For decades, investors valued logistics companies by tangible assets: fleet size, warehouse capacity, shipping volumes. But the industry is investing more and more in technology. Precedence Research projects the global market for AI logistics solutions will reach $707.7 billion by 2034. As competitiveness depends more on algorithms than trucks, so does valuation. A clear example is Rx2Go, a leading medical-delivery company on the US East Coast whose valuation grew from under $1 million to more than $600 million. We spoke with its CTO about why technological architecture has become a logistics business’s primary asset.
Technology as the Main Growth Driver
Rx2Go’s valuation grew several hundredfold during Dmitry’s tenure, driven, he says, by technology. Early on, Rx2Go was in chaos: it knew it could make money in its niche but not yet how. The solution was a proprietary system built for its use case — starting with dashboards for clients, drivers, and dispatchers, then expanding to finance, quality control, equipment tracking, and strict verification scenarios: a driver couldn’t close a delivery until every step was done, nor ship an order until it passed the full verification chain.
Over time the stack absorbed the operational minimum, letting the company run self-sufficiently, handle its volume with ease, and expand. It then had about 30 drivers and four administrative staff; today, with a fleet of 2,500 drivers, the administrative team still numbers no more than 30. “For us, being technological became synonymous with being efficient in practice. The more you invest in it, the more efficient you become,” says Dmitry Chistyakov.
Responsibility to Hospitals
The platform’s clients include some of the country’s largest healthcare systems: NewYork-Presbyterian Hospital, Mount Sinai Hospital, and Northwell Health. Healthcare is a demanding US niche: people’s health is at stake, which means enormous responsibility. Working with major hospitals, the team found monitoring reveals only part of a problem. When something broke, they had to trace cause and effect, filter false leads, find the real cause, and fix it without disrupting operations. “In the past, we handled errors exactly that way: manually, step by step, and it could take over an hour from the moment a crisis hit to the moment it was resolved,” Dmitry recalls. Later they brought AI in to identify root causes, suggest solutions, implement fixes, and manage the aftermath — and response time dropped to seconds.
“What really damages a system isn’t the error itself so much as how far its consequences spread. We added an AI layer to the early stages of CPLOM: the algorithm watches what’s happening and builds a forecast several hours ahead, so it can catch a problem before it fully develops.”
180 Seconds Per Delivery
What Rx2Go does is known as last-mile service — a term that, Chistyakov explains, is now less about the supply chain than about effectively fixing problems at its final stage. Every day, the company receives packages from many locations that must reach set destinations within a set time window and method. It doesn’t control where a package originates — lab, airport, port, warehouse, pharmacy, or hospital — but fully controls everything afterward.
Though most orders are individual, Rx2Go manages them in groups, continuously simulating the optimal route mapping for each concurrent batch. Chistyakov calls forming these groups correctly the key to success: they must be assembled accurately, dispatched to the right drivers, and timed to each address while accounting for traffic, delays, and weather — impossible by hand. CPLOM gauges current resources, anticipates in-transit conditions, and builds the optimal groups and routes.
The next challenge is executing delivery correctly. With more than 100 orders per driver per day, each has just 180 seconds per delivery and no room for error. “In essence, there’s a direct proportion here between the quality of the technological solution and the scale of the business,” Chistyakov sums up.
Regulatory Barriers and an Open Methodology
Complying with regulations across 16 states, and automating that compliance, is both a market complexity and a barrier to entry. Breaking in at scale demands costly safety certifications, office, warehouse, and server infrastructure, guaranteed SLA standards, and reserves to cover lost packages. But even then, the core challenge is operations: you need excellent software and know how to use it. “Even if AI helps competitors build the software, it definitely won’t give them the vision,” Chistyakov notes. Rx2Go also remains the only company delivering under a full-refund guarantee for loss.
Notably, Chistyakov set out his CPLOM approach in a technical white paper and made it public — a move that might look like a gift to competitors. He sees it differently: the team helps doctors fight for the health and lives of millions of Americans, and hopes to scale to every state. “And for that to happen, the industry needs to grow and develop alongside us. If my experience proves useful to someone, I’ll take it as a compliment and be glad of it,” Dmitry shares.
Who Survives in 3 to 5 Years
Beyond Rx2Go, Chistyakov judges international technology competitions and accelerators, including Starta Ventures, and in 2025 joined the Canadian cybersecurity delegation at the RSA Conference at the Canadian government’s invitation. He sums up the industry’s direction: within 3 to 5 years, logistics companies that haven’t adopted AI at some level are unlikely to stay afloat. Widespread adoption is inevitable across every industry, logistics included. “Beyond the question of survival itself, everything will come down to the degree of technological maturity. Companies with more advanced foundations will be able to offer lower prices, stronger guarantees, higher quality, and faster service. Behind them will come companies relying on the talent of their developers, and everyone else will bring up the rear,” the expert concludes.
