Inside Hirschbach’s push into AI driver communication

Every large fleet chasing AI eventually runs into the same fork in the road: buy a partner solution or build the technology in-house. Hirschbach Motor Lines picked a partner, and the trucking carrier is now automating driver communication through an AI agent built by Augment.

The rollout began on the brokerage side, where the agent, named Augie, now handles all outbound driver outreach across three interaction types: driver information requests, pickup arrivals and delivery arrivals. Those three interactions represent the automatable slice of the work.

They account for roughly 40% of Hirschbach’s overall track-and-trace volume, excluding power-only freight. Within that slice, Augie is already reaching drivers on more than 85% of the carrier’s Logistics Solutions loads and automating over 300 pickup and delivery check-ins a week.

“For me, the early success isn’t simply about the number of calls or messages Augie handles,” Ivan Ramirez, CTO at Hirschbach Motor Lines, told FreightWaves. “It’s that we’re proving AI can become part of the operating model and reliably own a defined portion of the work. That was the big unknown: it works really well in demo environments.

How does it actually work in real environments? And we’ve gotten it there.” Customers can also rename Augie. In the case of Hirschbach, they refer to their AI teammate as Hirschie.

The Buy-Versus-Build Decision Behind AI Driver Communication The decision to bring in an outside AI partner came after roughly a year and a half of evaluating vendors, many of whom showed up with polished voice demos and little else built. “I knew none of these guys had anything built,” Ramirez said.

“They’d all just gone and raised a bunch of money and had this great idea on how they were going to build out these different AI platforms. For me and our team, it was really about the team. What team are we going to partner with?”

Augment stood out on three fronts, Ramirez said: a team that combined logistics experience with technology depth, a product roadmap that stretched beyond track-and-trace into appointment scheduling, load creation and carrier communication, and a willingness to let Hirschbach shape that roadmap rather than wait on a vendor’s release schedule.

“We did not want a traditional vendor relationship where we purchased a fixed product and waited for features,” Ramirez said. “We’ve done that before and it’s been a horrible experience. We wanted a partner willing to learn alongside us.” That led to a deliberate build-versus-buy decision, even with a technology team capable of doing more in-house.

“We made a decision early on that Hirschbach is a transportation company that uses AI to operate better,” Ramirez said. “We’re not trying to become an AI infrastructure company. So let’s go find a really good partner where we can get to value a lot faster and get real operational value.”

Why Large Fleets Are Different Selling AI into an enterprise carrier looks nothing like selling it into a startup-friendly niche, according to Harish Abbott, co-founder and CEO of Augment. Dedicated operations alone carry layers of complexity: multiple stops, multiple loads, bill of lading handling and facility-specific assignment rules.

“The very first thing in all of this is: how do we get folks out of the day-to-day busy stuff, the unglamorous work, so they can be freed up to do more creative work,” Abbott said. Appointment scheduling is one of the biggest pain points large fleets bring to the table, Abbott said, particularly through high-volume retail portals.

“It’s not easy to make appointments, especially in these large portals like Walmart and others,” Abbott said. “Power-only is very different than live load, very different than dedicated runs.”

The bigger opportunity, he said, is tying appointment data back into hours-of-service and driver planning so fleets can see the whole network rather than one appointment at a time. The Data Problem Behind the 20% Roughly 70% to 80% of Hirschbach’s shipments arrive through EDI already structured for automation.

The rest shows up messier: tender emails, PDFs, or a bill of lading handed straight to a driver on a dedicated run. “How do you get them into the system, assigned to the right customer code, with a high degree of certainty so humans aren’t entering that, but also faster?” Abbott said. “So everything is detention.

Accessorials are all tied to that shipment very early on versus finger-pointing that happens after a load is delivered.” Ramirez pointed to the EDI 214 status message as an example of the inefficiency AI is meant to erase. “If I look at my EDI transactions, the biggest part of the 214, that’s where the biggest expense is,” Ramirez said.

“I’m already giving you guys all this stuff. Why are you reaching out for this stuff again? … We’re a low-margin business. I’m trying to figure out a way, and AI is a perfect answer to this stuff. It’s the stuff that we absolutely need to do. Let’s just let AI handle it and we’ll forge