Why Premier Labs Launched RAMP AI First 

The strategy behind RAMP AI, from Joel Reed, President of Premier Labs 

Premier Labs is Premier Automation's corporate venture studio, and RAMP AI, an agentic system that acts as an industrial machine interpreter for maintenance teams, is its first company. Brent Bregar, an automation engineer at Premier Automation who's now RAMP AI’s Chief Product Officer introduced the original idea to the Labs Studio, and Praveen Gowtham, RAMP AI's Chief Technology Officer, built the reasoning engine that makes it work. We sat down with Joel Reed, President of Premier Labs, to talk about the decisions behind how RAMP AI was developed, and why it was the first Premier Labs concept to reach the market. 

The Problem Wasn't Theoretical 

Premier Labs runs every new concept through the same test: a validated customer pain point, confirmed directly by the people living it. For RAMP AI, that meant talking to customers through the relationships Premier Automation, the Pittsburgh-based industrial systems integrator Premier Labs is built out of, had spent three decades building. What came back was concrete: automation engineers spending most of their time firefighting instead of modernizing, one experienced technician nearing retirement holding most of a plant's real troubleshooting knowledge, and alarms that regularly pointed people at the wrong problem. This is what convinced Premier Labs the problem was real enough to build around. 

The First Version Didn't Work 

The plan going in was to package Premier Automation's accumulated troubleshooting experience as a separate reference module, something Reed and the team called, half-jokingly, "Premier in a Box." The idea was to bolt that knowledge base onto a general AI model and let the system pull from it when needed. 

It didn't hold up. Reed is candid that some of the team's original assumptions about how to build RAMP AI simply did not work out. What replaced the reference-module idea was something the team calls deterministic pathways: hard-coded, known-good steps for breaking a problem down and searching for the right information, built directly into the reasoning process instead of sitting next to it as a lookup table. This shift, discovered through trial and error rather than planned from day one, is what ended up separating RAMP AI from a technician simply asking ChatGPT about a PLC fault, and it's the piece that moves mean time to repair, or MTTR. 

Reed Doesn't Buy the "Conservative Buyer" Story 

Asked how he navigates the conservatism of industrial buyers around AI, Reed pushed back on the premise itself. In his experience selling into capital-intensive, operationally constrained environments, the reputation for resistance to change is outdated. The workforce has shifted, with a generation raised on technology now running these plants, and Reed says many of the customers Premier Labs talked to were already trialing their own homegrown AI tools before Premier Labs showed up. 

The caution that does exist, Reed says, concentrates on technologies with a physical footprint, like robotics and autonomous systems, where the risk and the capital outlay are both real. RAMP AI doesn't carry that weight. It isn't a capital budget decision, which Reed points to as a real reason the resistance he expected going in simply wasn't there. 

Not a Vibe Coding Project 

Asked what he'd tell himself at the start, Reed's answer was simple: look for every way to go faster, from the very beginning. But he's just as clear that speed didn't mean skipping the validation, the failed first attempt, or the correction that followed. "This was not a vibe coding effort where we promised outrageous market revenue growth in six months," Reed says. This discipline, choosing the slower, harder path when the faster one didn't hold up, is exactly why RAMP AI launched first, and why Reed expects it to be around a long time.