The Physicist Who Taught RAMP AI
to Think Like an Engineer

Premier Labs built RAMP AI by pairing two different kinds of expertise: Brent Bregar, VP of Product Strategy, brought more than twenty years of plant floor experience, and Praveen Gowtham, RAMP AI's Chief Technology Officer, brought the ability to turn that experience into an agentic system.

RAMP AI reasons the way a seasoned engineer would, working through a machine's PLC logic, alarm history, and prior fixes to walk a maintenance technician toward a root cause in plain language, rather than assuming they already know where to look. Building that reasoning capability and shaping what it needed to become was largely Gowtham's work.

Arriving Without a Blueprint

Before Gowtham joined the team, RAMP AI was still an early idea inside Premier Labs, built around a real problem on the plant floor rather than a fully formed plan. Gowtham arrived with experience building similar systems in nanotechnology and semiconductor physics, and expected the work ahead to look much the same. It did not take long to see otherwise.

The moment that changed his thinking came early, sitting with a stack of manuals, complex engineering diagrams, and a customer's PLC code. Gowtham realized the real challenge was not the information itself, but what to do with it: truly understanding the troubleshooting process an automation engineer follows, and building a system that could embody that process faithfully and reliably.

Turning Complexity Into a Usable Answer

Bregar, then an advisor to the project, became a guide, helping Gowtham think through the troubleshooting process the way an experienced engineer would and finding ways to hand over that data in a form he could mine reliably. Working from that foundation gave Gowtham something concrete to build against, and he could finally start structuring a real system instead of interpreting diagrams by eye.

Gowtham's target was specific:

Doing that meant designing from two directions at once: the mental model of how an engineer reasons through a problem, and the separate question of how a language model interprets that reasoning and turns it into something a user can act on.

Solving that reasoning problem was only half of it. The other half was making sure none of that complexity reached the person using the system. A maintenance technician does not need, and often does not want, to see the control logic underneath a diagnosis. What they need is a straightforward answer to what is wrong and what to do about it.

More Than a Chatbot

Gowtham draws a firm line between RAMP AI and a general-purpose chatbot. A standard chat interface is user-driven: the person has to already know what to ask, which is precisely the knowledge a technician facing an unfamiliar fault does not yet have. RAMP AI is built to guide instead. A technician states the problem, and the system narrows it down step by step toward a specific answer, following a consistent process rather than an open-ended conversation.

The agentic system has dual responsibilities. It does the necessary calculations and reasoning for diagnostic correctness, and it is then responsible for translating its findings into a set of actionable steps for a maintenance technician, in clear and simple language. The difference separates a tool that is confidently wrong from a tool that has worked the problem through and does not leave a technician scratching their head at three in the morning.

Diagnostics are only one way a person interacts with RAMP AI. Ask and Explore is the system's freeform interface, built for open questions rather than a specific fault: the kind a technician or engineer asks when they want RAMP AI to explain how something works rather than fix it. It is armed with tools that mirror how an experienced engineer or technician would extract information from a wiring diagram or a pump schematic, and it surfaces the most relevant answer directly instead of leaving someone to search for it.

Leaders use the same interface differently, asking what faults have been most common in a given month or where the recurring pain points are, with the system drawing on diagnostic history to answer directly rather than generating a formal report. During an early deployment, one of the automation engineers at the customer site started using Ask and Explore to build training material for operators, generating questionnaires and quizzes, a use Gowtham had not anticipated but was glad to see.

What's Next

Gowtham describes RAMP AI's direction across a few fronts. The first is continuous learning and self-improvement of the engine. The second is continuing to deepen the reasoning engine itself, pushing specific capabilities further even in areas where the system already performs well.

The third is expanding beyond the two platforms RAMP AI supports today, Rockwell ControlLogix and Siemens S7, which together cover most PLC-driven equipment in the United States. Each additional platform Gowtham brings in validates that the system was built to be cross-vendor from the start, while also teaching him something new about what cross-vendor support requires in practice. The work gets easier with each one, but it does not get simpler.