Every enterprise is buying AI. Almost none are getting the value. In high-stakes supply chain operations, where the cost of a wrong call is measured in downtime and missed appointments, reliability is the difference between AI you watch and AI you can act on.

Reliability keeps hitting the same three walls: predictions you can’t fully trust, training data that isn’t objective, and signal scattered across every carrier, mode, and region.

The reframe: reliability is a data problem before it’s a model problem. You can’t build reliable systems on partial, self-reported, or biased data. Solve the data problem, and reliability follows.

project44 aggregates context at network scale, then applies disciplined method (ingest, normalize, validate, learn) to turn it into trusted signal. That signal fuels a world model, and a world model is what lets agents predict, plan, and act, inside the guardrails you set.

The result is a reliable system of work: proactive ETAs the system trusts enough to catch exceptions early and resolve them on its own. An ETA slips past a dock appointment, and the agent rebooks the slot automatically, before anyone would have noticed.

On July 30, see a live showcase of AI agents in action, including: 

• Mo, project44’s AI analyst, answer real supply chain questions in plain language, grounded in the world’s largest logistics data graph.
• The Agentic Workflow Manager close carrier data gaps and run the follow-up work itself, inside rules you set and can audit.
• Dynamic Slot Booking Agent catch an at-risk shipment and rebook the dock slot automatically, before the yard feels it.
• AI Freight Procurement Agent detect a lane at risk, triggering an autonomous mini-bid within your guardrails