OneRail Launches NVIDIA-Powered AI Platform to Make Delivery Decisions Up to 10x Faster

 

AI is no longer just deciding what you see online. It is increasingly deciding how products physically reach your doorstep.

Logistics technology company OneRail has launched OmniSTAR, an AI-powered delivery decisioning platform built with NVIDIA technology that helps retailers, wholesalers, and distributors determine the most efficient way to deliver individual orders.

Instead of automatically assigning an order to one delivery method, OmniSTAR can compare multiple options, including a company's own fleet, couriers, parcel carriers, and other transportation modes.

The system then selects the lowest-cost delivery option that can still meet the required service level.

OneRail says the technology could allow companies to make delivery decisions in minutes rather than relying on static rules, manual planning, or calculations performed long before an order actually needs to move.

NVIDIA GPUs Are Doing the Heavy Lifting

At the center of OmniSTAR is NVIDIA's accelerated computing technology.

The platform uses NVIDIA cuOpt, a GPU-accelerated optimization engine designed for complex problems such as vehicle routing and mathematical optimization.

It also uses NVIDIA cuDF, which accelerates the processing of large tabular datasets on GPUs.

OneRail combines those NVIDIA technologies with its own information on delivery pricing, transportation performance, operational constraints, and millions of previous deliveries.

The result is essentially an AI-powered decision layer capable of asking:

Which vehicle should carry this order?

Which carrier should handle it?

What route should it take?

How much will it cost?

And can it still arrive when the customer expects it?

OneRail says OmniSTAR can evaluate those variables significantly faster than traditional approaches.

From 20 Minutes to Around Two Minutes

OneRail claims the platform can make some optimization calculations up to ten times faster.

According to the company, a logistics problem that previously required around 20 minutes can now be processed in roughly two minutes or less, depending on the workload.

OneRail also said some much larger computations that previously took approximately a week could be reduced to around two days.

That difference matters because logistics does not stand still while computers calculate.

Traffic changes.

Drivers become unavailable.

Fuel costs move.

Weather conditions deteriorate.

New orders arrive.

Customers suddenly need priority deliveries.

When optimization takes too long, companies are forced to make decisions based on information that may already be outdated.

Faster computation allows the system to rerun scenarios while the delivery operation is actually happening.

AI Predicts. Optimization Decides.

There is also an important distinction between AI prediction and optimization.

OneRail uses machine-learning systems to estimate factors such as expected delivery time, lateness risk, delivery success, and price.

But predicting what might happen is only half the problem.

The company then uses optimization technology to determine what the logistics system should actually do.

For example, an AI system might predict that Route A is likely to experience heavy traffic.

The optimization system can then calculate whether changing the route, vehicle, carrier, or delivery method produces a better result.

That distinction could become increasingly important as AI moves from simply generating information to making operational decisions inside businesses.

How NVIDIA cuOpt Works

NVIDIA's cuOpt technology is designed to solve complex routing and optimization problems involving thousands of variables and constraints.

It can consider factors such as vehicle capacity, transportation costs, travel times, operating windows, vehicle types, and delivery requirements.

It does not simply calculate every imaginable route one by one.

Instead, the system searches for high-quality solutions using GPU-accelerated optimization techniques.

NVIDIA documentation also explains that cuOpt itself is stateless. When conditions change, such as a vehicle breakdown, driver absence, road closure, traffic problem, or new priority order, the optimization problem can be rebuilt with the updated information and solved again.

That capability is particularly useful for last-mile delivery, where conditions can change constantly.

Millions of Deliveries Become Training Data

OneRail says its system draws from operational data accumulated across millions of deliveries.

Its network reportedly includes more than 12 million drivers and over 1,000 logistics partners.

The company uses pricing and performance information from those delivery operations to compare transportation methods and identify decisions that could unnecessarily reduce profitability.

This is where AI becomes particularly valuable.

A human logistics manager may be able to compare several delivery options.

An AI optimization system can potentially compare thousands or millions of combinations involving carriers, routes, vehicles, prices, service requirements, and operational constraints.

And it can do it repeatedly.

US Foods Already Using OmniSTAR

OmniSTAR is already being used by selected enterprise customers.

OneRail identified US Foods as one example.

According to OneRail, the system discovered delivery configurations that were reducing margins, including situations where relatively low-margin products were being transported long distances using more expensive equipment.

US Foods used those findings to adjust pricing and restructure some delivery patterns, the company said.

OneRail also told CNBC that an unnamed large tire distributor achieved approximately $40 million in reported run-rate savings over three years.

That figure came from OneRail and the customer was not publicly identified, so it should be viewed as a company-reported performance claim rather than an independently verified result.

OneRail also expects OmniSTAR to handle more than $6 billion in gross merchandise volume during the fourth quarter of 2026, another forward-looking projection from the company.

Three Years With NVIDIA

The technology was not created overnight.

OneRail said it had been working with NVIDIA on the project for approximately three years, including collaboration involving NVIDIA's cuOpt engineering capabilities and participation in the NVIDIA Inception program.

The goal was to move logistics optimization away from static rules and toward a system capable of making decisions as conditions change.

OneRail CEO Bill Catania summarized the economic reason behind the technology simply: slow decisions in last-mile delivery can mean lost margins.

And last-mile fulfillment remains one of the most expensive and complicated portions of the entire e-commerce supply chain.

FedEx Is Already Working With OneRail

OneRail's growing role in logistics became even more significant earlier this year.

On March 24, 2026, FedEx launched FedEx SameDay Local in collaboration with OneRail.

The service gives participating businesses access to two-hour and end-of-day delivery options through a network of more than 1,000 delivery providers.

Orders can be automatically matched with vehicles and drivers while customers receive near-real-time tracking from pickup to delivery.

The partnership places OneRail inside a growing competition involving FedEx, Amazon, Walmart, Target, and other companies trying to make same-day delivery faster and more economical.

AI Is Moving From Chatbots to the Physical Economy

This story is important because it shows another direction artificial intelligence is taking.

When most people hear "AI," they still think of ChatGPT, image generators, AI videos, or virtual assistants.

But some of AI's biggest economic effects may happen quietly behind the scenes.

AI will decide:

Which warehouse should fulfill your order.

Which truck should carry it.

Which driver should deliver it.

Which route should be taken.

How much the delivery should cost.

And whether that delivery remains profitable.

This is AI moving from the screen into the physical economy.

What This Could Mean for the Philippines

For the Philippines, technologies like this deserve attention.

We are an archipelagic country where logistics remains complicated by distance, traffic congestion, inter-island transportation, fragmented delivery networks, and large differences between Metro Manila and provincial infrastructure.

Imagine AI optimization connecting:

local warehouses,

motorcycle couriers,

trucking companies,

provincial distribution centers,

ports,

air freight,

and last-mile delivery partners.

Instead of using one fixed delivery method for every order, an intelligent system could continuously calculate the best combination based on cost, location, urgency, traffic, weather, vehicle availability, and customer expectations.

That could eventually matter not only to major retailers but also to Filipino SMEs and online sellers.

Because the future of AI is not only about machines learning how to talk.

It is also about machines learning how to decide.

And increasingly, those decisions will determine how the real world moves.