AI is rapidly moving into the mainstream of the logistics technology arena. Software vendors are increasingly touting AI-enabled capabilities aimed at improving efficiency and automating decision-making across a wide variety of applications ranging from transportation management systems and shipment visibility platforms to customs compliance tools.
But according to Gnosis Freight, many of these solutions fail to deliver the expected results for one simple reason: the data behind them is not reliable enough.
Michael Rentz, Chief Revenue Officer at Gnosis Freight, believes AI can only perform as well as the information it receives.
“AI does not create accuracy, it amplifies whatever you feed it,” Rentz said. “If the underlying container data is incomplete, delayed, or conflicting, the AI does not just fail quietly. It confidently makes the wrong call, automates the wrong action, and scales the mistake across your entire operation.”
Founded in 2017 by Austin McCombs, Gnosis Freight initially focused on container tracking. As the platform evolved, the company realized that building accurate, real-time logistics data infrastructure was a far bigger challenge—and one that has become even more important as AI adoption accelerates.
“We started out focused on building great software,” Rentz explained. “What we learned is that the data infrastructure surrounding the container lifecycle has to be solved first. Without that foundation, no downstream outcome, whether it’s operational efficiency, automation, or AI execution, can be fully realized.”
To address that challenge, Gnosis developed a container tracking platform designed to create a single, validated, real-time record of every milestone in a container’s journey, from booking through its empty return.
Rentz argues that many companies are trying to deploy AI before solving their underlying data issues.
“Most companies are skipping the foundation and going straight to the model,” he said. “That’s why so many AI pilots in supply chain look great in a demo and fall apart in production.”
According to Gnosis Freight, one of the industry’s biggest obstacles is fragmented information. Data is often spread across carrier portals, spreadsheets, freight forwarder emails and legacy transportation management systems, making it difficult to establish a single, trusted source of truth.
Rather than simply collecting more information, Rentz says companies need operational-grade data—a continuously validated, real-time record that every team and every system can rely on.
Without that foundation, problems often appear in subtle but costly ways. Surprise demurrage fees, wrong arrival estimates or automated workflows based on stale information can erode trust in technology, driving teams back to manual processes over time.
To improve data quality, Gnosis Freight gathers information directly from ocean carriers, ports, terminals, Class I railroads, AIS satellite services and U.S. Customs instead of relying primarily on third-party data aggregators.
The company also applies a validation process that compares conflicting data sources and resolves discrepancies before the information reaches customers.
“Raw ingestion is only half of it,” Rentz said. “The validation layer is where the real work happens.”
Beyond technology, Gnosis Freight places significant emphasis on understanding customers’ day-to-day operations. Its engineering teams work closely with shippers and logistics partners to capture operational knowledge that cannot be obtained through software integrations alone.
According to Rentz, this combination of direct data access and operational expertise allows the company to continuously improve the accuracy, completeness and reliability of its platform instead of treating accuracy as a fixed number.
That foundation also supports predictive capabilities, including more accurate estimated arrival times based on real operational data rather than relying solely on carrier updates.
For Gnosis Freight, the value of AI ultimately comes down to measurable business results rather than technology itself.
The company says its platform helps customers reduce demurrage and detention costs, automate delivery orders, improve drayage scheduling, process arrival notices, capture equipment interchange receipts, and audit freight invoices using verified operational data.
According to Gnosis Freight, one of its top 50 U.S. importer customers saved more than $12 million in demurrage and detention costs within 12 months of using the platform. Across its customer base, the company reports an average 81% reduction in demurrage charges and a 64% reduction in detention charges during the first year.
Rentz believes these results are possible because AI is built on trusted operational infrastructure rather than disconnected data sources.
“AI isn’t the hard part,” he said. “The hard part is building the infrastructure that makes AI trustworthy enough to act on.”
Looking ahead, Gnosis Freight encourages shippers to look beyond AI marketing claims when evaluating technology providers. Instead, companies should understand where a vendor’s data originates, how conflicting information is resolved, how accuracy is measured and whether the provider continuously improves the data through customer feedback and operational expertise.
Rentz argues that vendors unable to clearly answer those questions may be selling AI capabilities without the infrastructure needed to support them.
Backed by a strategic growth investment from Vista Equity Partners in 2024 and recently named to the FreightTech 100 for 2026, Gnosis Freight believes the future of AI in logistics will depend less on the sophistication of algorithms and more on the quality of the data infrastructure supporting them.
