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How Dexory Cracked Enterprise Messaging for Warehouse Robotics After Years Building the Wrong Product

The autonomous robot worked perfectly. The scanning technology was breakthrough. The data capture was unprecedented. And customers kept asking: “But what does it actually do?”

Andrei Danescu, founder and CEO of Dexory, spent years building autonomous robots for public spaces before realizing he was solving the wrong problem. In a recent episode of BUILDERS, Andrei unpacked how Dexory pivoted to warehouse data intelligence, raised over $280 million including a preemptive Series C, and cracked the messaging that unlocked enterprise sales.

The journey reveals frameworks for positioning category-creating technology, structuring early customer collaborations, and maintaining commercial velocity in capital-intensive businesses.

The Public Spaces Detour: When Technical Feasibility Masks Strategic Misalignment

Before becoming a warehouse operating system, Dexory built autonomous robots for airports and public spaces. The robots could map environments, track footfall, and generate insights about space utilization. The technology worked. The market didn’t materialize.

“One of the first applications for autonomous robots that would collect data was for public spaces and airports,” Andrei explains. “We thought it was a great stepping stone and great idea, but it wasn’t.”

The detour provided underlying technology development but at massive opportunity cost. “It would have been a lot better to focus on the bigger problem early on and commit to it,” Andrei admits.

For hardware founders, this carries particular weight. Software pivots burn runway. Hardware pivots burn runway plus tooling, manufacturing relationships, and go-to-market learning that doesn’t transfer. The lesson: technical feasibility proves you can build something, not that you should.

From “World’s Tallest Robot” to “You Can See Every Pallet”: The Messaging Breakthrough

After committing to warehouse logistics, Dexory had genuinely differentiated technology. They built the world’s tallest autonomous robot. It could scan over 10,000 pallets per hour. These weren’t marketing claims—they were engineering achievements.

And none of it resonated with buyers.

“If we started talking about our amazing technology and talking about how we have the world’s tallest autonomous robots, yes, we can scan over 10,000 pallets an hour and so on, this gets lost,” Andrei says. “People look at it and say, this is a great piece of technology, but what does it actually do?”

The breakthrough came from flipping the conversation. Instead of leading with autonomous robot capabilities, Dexory led with a problem every warehouse operator immediately understood: inventory visibility and stock control.

“It’s very easy for people to understand. Okay, you use this robot, it’s got a bunch of cameras, you can take pictures of the rack, process the information, and you can see every single pallet in the warehouse,” Andrei explains.

Same robot. Same scanning rate. Completely different entry point that mapped to existing budget categories and operational pain points rather than requiring buyers to understand a new technology category first.

The tactical insight: for category-creating products, buyers need to map your solution to known problems before they can evaluate technical superiority. Lead with the problem they’re already trying to solve, then layer in how your approach differs.

Zero Infrastructure Change as Product Constraint and Competitive Moat

One product decision shaped everything else at Dexory: their robots had to work in existing warehouse environments with zero facility modifications.

This wasn’t a feature—it was a fundamental constraint. “We said, look, we need to go into these warehouses, logistics centers and these people cannot stop their operation. These are live businesses,” Andrei explains. “They don’t have the luxury to say, okay, we can pause for a few months and change all the infrastructure.”

The constraint forced architectural decisions. Autonomous navigation in complex, dynamic environments with people and machinery. Operation in variable lighting. No fixed infrastructure requirements beyond space allocation and power.

“We can bring this technology into any one of our customers, any one of the warehouses with zero infrastructure changes. They do not need to change their environment, they do not need to change the way they do things,” Andrei says.

This removed the deployment friction that kills many robotics pilots. No facility engineering reviews. No operational pauses. No multi-month integration timelines that give buyers reason to delay.

The broader framework: study your customers’ operational constraints as carefully as their technical requirements. The best product decisions often come from designing around what customers cannot change rather than what they say they want.

Structuring First Customer Relationships for Continuous Value Expansion

Dexory’s first customer faced a significant problem and committed to finding a solution—crucial prerequisites for any early pilot. But the relationship structure they established matters more than the initial sale.

“We really wanted to understand what you need, what you want. How can we best adapt our technology to fit your needs?” Andrei explains. “This is the first customer you have, right? So you will literally bend over backwards for them.”

More strategically, Dexory built infrastructure from day one for continuous value delivery rather than one-time implementation. “We’ve laid down the foundation in the infrastructure from the beginning to have a way of continuously improving the product,” Andrei notes. “Creating this mechanism that gives you the ability to increase, incrementally increase the value that you bring to your customers.”

