Industrial maintenance runs on expertise that lives in people, not systems. Lore exists to make that expertise visible, governable, and transferable — before it walks out the door.
I'm a mechanical engineering student at Notre Dame graduating this May. Through internships in energy, water utilities, and manufacturing, I saw firsthand how much industrial operations depend on the knowledge of experienced workers. The people closest to retirement often held the deepest expertise, and I kept thinking about how much of that knowledge could disappear if it was never captured.
At the same time, I've grown up watching AI advance at an incredible pace. But those experiences showed me that in industrial environments, human expertise is not replaceable. The real challenge is figuring out how to preserve it, transfer it, and use AI in a way that actually strengthens the people doing the work.
That's what inspired Lore: a way to help organizations capture critical operational knowledge, pass it on to the next generation, and put AI in the position where it can create the most value — by amplifying human expertise, not replacing it.
Every maintenance operation has a handful of people who know how everything really works. When those people retire, transfer, or are simply out sick on the wrong day, that knowledge disappears. The next technician spends hours troubleshooting something that used to take twenty minutes. Parts get ordered wrong. Mistakes get repeated.
The data that reveals these risks already exists in CMMS work order history — recurring failures on the same asset, resolution times that spike when the expert is absent, corrective work with no linked procedure. But nobody has time to mine thousands of work orders looking for patterns.
Lore uses modern models where they help: structured extraction, similarity, assistive checks. The product stance stays consistent: reviewers decide. Models surface candidates and evidence; they do not replace sign-off, safety culture, or your change process. In plants, the goal is not to replace experts — it is to preserve, transfer, and strengthen what they know.
Lore is built for maintenance and reliability leaders at facilities where operational knowledge is critical:
Lore is in early validation. We're working with maintenance teams to test the detection engine, refine capture workflows, and prove that governed knowledge placement reduces operational risk.