From blind spots to digital twins – how Dexory aims to transform warehouse visibility

In one warehouse, Dexory’s robots uncovered more than $1 million of stock that managers thought was lost. In another, they flagged safety hazards before pallets collapsed. 

These discoveries sound like the closing scene from Raiders of the Lost Ark, but they highlight a challenge logistics has long struggled with – knowing what is really happening inside the four walls of a warehouse.

Dexory, a UK-based start-up, has built its business on tackling that blind spot. Its fleet of towering autonomous robots – described by co-founder and Chief Commercial and Product Officer Oana Jinga as “the world’s tallest” – patrol warehouse aisles, capturing data with a battery of sensors. The company doesn’t move goods. It provides information, she explains:

Our robots are there to collect the data, but they’re not touching or moving anything. The data, in itself, is the actual product that we offer our customers.

From companion robots to supply chains

Dexory’s founders came to logistics by a circuitous route. For a decade they had worked together on robotics projects, including domestic companions for elderly care. The pandemic disrupted that trajectory and created new demand. Jinga continues:

About two and a half years ago, in 2023, we pivoted into logistics after actually quite a strong pull from the industry post-pandemic. Everyone was very keen to digitize as much as possible, automate, not rely so much on human resources, especially if another black swan event comes our way.

That pivot placed Dexory in an industry often regarded as conservative. But, as Jinga pointed out, COVID-19 accelerated a mindset shift: 

After the pandemic, there’s been a massive shift in mindset around: we need more digital tools. We need to adopt technology. We need to kind of use it to grow.

The visibility problem

The central challenge Dexory addresses is visibility. At the point of entry and exit, stock movements are typically scanned. Inside the warehouse, chaos can creep in. Jinga elaborates:

The bigger the volume of the warehouse, or bigger the velocity of goods, also the bigger the value, there’s a high chance that things get quite messy inside.

The robots tackle that by scanning up to 10,000 pallet locations an hour, producing a wall-to-wall inventory in hours rather than weeks. Instead of relying on handheld scanners, Dexory’s system captures barcodes, quantities, conditions, and high-resolution images, alongside three-dimensional Light Detection and Ranging (LiDAR) data that identifies dimensions and potential safety issues such as toppling pallets. Jinga observes:

The biggest surprise that [customers] have is, first of all, the amount of data and information they can get so quickly.

 In one case, a client discovered stock worth more than $1 million that had been written off.

Data as shock therapy

That initial discovery phase can be uncomfortable, Jinga notes:

We always say… the first weeks or even months will be actually more intense than you were before. Because it’s going to highlight a lot of issues that you hadn’t thought about.

But once corrected, the payoff is efficiency. Errors are caught before they cascade into production stoppages or missed deliveries. Safety risks are flagged before they become accidents. The data also feeds directly into Warehouse Management Systems (WMS) via APIs, minimizing integration pain.

The technology works without any floor markers, Jinga says:

The robots are truly, truly autonomous. We don’t put any beacons or mines on the floor or anything else like that.

Dexory’s approach relies on a confluence of hardware cost curves and software sophistication. A decade ago, a LiDAR sensor might cost £20,000. Today, high-resolution models are available for a few hundred pounds. “What they can do and how they see the world through the lidars is so much more sophisticated and closer to reality,” Jinga explained. That richer input allows Dexory to layer on computer vision, barcode recognition, and analytics at scale.

The result is a 'digital twin' of the warehouse – a constantly refreshed, navigable representation of stock and its condition.

Real-world use cases

Two live examples illustrate the impact. In the automotive sector, Stellantis uses Dexory’s technology to coordinate parts across multiple US plants, now scaling into Europe. Jinga explains:

By having the visibility that we offer and exactly what’s in stock in every one of these locations, you can immediately react if anything is low on stock.

Before implementation, last-minute shortages forced the manufacturer to charter private planes to move components.

For logistics provider GXO, the benefit lies in managing third-party client contracts across multiple sites. With live stock data, GXO can prove KPIs to customers, negotiate space rates, and avoid delays that previously required manual checks and days of communication.

Jinga described the cultural shift that follows: 

Initially, a lot of the 3PLs [third-party logistics providers] were quite reticent. They were like, ‘I’m not sure I want to open my data to my customer too much.’ But they managed to use our system to really clean things up and have that level of accuracy expected by the customer. Now they have very transparent conversation with their own customers.

Dexory positions itself not as heavy infrastructure but as a plug-and-play subscription model. Installation can take as little as four days. That changes the calculation for companies hesitant about capital-intensive automation.

“People think it’s too complicated, it’s too late, there’s going to be quite a hassle,” Jinga said. She continues:

But… most of the systems are a lot more future proof, and have this capability of exchanging information quite easily.

My take

Dexory’s story sits at the crossroads of two industrial shifts. First, the drop in sensor costs has enabled data capture at a speed that would have been unimaginable a decade ago. Second, logistics providers are under mounting pressure to do more with fewer people, less space, and tighter environmental constraints.

The company’s approach is telling because it sidesteps the heavy engineering of conveyors or robotic arms and focuses on intelligence. That lowers barriers for adoption while promising fast payback through error reduction and transparency. The risk, as with any data-driven solution, may be information overload. Customers suddenly confronted with their own inefficiencies may struggle to prioritise action. Dexory’s success will depend as much on guiding change management as on technical accuracy.

The shift from manual checks to autonomous visibility feels less like an incremental step and more like a reset of expectations. In warehouses long accustomed to working around blind spots, Dexory’s robots are teaching operators how to see.