When FleetHub.AI set out to build a voice-first co-pilot for commercial fleets, the design challenge had little to do with screens and everything to do with judgment.
The machines got smarter. So did the human work.
There were no mockups. No component libraries. No color tokens. No screens to stare at in a design review while someone asks if the font could be slightly bigger.
Just a different kind of question than I was used to asking:
Who is E.L.L.A., and what does this actually do?
I am part of the Robots & Pencils team working with FleetHub.AI to shape E.L.L.A.’s experience, and I quickly realized we weren’t designing another interface. We were designing how an intelligent system should behave around a person.
Inside the Design of E.L.L.A., FleetHub.AI’s Voice Co-Pilot for Truck Drivers
FleetHub.AI is building a connected technology stack for commercial trucking fleets. At the center of that vision is E.L.L.A., a voice-first AI co-pilot designed to live in the cab alongside truck drivers, not as another app competing for attention, but as an intelligent layer connecting the systems already at work around them. Underneath, E.L.L.A. runs on Amazon Nova 2 Sonic for real-time voice and Amazon Bedrock for reasoning, with AWS Lambda, Amazon API Gateway, and Amazon DynamoDB handling everything in between.
We weren’t designing another interface for a driver to look at. We were designing how an intelligent system should listen, interpret, respond, and know when to stay out of the way.
E.L.L.A. had to work across a complicated technology environment while making the experience feel simple for the person behind the wheel. A co-pilot, not another screen. Something that needed to understand the difference between a driver who needed a heads-up about road conditions and a driver who needed silence. Something that had to know when to surface a delivery conflict, when to help, when to act, and when to simply let the road go by.
Eyes on the road. Hands on the wheel.
In this environment, every interaction that asks a driver to look away carries a cost. Visual design wasn’t irrelevant, but it couldn’t be the primary way E.L.L.A. communicated. The experience had to work without requiring the driver’s eyes.
Designing a Voice AI Persona for Trucking Means Asking Human Questions, Not UX Questions
The difference this time was what I was actually designing.
Not an experience built around a person. A person.
Not a screen a driver would glance at, but a presence a driver would talk to.
That meant designing a character: a voice, a sensibility, a set of behaviors, and a sense of when to speak and when to pause. It was one of the most human design problems I’d worked on, and it had almost nothing to do with visual design.
What would I want to hear if I’d been driving for six hours? What information would feel helpful at mile three and overwhelming at mile forty? If I was running late and stressed, would I want a conversation, or would I want E.L.L.A. to handle it quietly and tell me when it was done?
At what point does a helpful nudge become noise?
And once you answer that, what’s the next layer?
What if the driver just got off a difficult call? What if traffic is about to add forty minutes to an already-long day? What if something changes in the delivery workflow while the driver is moving?
These weren’t UX questions in the way I’d always framed them.
They were human questions.
The more I worked on E.L.L.A., the less I thought of her as a voice interface and the more I realized we were designing how she should behave — how she should understand context, make judgments, respond to a person, and know when to speak or stay quiet.
That distinction mattered.
A traditional interface gives someone a place to go and things to interact with. E.L.L.A. had to do more of the work. Her behavior had to account for what was happening around the driver, determine what mattered, and decide how — or whether — to bring it into the conversation.
That meant we weren’t just designing what E.L.L.A. would say.
We were designing the conditions under which she should speak at all.
Answering those questions required me to sit in the driver’s seat, not literally, but fully. To model a person, not just a user flow.
That project changed something in how I see our work.
E.L.L.A. made one thing impossible to ignore: when you strip away the screen, design doesn’t disappear. It moves somewhere else.
It moves into judgment.
Why Voice AI Design for Trucking Demands Human Judgment
We talk a lot right now about how AI is transforming design. The tools are faster. The cycles are compressed. What used to take three weeks of exploration can surface in three hours. I won’t argue with any of that.
But I think we’re sometimes tempted to let the speed of the tools define the conversation. To make the story about efficiency. About throughput. About how many design variations we can generate before lunch.
And I’d push back on that, not because the speed isn’t real, but because it’s not the point.
E.L.L.A. didn’t need more variations. She needed judgment.
She needed someone to think carefully about what a human being, inside a specific context, on a specific kind of day, would actually find useful.
No tool was going to figure that out.
That thinking had to come from somewhere, from someone willing to put themselves in those shoes and stay there long enough to feel the weight of the question.
And in trucking, that context matters.
A driver isn’t sitting at a desk waiting for information. They’re managing a long day on the road, navigating traffic, schedules, deliveries, communications, compliance requirements, and everything else that comes with keeping a truck moving.
The challenge isn’t necessarily a lack of information.
It’s too much of it, arriving through too many systems, at too many moments.
Good voice AI has to make that complexity feel simpler, not add another layer of noise.
That’s where judgment becomes part of the design.
When the Interface Becomes Behavior
Here’s what I keep coming back to.
With traditional digital products, we spend a lot of time designing what people see, where they tap, what happens next, and how information is organized on a screen.
