NomadGo Introduces Early Access Program to New Inventory Reasoning Engine, Bringing Physical-World Reasoning to the Supply Chain

NomadGo, the industry leader in Inventory AI, announced its NomadGo Early Access Program of NomadGo’s new intelligence layer that expands NomadGo Inventory AI from observing the physical world to actionable reasoning. This new offering provides even greater access to live, location-level inventory data from NomadGo, while adding a reasoning engine that helps operators and businesses ask better questions, understand conditions, receive recommendations, and take action based on what is actually happening on the ground. Select customers can now trial the physical-world reasoning engine, ahead of full availability in Q2 2026.

NomadGo introduced NomadGo Inventory AI in Q4 2025, setting the industry foundation for delivering fast, frequent, and accurate visibility into physical inventory, including counts, velocity, utilization, and capacity. The new reasoning engine reflects the next stage in that journey, extending the platform based on customer-led experimentation and real-world learnings. In its current form, the engine builds on observed physical inventory signals to surface explainable insights and recommended actions, helping business owners explore new ways to translate physical-world data into meaningful operational value.

Read More: SalesTechStar Interview With Matt Quarles, Chief Revenue Officer, Tiugo Technologies

This engine reasons over live, location-level inventory data captured directly from physical environments. This data is continuously generated by NomadGo, which observes and captures inventory across shelves and storage areas using an iPad or iPhone device. Rather than introducing a new data source, the NomadGo now adds a new layer of intelligence, enabling reasoning and decision-making on top of the physical reality NomadGo Inventory AI already observes.

“NomadGo’s updated Inventory AI tool increases our ability to see and manage inventory across our restaurants,” said Russ Lo Bello, President of The Phoenix Organization and a Burger King franchisee. “For example, one use could be when a store unexpectedly runs out of an item, our teams often must react after the fact by calling nearby locations and manually coordinating transfers. What’s compelling is the idea that it could recognize those conditions earlier, understand why a shortage is likely, and proactively recommend or initiate a store-to-store transfer before it impacts sales. We see this as an important step toward a future where inventory issues are anticipated and handled automatically, not discovered at the last minute.”

Read More: Salestech for Network-Led Growth: Turning Internal Relationships into Pipeline

From Insight to Action

Delivered through an easy-to-use web application, the engine allows operators and planners to:

• Explore inventory conditions across locations: See what inventory actually looks like across stores, sites, or facilities, such as what’s on the shelf, what’s missing, where space is constrained, and where capacity is being under-used.
• Ask intent-driven questions in natural language: Ask practical, operational questions, such as, “What items are at risk of stocking out?” or “What should be refilled first?” without building reports or dashboards.
• Receive clear explanations and prioritized recommendations: Understand not just what is happening, but why. For example, why a product is repeatedly out of stock, why a shelf remains empty, or why space is being wasted along with ranked recommendations that show what will have the biggest impact if addressed next.
• Take action directly: Move seamlessly from insight to execution by initiating targeted scans, sharing inventory insights with store or operations teams, or adjusting replenishment and layout decisions based on real-world shelf and space conditions.

“Traditional business intelligence tools summarize what has happened, and general-purpose AI models reason over the data they’re given,” said David Greschler, CEO and co-founder of NomadGo. “Our reasoning engine is different. It reasons over live, location-level inventory data captured directly from physical environments through our app. This allows our customers to understand and act on real-world supply chain conditions – from availability risk and space utilization to compliance and the financial impact of inventory decisions – helping them make smarter, faster choices every day.”