FreightWise launches Kuebix AI to unify shipper TMS

By Tom Whitfield

FreightWise announced Kuebix AI on Oct. 20 as the single TMS for every FreightWise and Kuebix customer across LTL, truckload and parcel, with thousands of users already on the platform and legacy SaaS upgrades continuing.

FreightWise CEO Chris Cochran presented the product at the F3: Future of Freight Festival in Chattanooga, a day after demonstrating it. The software targets small and mid-market shippers, and Cochran drew a deliberate line around the buyer: “We’re focused 100% on the shipper,” he said, unlike many exhibitors building for brokers.

The release also ends a two-platform structure. FreightWise acquired Kuebix in 2023 but continued running FreightWise TMS and Kuebix TMS separately. One technology stack, one support team and one roadmap should spread new features faster.

The rebuild starts from FreightWise’s modern native application for managed transportation, embeds AI in core functions and preserves functionality Kuebix users have known for nearly two decades. Upgrades for SaaS-only legacy customers will continue through the coming year.

What changes for multi-stop truckload planning?

Traditional transportation management systems generally start with the cheapest route. They struggle to test that plan against drivers, dock doors, delivery appointments, hours-of-service limits and facility schedules.

Cochran said a multi-stop truckload plan often reached the planner only after the system built it. The planner would discover missed appointments without an explanation or usable alternatives, a problem he traced to optimization logic written years ago. He argued that AI can bypass those legacy algorithms and determine a workable solution on its own.

Kuebix AI puts several route choices on one screen and exposes each option’s weakness:

• One adds miles to preserve a delivery appointment.
• Another cuts miles and cost but requires an appointment change.

Against that trade-off, the planner selects a route and decides whether to reset a customer’s expectation or flag a retailer shipment that could attract fees or damages.

Service-time models incorporate hours of service, waiting, loading, unloading and facility hours. The routing layer then tests whether the stop sequence is feasible and recommends the optimal route, with the stated aim of making customer delivery dates hold.

FreightWise also wants difficult exceptions handled inside the TMS. “Instead of them going away from the TMS when they have a bad route or a false positive, it allows them to do it right there,” Cochran said.

Can integration happen without an IT queue?

A TMS rarely serves as the order system of record, and small shippers often depend on old WMS or ERP feeds with limited IT staff and no current JSON support. Kuebix Connect, a no-code middleware layer, lets a business user visually match fields and map logic between the source system and TMS.

Its embedded AI monitors daily feeds, fixes bad data and diagnoses the source of failures, including an incorrect character that stopped orders from entering the system. IT still supplies credentials and tests security, but it no longer has to make manual calls every time a feed breaks, Cochran said.

That matters because a blocked order can leave freight sitting in a warehouse while the shipment itself remains invisible to the TMS.

What can the dashboards and freight audit show?

Kuebix Intelligence provides AI-backed dashboards for spend by location, carrier and mode, explaining the drivers behind a trend instead of only displaying it. Rating, routing, freight audit and payment now sit in one system rather than splitting settlement data across an outside audit provider, which FreightWise argues produces better data for its AI.

Cochran said an off KPI should trigger an AI explanation of what went wrong, rather than leaving a CFO to wait for a logistics analyst. The freight-audit module compares quoted and final invoiced rates, identifies discrepancies, quantifies the financial impact and recommends carrier dispute strategies.

Why did FreightWise wait?

FreightWise deliberately waited six to eight months longer than Cochran wanted. The company had seen users engage with chatbots built on incomplete data and context, but without long-lasting value. Cochran summarized its threshold as waiting until AI could become operational.

FreightWise calls the approach operationally executable AI. CTO Chris Haarmeyer said the team began with problems raised by customers and internal users, including delivery dates that ignored hours of service and scheduled appointments, rather than adding AI to imitate competitors.

Security sits inside that operating rule. Haarmeyer said customer data remains protected, never trains third-party models and is limited to opt-in AI features.

Where should shippers draw the line?

Bad data still bites. Cochran warned that weak inputs can produce hallucinations and false conclusions, leaving shippers without a clean, single source of record at a disadvantage. His practical condition was simple: a shipper without good data is not ready for AI.

He also rejects AI as a stand-in for people or service. A human still sells the order, loads the truck and delivers the goods. He does not want customers calling FreightWise support or an account manager to reach a bot; when a customer is in a pinch, Cochran wants them talking to a real person.

His sharper warning is commercial: shippers that delay adoption risk letting their cost to serve drift out of control.

What happens next?

The proof now rests on execution. Legacy Kuebix upgrades are underway, and FreightWise says additional AI-backed functions will arrive in 2027 and beyond. Its priorities are practical: solve real business problems, integrate cleanly with existing technology, flag errors and improve the data behind each decision.

Over the next year, the SaaS-only migration will test whether one roadmap can retain nearly two decades of Kuebix behavior while adding routing, integration and audit tools that planners actually use.

FreightWise’s 2027 expansion will be credible only if the platform turns those operational constraints into dependable delivery dates, cleaner order feeds and quantified freight-audit exceptions without pushing routine customer decisions onto software.