Brent Bregar Built RAMP AI
to Think Like He Does

Premier Labs is Premier Automation's venture studio, built to turn deep industry experience into new companies. Many of its best ideas come from inside Premier Automation itself — from engineers who already know the problems the industry keeps running into.

Brent Bregar is one of those engineers. For more than twenty years he worked as an automation and controls specialist, called in on the toughest machine problems plants faced. RAMP AI is Labs' first venture, and it started as a response to a call for ideas. Bregar now serves as the company's Chief Product Officer.

RAMP AI is an agentic system built to act as an industrial machine interpreter. It reasons over a machine's PLC logic, alarm history, and prior fixes, then walks maintenance technicians toward a root cause in plain language rather than assuming they already know where to look. The need for it started with a problem Bregar kept running into himself.

A Method Without a Name

Plants brought Bregar in to fix machine problems no one else on staff could solve, often flying him out with little notice because the line was down and the cost was adding up fast. He built a reputation for narrowing a problem quickly, and for years he never considered it anything more than experience.

Everything shifted when he started testing generic AI against real troubleshooting problems. The AI outperformed him on the math: given the logic, the numbers, and the operating conditions, it worked through them faster and more precisely than he could. What it couldn't do was tell him where to look in the first place.

Teaching the Playbook

He came to call the missing piece his playbook — the judgment calls that happen before anyone opens the code, and the reason two technicians can look at the same fault and reach different diagnoses. Deciding whether to replace a suspected part on the spot or keep tracing the fault depends on what an hour of downtime is costing the line.

Naming the pattern made the direction for RAMP AI obvious: build a system that reasons the same way, working through the same judgment calls instead of just the math.

What Mean Time to Repair Really Means

RAMP AI is built to shrink one specific gap: the time between knowing a machine is broken and knowing what's actually wrong with it. Bregar calls this the real driver of mean time to repair, since once a technician knows what to fix, the repair itself rarely takes long.

Bregar had already seen what this gap costs. At a previous job he worked on a saw system prone to a recurring fault: metal chips from the cutting process would damage a sensor or its wiring in ways that were hard to spot and harder to trace.

Built to Earn IT's Trust

A faster diagnosis is worthless if IT won't let the system onto the floor in the first place. Security became the other piece Bregar pushed hard early.

His reasoning: the biggest obstacle to adoption wouldn't be technical, it would be earning the trust of IT departments, who often have more say over what comes onto a plant floor than the plant manager does. If IT isn't confident in a system, the default answer is no — because saying no rarely creates problems for anyone. So RAMP AI is air-gap compatible by design, with no live connection to the plant floor and no agent running on the controller.

Not a Replacement, a Transfer

The goal was never to replace the technician who already knows the machine. RAMP AI is designed to take what the best troubleshooter in a plant already knows and make it available to a new hire, a third-shift technician, or a facility an hour down the road.

Bregar spent more than two decades becoming the person plants called when nothing else worked. RAMP AI exists so that knowledge no longer has to live with just him, or any single technician. Reducing that risk helps manufacturers build more resilient and predictable operations.