailers.
Measured onHard dollars recovered, traceable to a bank statement
The retail AI graveyard is full of technically excellent teams. What none of them had was someone who had sat in the buyer's chair with P&L accountability.
Narayan Iyengar — 25+ years in retail, consumer operations and technology. Senior operating roles at Albertsons, Disney and DISH. Former leader in McKinsey business technology, and serves on the board of an NYSE-listed retailer.
The span of a bridge is the distance between supports, and the construction material always sets that limit. Timber and masonry carry compression well and tension poorly, so their spans were short. Long crossings needed many piers. Steel carries tension, and its limit is far higher. But for decades engineers kept building steel bridges in the shape of wooden ones, member for member, and spans did not increase. A better beam by itself doesn't get you a longer span. The material sets the ceiling; the architecture decides whether you reach it.
Most AI in retail today is a steel bridge in the shape of a wooden one. Same workflow, same org chart, new material with a few copilots hung off it. A free span is the distance you cross with nothing holding it up in the middle. Retail gets one by redesigning the operating model around what agents can actually do, not by bolting the agents onto the current operating model.
Whether you are a retailer, invest in this space, or are an operating / AI leader, we'd love to hear from you.