Project44 launches Autopilot, an AI-enabled logistics operating system that offers infinite labor

Project44 has launched Autopilot, a no-code platform that lets shippers, brokers and 3PLs deploy AI agents across their supply chain workflows without writing prompts, building integrations or hiring an engineering team to wire it up. The product is the culmination of more than 18 months of agent deployment across the project44 network.

According to the company, that work has already produced a 4% reduction in freight spend, a 70% reduction in manual coordination, sourcing cycles up to 75% faster and as much as a 40% reduction in disruption-related costs. But Autopilot is more than another feature release. It is project44’s bet on what the next decade of supply chain software looks like.

It is also a direct challenge to the wave of agentic AI startups that have raised hundreds of millions over the past two years to automate the very workflows project44 is now automating itself. (A screenshot of project44’s Autopilot. (Image: project44) window. googletag = window. googletag || {cmd: []}; googletag. cmd. push(function() {googletag.

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push(function() {googletag. display('div-gpt-ad-1709668545404-0'); }); What Autopilot actually is Autopilot gives users a visual workflow canvas where AI agents respond to real-time logistics signals (a late shipment, a missing PRO number, a container dwelling too long at the port of discharge) and act on them autonomously.

Customers configure the triggers, set conditional branching by carrier or lane, define escalation paths and choose post-agent actions like notifications, task creation or routing work to a human operator.

A draft-and-publish model lets teams iterate on workflow configurations without disrupting live operations, and every agent action is logged and auditable through project44’s Movement Collaboration Center.

The pre-built library covers about 40 workflows today, including collecting truck milestones, late shipment reason codes, missing PRO numbers, and carrier outreach for ETA confirmation. Project44 says it is shipping two to three new ones a week.

Administrators can toggle any of them on with a click and tailor the steps to fit their own escalation paths and notification preferences. No prompt engineering. No TMS integration project. No data normalization. Eastman Chemical Co.

, an early customer, said in the launch announcement that the platform let its team expand into APAC and onboard less-technical carriers without adding operational complexity. “The right work reaches the right people without manual intervention,” said Josh Moss, Eastman’s process lead for global supply chain.

Signal, trigger, action In a conversation previewing the launch, founder and CEO Jett McCandless walked me through what he called the signal-trigger-action evolution of project44’s platform, and by extension of supply chain visibility itself. “Signal” is the network.

It’s the data graph project44 has spent more than a decade building, which now connects 259,000 carriers and processes 1. 5 billion shipments a year across 186 countries and territories, ingesting more than 700 million logistics events a day. McCandless calls it the world’s largest synchronous logistics data graph, and he is not subtle about what it cost.

“It costs me a billion dollars to build this layer here,” McCandless said. “Maybe even more, but that’s the context that we need for everything.” “Trigger” is what project44 layered on top during the COVID era: an exception engine, released around 2022, that turned the firehose of raw shipment data into prioritized work items.

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pubads(). enableSingleRequest(); googletag. pubads(). collapseEmptyDivs(); googletag. enableServices(); }); googletag. cmd. push(function() {googletag. display('div-gpt-ad-1665767553440-0'); }); McCandless’ framing captures something most supply chain software companies dance around: visibility didn’t solve the underlying problem. It exposed it.

“When we turn the lights on, what came back was chaos,” he said. “Right now you can see everything that’s going on. But how do you separate noise from signal? How do you know what the actual exceptions are?” By the time exception engines arrived, customers had a different problem: they could see what was wrong, but they couldn’t process it fast enough.

“Humans don’t have the ability to process as much as that’s there,” McCandless said. “So you’d