Logistics technology is entering a new phase. After years of focusing on shipment visibility, platforms are increasingly being designed to act on the information they collect.
That is the proposition behind LSP44, the AI-focused business founded by project44 CEO Jett McCandless. The platform is aimed at logistics service providers, including freight forwarders, third-party logistics companies, warehouses and US freight brokers. It connects their existing systems and workflows with project44’s carrier network and logistics data.
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The technology is intended to bridge a major weakness in fragmented transport networks: carriers use different systems and many still rely on email or telephone communication. LSP44 says its agents can work through APIs where machine-to-machine connectivity exists, while using voice or messaging when information must be obtained from a person.
One example involves an invalid truck or licence-plate number. Instead of leaving an exception unresolved, an agent can contact the carrier’s dispatcher, request the correct information and return it to the tracking system. Visibility can then be restored without requiring an employee to make every routine call.
The same model could support risk management. GPS data can be compared with route deviations and known risk areas, with suspicious movements escalated to a human operator. The system performs monitoring and communication; people retain responsibility for judgment and decisions.
Data quality remains decisive
The platform’s stated network covers 282,000 carriers in 194 countries. Its usefulness, however, depends on the quality and context of the underlying information. McCandless says project44 has invested more than $1 billion in connectivity and research and development to normalise carrier data and build a logistics data graph.
For industrial companies, the practical value is potentially significant: fewer manual follow-ups, faster exception handling and more time for teams to manage capacity, customers and network performance. Examples cited include CEVA Logistics’ use of the technology to find trucks more efficiently and Alcon’s reported reduction of inventory by approximately $150 million through improved lead-time forecasting.
Implementation will not be purely technical. Legacy applications, third-party tools, data governance and employee adoption all shape the outcome. AI may increase the scale and speed of logistics operations, but its industrial impact will depend on whether organisations can connect the technology to trusted data and clearly defined human decisions.