With conversational and agentic AI, some of that work moves upstream.
We’re designing what the system knows. What it notices. What it prioritizes. What it does on someone’s behalf. What it says. How it says it. And, perhaps most importantly, what it chooses not to say.
The design artifact becomes behavior, not interface.
For E.L.L.A., that meant thinking about timing as carefully as content.
The right information at the wrong moment is still the wrong experience.
A useful notification can become an interruption. A well-intentioned prompt can become noise. A perfectly designed response can still be wrong if it arrives when the driver doesn’t need it.
The intelligence isn’t just in having the answer.
It’s in knowing when the answer matters.
The Design Work That Doesn’t Change
Here’s what I keep landing on:
The canvas has changed.
Interfaces are becoming ambient, conversational, and layered. With E.L.L.A., we weren’t just designing a screen or even a voice. We were designing an intelligent layer between a person and the systems surrounding them — one that had to account for what mattered, what could wait, and what didn’t need to be said at all.
We’re no longer just designing what people see.
We’re designing the conditions under which a system decides how to meet them.
That’s a bigger, stranger, more interesting problem than we’ve had before.
But the core of the work?
Unchanged.
We are still, fundamentally, in the business of understanding people well enough to know what they need before they’ve figured out how to ask for it.
That has always been the job.
Screens were just one era’s answer to how you meet a person where they are.
The tools get faster. The outputs get more sophisticated. The systems get more capable.
And every time the canvas shifts — from web to mobile, from mobile to voice, from voice to whatever comes next — the designers who do the best work are the ones who go back to the same question.
Not what can I build? What does this person actually need?
E.L.L.A. made that question feel especially real because the person at the center of the experience can’t always look at what we’ve built.
So, we had to design for everything else.
For attention. For timing. For trust. For context.
For knowing when to speak.
And when to let the road go by.
That’s what good design does.
It always has.
Learn more about Robots & Pencils’ solutions for the transportation industry.
About the Author
Brad Istnick is Principal Experience Creative at Robots & Pencils, where he helps shape how intelligent systems speak, listen, and decide — turning complex AI behavior into interactions that feel less like software and more like presence.
Key Takeaways
- Voice AI changes the design problem. When drivers can’t rely on a screen, designers have to think beyond visual hierarchy and into behavior, timing, and conversational judgment.
- E.L.L.A. is designed as more than a voice interface. She acts as an intelligent layer between the driver and the increasingly complex technology environment inside the cab.
- Good AI knows when not to speak. The right information at the wrong moment can still create a poor experience.
- Designing AI means designing behavior. What a system notices, prioritizes, says, does, and chooses not to do are all part of the experience.
- Human judgment remains essential. Faster AI tools can generate possibilities, but they don’t replace the human work of understanding what someone actually needs in a specific context.
- The canvas is changing, but the job isn’t. Whether the interface is a screen, a voice, or something else entirely, good design starts with understanding the person on the other side.
FAQs
What is voice AI for truck drivers?
Voice AI for truck drivers is AI technology that allows drivers to interact with information and fleet systems through natural conversation rather than relying primarily on screens, tapping, or manual navigation. The goal is to make information and assistance available while minimizing unnecessary visual and manual interaction with technology.
How does E.L.L.A. work as a voice AI co-pilot for commercial fleets?
E.L.L.A. is FleetHub.AI’s voice-first AI co-pilot, designed specifically for the commercial trucking environment. Rather than functioning as another standalone application, E.L.L.A. is designed to work as an intelligent layer across the systems and information surrounding the driver, helping determine what matters, when it matters, and how it should be communicated.
What is E.L.L.A. built on?
E.L.L.A. runs on Amazon Nova 2 Sonic for real-time voice and Amazon Bedrock, using Claude 3.5 Haiku for reasoning. AWS Lambda, Amazon API Gateway, and Amazon DynamoDB handle everything underneath the conversation.
How is E.L.L.A. different from a traditional voice assistant?
E.L.L.A. isn’t designed as a general-purpose voice assistant. She’s purpose-built for commercial trucking and the specific operational context of a professional driver. Her value comes not simply from answering questions, but from understanding the environment in which the driver is working and helping turn complex fleet information into timely, conversational assistance.
How is designing voice AI different from traditional UX design?
Designing voice AI moves much of the design work beyond screens and visual flows. Designers have to define a system’s voice, personality, behaviors, timing, decision-making, and rules for when it should speak and when it shouldn’t. The design artifact becomes behavior, not just interface.
How can voice AI reduce driver distraction?
Voice AI can reduce the need for drivers to physically interact with multiple applications while driving by making information and certain interactions available conversationally. The goal isn’t to eliminate every visual interface, but to reduce unnecessary moments when a driver needs to look away from the road to interact with technology.
What makes a good voice AI experience for truck drivers?
Context-awareness and judgment. A good voice AI understands that the same information can have very different value depending on what the driver is doing, what has already happened, and what requires attention right now. It earns trust by getting the timing right — not simply by having the right answer.
