When I joined Stord, I came in with a specific belief: that the companies that would define the next generation of commerce infrastructure were the ones that figured out how to build and validate operational AI and robotics at network scale. Not in vendor demos. In real operations.
The question was how to create this in a tangible way. Six months in, we had an answer. Stord Labs.
Stord Labs is a physical intelligence lab at our Atlanta headquarters. It replicates the core workflows of a modern fulfillment operation across receiving, storage, picking, packing, shipping, and returns, and runs real and synthetic orders through the same WMS and OMS systems powering our production facilities. It is where we build and validate the AI, agentic robotics, and operational innovations that power Stord's network. These are real systems running against real operational complexity.
The demo problem
If you spend time in logistics operations, you learn quickly that vendor demonstrations are a poor proxy for real performance. A robotics system that runs beautifully in a controlled environment with a single product type and predictable order patterns can fail completely when introduced to the complexity of a real multi-client environment: mixed SKU profiles, variable order sizes, seasonal demand swings, and the constant variability that comes with running operations at scale.
Determining which technologies actually improve performance in a real environment is difficult. Testing these systems directly in live operations introduces disruption and potential chaos that no brand should have to absorb.
Stord Labs solves that problem. Our innovations face real conditions and are stress tested for performance. Real orders flow through the system. Real workflows create real pressure. And the data tells us the truth. These are not simulations in ideal scenarios, they are validated approaches that drive real outcomes.
How we built it
The design of the lab was deliberate. We replicated the operational zones found in a production facility: a storage and picking zone for robotics integration and inventory strategy experiments, a packing and consolidation zone for order assembly workflows, a flexible automation test area for agentic robotics pilots, and a control tower workspace for AI model development, real-time operational intelligence, and digital twin modeling. The agentic robotics work is where I am most excited. Traditional warehouse automation is scripted. Robots follow fixed paths and execute predetermined tasks. Agentic systems are different. The AI observes the environment, makes decisions in real time, and directs physical action dynamically. That closes the gap between what automation promised and what it actually delivers in a complex, variable, multi-client environment. Building and validating our agentic systems requires the environment Stord Labs provides.
We can generate synthetic orders to simulate peak demand scenarios and route orders back through the system as returns to test reverse logistics workflows. We can run mystery shopping scenarios (placing orders the way a consumer would) to evaluate fulfillment speed, packaging quality, and the end-to-end consumer experience from checkout through delivery.
The goal was to build an environment rigorous enough to generate meaningful data but flexible enough to support the range of experiments we needed to run.
The rigor matters as much as the setup
A lab is only as valuable as the discipline applied to it. We designed Stord Labs around a simple principle: every experiment begins with a clearly defined hypothesis, success criteria, and measurement framework. We do not run experiments without knowing what success looks like in advance.
Technologies that demonstrate measurable improvement move into deployment across the network. Technologies that do not are discontinued quickly. No prolonged evaluations. No vendor-managed assessments. The data decides.
This sounds straightforward but it requires real discipline to maintain. Stord Labs is designed to move fast, measure clearly, and scale what works.
What this means for the network
Every improvement validated at Stord Labs flows across Stord's nearly 100 facilities worldwide. That is the part of this that I find most compelling.
Because Stord's technology and operations run on a single unified stack, a proven improvement doesn't need to be re-integrated or re-validated at each location. It deploys everywhere, across nearly 100 facilities, 20 of which we self-operate. When we validate a pick path optimization that reduces travel distance by a meaningful percentage, that improvement does not stay in Atlanta. It scales. When we validate a demand forecasting model that reduces stockouts, every brand on the platform benefits.
What comes next
We are just getting started. The first phase of Stord Labs is focused on establishing baseline workflows and running our initial experiments around picking strategies, inventory logic, and robotics integration. Over the course of this year, validated improvements will move into deployments across the network.
The real frontier is agentic AI and physical operations. Most AI in logistics today is analytical. It tells you what happened and suggests what to do. What we are building at Stord Labs is operational: AI that acts on real physical systems. Agentic powered robotics that dynamically re-route based on live order flow. Forecasting models that don't just predict demand but trigger inventory repositioning autonomously across the network. Pick optimization that changes what the robot does right now. Applied to real operational data, these systems produce outcomes that models built on synthetic data cannot replicate. The results we are seeing early are exactly what we hoped for.
In the near future we will also be opening Stord Labs to customers. Quarterly demonstration days will give brands on the platform a chance to see firsthand what we are testing. Selected partners will have the opportunity to co-develop solutions to their specific operational challenges inside the lab.
Stord Labs is how the physical intelligence era gets built at network scale. But more than that, it is how every improvement we make at 10,000 square feet compounds into a better consumer experience for every brand on our platform. Faster delivery. Fewer stockouts. More accurate promises. A consumer relationship that gets stronger with every order.
