Dmitry Chistyakov is a systems architect, researcher, and international AI expert. He serves as a mentor and auditor at technology conferences, and is the author of a new approach to AI in logistics — the CPLOM predictive management framework. Dmitry shares what businesses actually expect from AI in logistics, and whether delivery will ever become free for the customer.
In today’s technology race, everyone uses AI — regardless of business size or industry. But having a powerful algorithm is only half the battle. To operate without disruption and break into global markets, a company needs a well-coordinated architecture. That’s exactly the kind of system built by leading AI architecture expert and Rx2Go CTO Dmitry Chistyakov.
His CPLOM framework makes decisions autonomously and cross-checks them through multi-agent validation. In this interview, he explains how to turn AI into a predictable management tool, what “swarm behavior” means, and why the future of technology depends on system transparency — not on the number of AI agents.
– Dmitry, you created the CPLOM architecture, which helped Rx2Go achieve 99.99% order fulfillment accuracy. Yet in a field as sensitive as medical logistics, it’s still difficult to fully trust a system whose decisions are hard to verify. Your approach is built in part on complete transparency — on making the decision-making process visible. How did you get AI to not just produce a result, but back it up with confidence scores?
– Automation and AI involvement in operations have long been a reality, but the feeling of distrust hasn’t gone away — and that’s completely normal. People are afraid of losing control. With AI, there’s no sense of understanding why a particular decision was made. Trust emerges when a system behaves predictably, explains its reasoning, and allows for human intervention. In practice, we found that trust in AI can only be built through architecture.
Technically, the decision-making logic in CPLOM is a branching tree where every “yes” or “no” is logged. We see the full picture: what the AI was considering, where it followed the “yes” branch, where it went with “no,” and what its Confidence Index was at each point. We then added reasoning annotations at ambiguous junctions so we could better understand what the system lacked in order to make a confident call.
– You’re not only in business — you’re also doing research and publishing papers on decision-making in complex systems. Why have you focused specifically on decision-making architecture?
– For me, moving into research wasn’t driven by curiosity — it was driven by necessity. I’d even call it a survival tool. At some point you run into problems that can’t be solved by yet another optimization or yet another algorithm. You hit fundamental questions: how does a system make decisions, how does it assess risk, how does it behave under uncertainty. If you don’t have a formalized answer to those questions, you start building on intuition. And intuition doesn’t scale well. That’s why I began formalizing what we were doing in practice — and that’s how my work on decision-making architecture emerged.
Over time, it became clear that the problem wasn’t the data or the models — it was how the decision was being made. You can have a perfect forecast and still draw the wrong conclusion. Or you can make a sound decision even with incomplete data. Models can be wrong, but architecture determines whether that leads to a minor correction or a full-blown disaster. In that sense, research is a way not just to build a working system, but to understand why it works — and whether the approach can be taken further.
– Where do these technologies stand today? Could AI be trusted to manage an entire city?
– Today’s models are strong enough to tackle complex problems, but they’re still unstable and prone to hallucination. So we’re currently in the era of “smart models” — but not yet “smart systems.” The former predict; the latter decide.
As for managing a city — theoretically, it’s already possible. But practically, not yet. A city is a system with an enormous number of unknowns and social factors that are difficult to formalize. I’d put it this way: we can already automate individual city subsystems — transportation, logistics, energy. But handing over full control is still premature.
– If such systems become the standard for organizing urban life, how dramatically will logistics change? There’s a common notion that free robots and unemployed delivery workers are coming. Will delivery actually get cheaper?
If these systems become the standard, logistics won’t just change in degree — it will change in kind. Today, logistics is largely a reactive system: something goes wrong and we fix it. With decision-making architectures, it becomes predictive — the system sees problems before they happen and adapts, reshaping the behavior of the entire network.
When people talk about AI replacing humans, you often hear things like: “That’s it — free robots are coming and couriers will be out of work.” In practice, of course, it doesn’t work that way. First, robots are far from free — a good delivery robot can cost several years’ worth of a courier’s salary. Second, people aren’t going anywhere; they’ll simply move to a higher level, overseeing the system.
But the most interesting part isn’t even that. Everyone assumes the future is cheap robots replacing people. In reality, the future is expensive thinking systems that manage agents as a single organism. The real advantage comes from shared context — when every agent knows what the others are doing, where problems are emerging, and how to respond in real time. That’s no longer automation. That’s swarm behavior. In a system like that, you’re not paying for the delivery or the robot — you’re paying for the intelligence that manages the entire network. So yes, delivery will definitely get faster. Whether it gets cheaper is another question entirely.
– When you joined Rx2Go, it was a small startup. Under your technical leadership it reached $70 million in revenue and $600 million in valuation, and is now preparing to expand into Canada and Europe. How adaptable is the architecture you built to different countries with their own regulatory frameworks?
From an architectural standpoint, CPLOM was designed to be universal from the start — because the underlying problems look very similar across countries: uncertainty, constraints, complex interdependencies. But at the same time, every country has its own regulations, its own processes, its own decision-making culture.
So the key isn’t “porting the system” — it’s adapting it. We don’t transfer the rules; we transfer an architecture that knows how to work with rules. And that’s a fundamental distinction. In the US, for instance, the system operates under heavy regulation; in Europe, comparatively less; in Canada, somewhere in between. And the system can’t simply “work” — it has to behave correctly in each of those scenarios.
