Why We Built a Machine Interpreter

Most industrial AI tools follow the same pattern. When a fault fires, the system surfaces a set of recommended steps, and a technician follows them. For well-documented, straightforward failures, that kind of product works well enough. 

The hardest problems on a plant floor are a different category: where the delay is not in the repair but in understanding what actually needs to be repaired. That is the gap we set out to close with a Machine Interpreter, RAMP AI. 

What We Kept Seeing 

Brent Bregar, RAMP AI's Chief Product Officer, has spent more than thirty years in industrial automation. When he gets called to a customer's facility, it is not for the easy problems.  

"They don't have somebody that does what I do on most plant floors anymore," Brent said. "And the logic used to be very simple 15, 20 years ago. Now it takes somebody to interpret it along the way, and they don't have that person." 

Premier Labs was built alongside Premier Automation for exactly this reason. Three decades of showing up on plant floors gave us direct access to the controls engineers, maintenance leads, and plant managers who live this problem every day. The question we kept asking was where time actually goes during a failure, what people wish they had in front of them, and which parts of their expertise are hardest to hand off. The answer was consistent. The bottleneck is not instruction. It is time to understanding. 

Machine Intelligence 

We use the term Machine Intelligence deliberately. Machine Intelligence is what happens when AI truly understands a machine — its logic, its history, its operating conditions. Not data retrieval or keyword search, but genuine machine understanding, grounded in PLC code as the source of truth. 

Most industrial AI reads data. RAMP AI reasons over it. 

PLC code is the source of truth because it is the only thing on the plant floor that is always current. Manuals go out of date the moment a machine is modified. Documentation reflects intent, not reality. As Brent described it: "The code says what's supposed to happen, and it's true." 

That reasoning process is what RAMP AI replicates. It works through the machine's own control logic, traces cause-and-effect relationships across alarms, fault history, and subsystems, and surfaces plain-language root-cause guidance that a technician on any shift can act on. The system does not tell people what to do. It gives them the facts, the context, and the reasoning, and trusts them to do the job they were trained to do. 

"We are not there to hold their hand," Brent said. "We are there to guide them so they can identify the proper path and not waste time getting to it. Once they get there, they don't need us." 

Compounding over time

What makes Machine Intelligence different from a monitoring tool or a search interface is what happens after the first resolved fault. Every diagnosis strengthens the system for the next one. Situational context accumulates across shifts, failures, and resolutions. The knowledge does not reset. 

"We're going to be your machine historian," Brent said. "We're going to capture knowledge that people had over the years — so as people move on or retire, that knowledge is still available." 

The code remains the source of truth. Everything else — historical fixes, documentation, expert reasoning — exists in the context of that. 

What happens next 

RAMP AI is onboarding a limited set of early pilot partners. If your plant runs complex PLC-driven equipment, relies on a small number of experts to solve the hard problems, and is looking for a practical way to bring Machine Intelligence into maintenance operations, we should talk. 

Premier Labs has additional ventures in active development, including a semi-autonomous material removal system for industrial environments and an AI-driven platform for enterprise-wide controls and operations management. Neither has been formally announced. 

Contact Joel Reed at jcreed@premierlabs.io or (412) 580-7846.