Every large fleet pursuing AI faces a familiar decision: purchase a partner solution or develop the technology in-house. Hirschbach Motor Lines chose the partner route, and the Iowa-based 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. These are core components of track-and-trace, the process by which freight brokers and carriers confirm shipment status milestones — pickups, in-transit updates, and deliveries — throughout a load's journey. Together, these three interactions represent the automatable portion of the work, accounting for roughly 40% of Hirschbach's overall track-and-trace volume, excluding power-only freight — arrangements where a carrier provides only the tractor and driver, not the trailer. 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 per 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. Hirschbach refers to their AI teammate as Hirschie.

The Buy-Versus-Build Decision

The decision to bring in an outside AI partner followed roughly a year and a half of vendor evaluations. Many contenders arrived with polished voice demos but 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 distinguished itself on three fronts, Ramirez said: a team combining logistics experience with technology depth, a product roadmap extending 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 though Hirschbach has a technology team capable of doing more in-house. The question of whether to build proprietary AI tools or partner with specialized vendors has become a central strategic debate across the logistics industry, as carriers and brokers weigh speed-to-value against the cost and complexity of maintaining in-house AI infrastructure.

"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 view the entire network rather than one appointment at a time.

The Data Problem Behind the Remaining 20%

Roughly 70% to 80% of Hirschbach's shipments arrive through EDI — Electronic Data Interchange, the standardized messaging format widely used across the transportation industry for structured transactions between trading partners — already structured for automation. The remainder arrives in messier formats: tender emails, PDFs, or a bill of lading handed directly 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 — a standard transaction set used across the industry to report shipment milestones such as pickups, arrivals, and deliveries — as an example of the inefficiency AI is designed to eliminate.

"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 forget about it."

Abbott said narrow, specific use cases — not a broad AI rollout — are what earn an operator's trust.

"If you sprinkle AI across the board like 'here's this cool stuff and it's going to make everybody's life better,' the operator's like, 'Okay, my life hasn't changed. I'm still doing the same thing,'" Abbott said. "For operators, you have to be extremely specific: 'Hey, you're spending this much time on X and now let's have AI or Augie take care of it.' And they see that."

The AI Agent Hirschbach Wants for Driver Retention

The next use case Hirschbach plans to activate is an AI assistant positioned between drivers and their driver leaders, fielding routine questions so leaders can focus on the conversations that actually keep drivers around. Driver retention has long been one of the trucking industry's most persistent operational challenges, with carriers across the sector investing heavily in recruiting and retention amid recurring driver shortages and high turnover.

"The biggest complaints we get right now from our drivers is 'I can't get ahold of my driver leader,'" Ramirez said. "I'm a driver leader. I have 50 to 60 drivers that I'm handling. I can't be available for everyone at every single time to answer those calls."

"I've listened to some of these conversations that driver leaders have with their drivers. A lot of it is, they're literally psychologists," Ramirez said. "A lot of these conversations are not freight-related. They're 30-minute conversations about their family, their pay, 'I need more miles.' Those are the conversations we want our driver leaders having with our drivers because that's how you retain more drivers."

The longer-term vision extends beyond answering questions after the fact. Abbott described a model where the agent anticipates a delay and reschedules an appointment before a customer ever has to ask where a load is.

"What would be cool is that before the email comes from the customer, we reach out to the customer or the facility and say, 'Hey, this driver is running late. I'm rescheduling the appointment. It's done,'" Abbott said. "It's sort of anticipating exceptions and actually being proactive about it versus today, in all our use cases for AI it's very reactive."

That kind of proactive rescheduling helps both sides of the load, he said, since a warehouse aware that a truck is running late can reallocate the labor it had scheduled to unload it.

"Driver retention is a big thing for everyone," Ramirez said. "I can't wait to get to that use case."