The Scale Problem

As call volume increased, Einride’s teams were forced to manage multiple responsibilities at once. Support reps were answering inbound calls, collecting issue details, creating tickets, coordinating vendors, and manually tracking loads across different systems.

Maui, Director of Customer Service at Einride, described how tracking and tracing was still largely manual. Reps were doing their best to keep up, but as volume continued to rise, it became clear that this approach would eventually require a dedicated team just to handle tracking alone.

Headcount was not a sustainable answer.

At the same time, Einride was relying on Zendesk as part of its support stack. While widely used, it was expensive and did not meaningfully reduce operational friction. Tickets were duplicated across systems, workflows were fragmented, and the return on investment did not justify the cost.

The math simply did not add up.

“we were manually track it. Our reps do a lot of different things, and at some point, we would need a specific team just to track and trace shipments.”
Maui, Director of Customer Service

The Solution

Einride realized they needed more than a traditional support tool. They needed an intelligent operational layer that could sit in front of their workflows, absorb volume, and structure information before humans got involved.

They broke the solution down into three phases: strategic selection, rapid implementation, and workflow automation.

Strategic Selection

Einride began evaluating Voice AI as a way to scale operations without sacrificing service quality. The objective was not to remove humans from the loop, but to automate the repetitive, high-volume interactions that were slowing teams down.

Chelsea Thompson, Director of Operations at Einride, explained the core challenge clearly. “We needed a scalable way to manage high call volumes without sacrificing customer experience or putting additional strain on our operations team.”

Callers stood out due to its ease of integration, flexibility in workflow design, and highly responsive team. The ability to move quickly and adapt the system to real operational requirements was a decisive factor.

Rapid Implementation
Callers was deployed as the first point of contact for inbound driver calls. From day one, calls were answered instantly, twenty-four seven, without queues or delays.

Drivers were guided through natural conversations designed to capture mandatory information such as truck number, location, issue type, and urgency. Instead of agents spending valuable time gathering context at the start of every call, that information was already structured and ready.

"The speed to value surprised us. We were up and running faster than expected and started seeing measurable results within the first week."
Chelsea Thompson, Director of Member Acquisition & Growth

Workflow Automation
Once information was captured, it was logged directly into Einride’s operational systems, including Fleetio. Support teams received complete, actionable tickets instead of fragmented notes or missing details.

For Maui and his team, this fundamentally changed how they operated. Reps were no longer calling drivers blind. They could see the truck, location, issue description, and relevant context before taking action.

Routine issues were handled automatically. More complex cases were escalated with full visibility and intent.

Expanding the Engine
What started as inbound automation quickly proved its value across the organization. Shortly after going live, Einride made the decision to fully remove Zendesk from its stack.

The cost savings were immediate.

Maui was candid about the decision. Zendesk, while common, was expensive and underutilized. 
“As soon as we went live with you guys, we stopped using Zendesk,” he said. “We were paying close to two thousand dollars a month and underusing it. It didn’t make sense anymore.”

The switch resulted in over $65K in annual cost savings, without reducing service quality or operational visibility.

Unprecedented Connection Rates

Operationally, the impact was immediate. Response times improved, manual call handling decreased, and human follow-ups became faster and more effective because full context was always available.

More importantly, Einride avoided adding additional support headcount as call volume continued to grow.

Reliability and adoption were strong. Maui noted that downtime was rare and that drivers frequently assumed they were speaking with a real person. “Some drivers only realized it was AI at the very end of the call,” he said. “That surprised me.”

"We needed a scalable way to manage high call volumes without sacrificing customer experience."

Chelsea ThompsonDirector of Operations

Operational Readiness and Data Quality

One of the most meaningful improvements came from data consistency. Before Callers, critical information was often missing or incomplete, forcing teams to chase details after the fact. With Voice AI handling intake, mandatory fields such as truck number, location, and issue type were captured before tickets progressed.

Maui explained why this mattered operationally. “When we call the driver back, we already know the truck, the issue, and where they are,” he said. “We’re already ahead of the curve.”

This readiness allowed teams to act faster, coordinate vendors more efficiently, and resolve issues with fewer touchpoints.

Reliability, Adoption, and Driver Trust

System reliability was critical for Einride’s operations, where missed calls or incomplete data could disrupt real-world logistics. Over more than a year of use, downtime was rare, and call data consistently arrived with the required information.

Maui emphasized that reliability was one of the reasons he continued to advocate internally for Callers. “I can probably count on one hand the times it was ever offline,” he said. “And when things did happen, your team was always responsive and collaborative.”

Driver adoption was another unexpected outcome. Many drivers assumed they were speaking with a real person, only realizing later that the interaction was handled by AI.

“Some drivers told us they didn’t realize it was AI until the very end of the call. That surprised me, especially because of how empathetic it sounded."
Maui, Director of Customer Service

The Future: Expanding the Engine

Following the success of inbound automation, Einride is now expanding Callers into outbound workflows. This includes automated driver updates throughout the load lifecycle and calls triggered by real-time operational data.

Longer term, the model is expected to expand into European operations, allowing teams to replicate the same approach without rebuilding processes or increasing headcount.

What began as a way to manage inbound calls evolved into a foundational operational layer. 
Callers enabled Einride to scale service, reduce costs, and maintain trust across a growing, complex logistics environment.

The result was not just efficiency. 
It was confidence in the ability to grow without breaking the system.

“We started with inbound driver support. Now we’re expanding into tracking, outbound ETA calls, and ticket automation."
Maui, Director of Customer Service