Industrial maintenance runs on expertise carried by people; systems capture only part of the story. Lore exists to make that expertise visible, governable, and transferable — while experienced people are still here to partner on it.
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 to preserve that depth as crews and roles change.
At the same time, I've grown up watching AI advance at an incredible pace. Those experiences showed me that in industrial environments, human expertise stays central. The opportunity is to preserve it, transfer it, and use AI in a way that 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 where it creates the most value — amplifying the judgment and experience of people on the floor.
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 that stands apart from written procedures. Lore scales the pattern-finding across thousands of work orders so your team can focus on decisions.
Lore uses modern models where they help: structured extraction, similarity, assistive checks. The product stance stays consistent: reviewers decide. Models surface candidates and evidence; your sign-off, safety culture, and change process stay in place. In plants, the goal is to preserve, transfer, and strengthen what experts 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.