This transformed the relationship dynamic. Early customers didn’t just reference Dexory—they became advocates. “New people looked at it as a point of pride. They were very proud of the technology. They’re very proud of the value they can derive from it,” Andrei says.

The implementation framework: structure early customer agreements and product architecture to support continuous delivery cycles. Build mechanisms for customers to influence roadmap. Create regular touchpoints where you deliver incremental value increases. The goal is compounding advocacy, not a transactional reference.

Geographic Expansion Through Multinational Customer Networks

UK-based company. European customers. US expansion target. The default playbook: open regional offices, hire local sales teams, build market presence in each geography.

Dexory’s approach: target multinational companies where one regional deployment generates cross-regional interest through internal networks.

“We looked at the global companies that we can actually start working with. So internally it makes it a lot easier for them to pick up the phone and call, you know, the US calls their counterparts in Europe and vice versa,” Andrei explains.

The reference strength compounds because the validation comes from inside the same organization. “That reference is a lot stronger because they’re already part of the same company,” Andrei notes.

This enabled UK-to-Europe-to-US expansion without proportional overhead investment. For capital-intensive businesses where every operational dollar matters, the leverage multiplies—one customer deployment generates pipeline across multiple geographies through their internal networks.

The strategic principle: identify customers with multi-region operations who can accelerate your geographic reach through internal advocacy. The trust transfer happens faster within organizational boundaries than across them.

The Series B Velocity Trap: When Raising Capital Tempts You to Slow Down

After raising their Series B, Dexory faced the temptation most well-funded startups encounter: the urge to ease off commercial acceleration and invest more in product development.

Andrei identifies this explicitly as strategic error. “It’s easy to say, okay, we raised our B, it’s 50 million, now we can actually take our foot off the gas a little bit. We want to invest a bit more in product. It’s not as important and that’s that. I think that is wrong.”

The alternative: treat post-raise periods as opportunities to demonstrate what capital efficiency looks like at your stage. “You always need to stay very close to customers, but you always need to have the North Star and the big focus is to drive that commercial adoption.”

For Dexory, this discipline created their Series C opportunity. “For us, this was a preemptive round, so we weren’t out in the market fundraising,” Andrei explains. Investors consistently found the company ahead of projections: “They would literally come back to us a month later and we’re already ahead of plan while we had told them.”

For full-stack businesses where capital takes longer to convert to revenue, consistent over-delivery on growth targets becomes the primary signal of execution capability. The framework: use each funding round to prove you can accelerate proportionally to capital deployed, not just maintain current velocity.

Timing as Strategy: AI Adoption, Sensor Economics, and Industry Data Appetite

Dexory bootstrapped for five to six years before raising venture capital. That timing wasn’t just about building product—it was about waiting for market conditions to align.

Three trends converged to accelerate Dexory’s market entry: logistics industry appetite for data-driven operations increased, AI advances enabled deeper insights from massive datasets, and sensor and edge computing costs declined.

“A combination of the industry, the wider industry, being a lot more excited about visibility, a lot more excited about being data driven, coupled with the fact that AI has become so much more popular,” Andrei notes, alongside “the cost for sensors, the cost for computing power and what you can actually do on the edge” helped “accelerate and propel our product and our technology a lot faster into the market.”

This convergence transformed what used to take “weeks to be able to actually understand how the space utilization is in the warehouse” into something that “can now take minutes,” as Andrei describes.

The strategic insight: sometimes early market entry isn’t a moat—it’s just expensive customer education. For deep tech businesses, timing around technology cost curves and adjacent technology adoption can matter as much as product readiness.

Building Toward Self-Optimizing Supply Chains

Dexory’s long-term vision extends beyond autonomous robots or data intelligence to a complete warehouse operating system. “Our drive is to transform this industry to have a much more comprehensive and continuously optimizing supply chain,” Andrei explains. “Leveraging AI, leveraging the latest developments, you can actually create a system that autonomously looks at improving itself.”

The entry point was inventory visibility. The platform vision is comprehensive warehouse operations. The ultimate ambition is self-optimizing supply chain infrastructure.

For founders building category-creating technology, Dexory’s progression offers a clear framework: solve an immediate, recognized problem as your entry point, architect your product to expand toward adjacent problems, but position for a larger systems-level opportunity as the market matures and your platform capabilities compound.