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The Next Phase of Digital Twins in Retail

Explore how digital twins are becoming production-ready in retail through accurate store data, edge AI, and actionable operational workflows.

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Authored By

Călin Ciobanu

Co-founder & CTO

Digital twins are entering a new phase in retail. For years, the concept has promised a way to connect the physical and digital worlds. But what is changing now is our ability to make that connection accurate, frequent, and practical enough to support real decisions. A physical store is constantly changing. Products move, shelves need replenishment, prices change, promotions come and go, and inventory shifts throughout the day.

The challenge is not simply creating a digital representation of the store. It is keeping that representation accurate and current enough to reflect what is actually happening on the shelf. A digital twin becomes valuable when that understanding is accurate and frequent enough to drive action.

What changed?

Three developments have brought digital twins closer to this reality: frequency, cost, and accuracy.

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OmniShelf’s On-Device AI solutions have made frequent image capture and data transfer more practical and economically viable. At the same time, computer vision accuracy has improved. Accuracy is particularly important. Even cloud-based computer vision solutions are often below 85% accuracy. At that level, the data may exist, but it is difficult to act on reliably.

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The real shift happens when these three factors work together: enough data, captured frequently enough, at a cost that makes sense, and with enough accuracy to support decisions.

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In our recent Q2–Q3 2026 implementations and benchmarks, we have reached 95–99% accuracy for products, prices, and marketing materials, within two weeks of preparation and two weeks of testing. Once the data becomes reliable enough, the question is no longer whether a digital representation can be created. It becomes what can we actually do with it?

Designing for the real store

Getting the accuracy right is only part of the problem. Making computer vision work in retail means designing for the environment where the technology actually operates. Retailers have different hardware and technology ecosystems. Connectivity can be limited. Solutions need to work across large numbers of stores and tens of thousands of SKUs, while remaining fast and responsive for users.

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These constraints shaped our approach at OmniShelf. We researched and developed a novel edge-first computer vision AI designed to run on extremely low-compute edge devices. Our models can run on hardware with just 2 ARM cores and 250 MB of RAM, while processing a full shelf in less than 10 seconds. The solution was also developed to outperform major cloud-based approaches, including Google and Microsoft, on product and other shelf-element recognition.

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Image 1.1: OmniShelf's Edge AI Computer Vision Built for Existing Retail Devices

But the objective was not simply to make the model smaller. It was to make computer vision compatible with the conditions of physical retail: existing devices, low internet bandwidth, limited connectivity, and the need for a fast and responsive user experience.

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That compatibility is critical for deployment. Our focus has been on making the technology plug-and-play within existing client ecosystems, rather than requiring retailers to rebuild their infrastructure around it.

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When you build AI for the physical world, the technology has to adapt to the environment, not the other way around.

From seeing the shelf to acting on it

Once a physical environment can be understood with sufficient accuracy and frequency, the digital layer becomes much more useful. Consider replenishment. Cameras or other signals can monitor shelf conditions and trigger tasks for store employees, allowing replenishment to become proactive.

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The same data can support retail media. Digital signage and electronic shelf labels represent valuable advertising space inside stores, and better visibility into that space can help retailers optimize how it is used. At the shelf level, the technology can also support on-shelf availability, planogram execution, and inventory accuracy, with an impact on both operational costs and sales.

Image 1.2: From Physical Shelf Data to Operational Action

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These use cases share the same underlying idea: what is happening physically can become structured data, and that data can become action.

Production readiness is the real challenge

This is where the gap between a digital twin demo and a production system becomes clear. The biggest challenge is not proving that the technology works in a laboratory. It is taking that solution into a large-scale, dynamic, imperfect production environment while remaining economically viable.

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At retail scale, almost every part of the system becomes a challenge: data quality, infrastructure, reliability, scalability, connectivity, real-time processing, and integration with existing digital systems. Our years of research and development were put to use in building a system that meets production criteria and has been battle-tested in demanding conditions. A computer vision model is only one part of that equation.

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At OmniShelf, the surrounding ecosystem needs to continuously maintain the data behind the digital representation. That includes maintaining product databases containing tens of thousands of SKUs, processing hundreds of thousands of reports daily, and maintaining a high-confidence SLA for production reporting. It also means integrating into existing client ecosystems rather than asking retailers to change their infrastructure.

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For me, this is the real definition of production readiness: the technology has to work consistently, at scale, in the conditions where the business actually operates, and at an economics that makes sense. This is where OmniShelf AI has moved computer vision from a technical demonstration to a production-ready capability for retail. It combines high recognition accuracy with extremely low compute requirements, limited connectivity, and the ability to operate at large scale.

What comes next?

We are still early in the market for real-time, high-fidelity digital twins in retail.

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But I don't think the next phase is primarily about making the underlying technology more sophisticated. Much of the technical foundation is increasingly there. The bigger opportunity is to productize it, discover which use cases create the most value, understand which business needs should be prioritized, and build the integrations and features that can turn the technology into measurable ROI.

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The next phase of digital twins will make it possible to continuously understand what is happening in the physical store and use that data to drive operational decisions, from proactive replenishment and better planogram execution to retail media optimization and more accurate inventory.

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That is what makes physical retail more measurable, actionable, and increasingly proactive.

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