An applied AI research lab for logistics.
Operators, engineers, and analysts studying where innovation changes what is possible for drivers, for the companies that move the freight, and for the communities it runs through.
Supply chains run on fragmented data. The hardest problems are not a shortage of trucks, docks, or software, but a shortage of evidence about what actually works. We supply that evidence from inside the operation: we test findings with partner carriers where the work runs, and build the AI systems that act on it.
Long-haul freight, home every night.
Lower cost per mile, higher utilization, coordinated backhauls.
Fewer empty miles and fewer trucks on the same roads.
We run independent research on the operational data operators already hold, and publish what we find. The research stands on its own.
We test findings with partner carriers, on their real lane and dispatch data and alongside their planners and dispatchers, to see whether they hold on the ground.
Not every improvement needs software. When one does, it becomes a product we stand behind. The research and the working system are the deliverable, never a slide deck.
Freight is the first area we quantified, because the numbers are large and nobody had put them together. Here is what that work found:
The same pattern holds everywhere we look: idle capacity, avoidable turnover, and demand mispriced against what the operation can actually do. It is as true of a fulfillment center as it is of a lane network, and in both the losses are well known while the evidence on what fixes them is not. We quantify where the leakage is, problem by problem, then test the fix with the operators who live it and build the systems that close it.
We have run these networks. Anyone can analyze one from the outside; we have carried the pager for fulfillment and freight operations at national scale, and it shows in what we choose to measure.
We do not sell vague efficiency. We quantify the liability and the recovery in numbers a CFO or an investment committee can act on, built from benchmarks anyone can audit.
The answers are sitting in your TMS, ELD, and HR systems. Nothing to install, nothing to rip out, and no year-long integration before the first finding.
Deadhead, HOS, length of haul, lane density. We arrive as peers who have run the operation, not outsiders learning it on your time.
Four disciplines in one small team, which is why the work moves from analysis to the floor without a handoff gap.
Running large-scale logistics and fulfillment: network and lane design, dock and yard flow, frontline scheduling, and the cost models underneath all three.
Turning operational exhaust from TMS, ELD, WMS, and HR systems into forecasting, optimization, and decision models that hold up against a real operation.
Distributed systems, data pipelines, and analytics products built to enterprise reliability standards, so a finding can become software people depend on.
Complex enterprise deals, operational due diligence, and value creation planning, framing every finding in terms an investment committee can act on.
The research stands on its own and we publish it openly. Findings come before any product we might sell.
We combine logistics expertise with data science to understand the problem before we build anything.
We work from inside the operation, not above it. Our team sits with planners, dispatchers, and floor leads, and every claim is tested against a real operation.
We have run these networks ourselves. You start with a bounded study, not a statement of work, and what you get back is a decision you can act on rather than a deck.
Work with us
Bring us a network, a dataset, or a question. We are looking for regional carriers on 550–600 mile lanes to test coordinated relays with us. Software follows when the evidence earns it.
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