Lore surfaces knowledge risks from maintenance history, captures expert context at the right moment, and governs it into operational artifacts — before the expertise walks out the door.
Prioritized knowledge risks: concentration, recurrence, retirement exposure, drift — each waiting for a human decision.
Org-level metabolism: surfacing, capture rate, approval latency, placement — is knowledge getting stronger or leaking?
Maintenance teams generate thousands of work orders, but the knowledge behind the resolutions stays in people's heads. When those people leave, the knowledge goes with them.
Your best technician retires in 18 months. Can someone else troubleshoot the RAS pump?
The same failure has happened 12 times. The fix is in one person's head.
Work orders close every day. The knowledge in them doesn't go anywhere useful.
Lore turns your existing work order history into a prioritized map of knowledge risk, then helps you close the gaps systematically.
Lore analyzes your work order history to find where knowledge is missing, concentrated, or at risk. No new data entry required.
Selective prompts at work completion — voice or text. The right person, the right time, the right question. Not a suggestion box.
A reviewer approves, classifies, and places each piece of knowledge where it belongs — asset notes, troubleshooting cards, SOP updates.
See how assets, experts, and failure modes connect — and what breaks when someone leaves.
Leadership view of whether the org is capturing and placing knowledge fast enough.
Flag when practice diverges from documentation — surfaced for reviewer judgment, not auto-policy.
Artifacts formatted for where knowledge actually lives: notes, cards, WO context, SOP deltas.
Product screenshot
Issue board showing prioritized knowledge risks
(Replace with a real screenshot from your demo)
Not a CMMS. Not a chatbot. Not a connected worker platform.
Lore is a knowledge risk workspace for maintenance teams
who can't afford to lose what their best people know.
The first screen is the outcome. If you need to know how Lore backs that up — detection, governance, leadership health, practice vs procedure, and where assistive AI fits — we spell it out in plain language.
From your work order history, not generic AI chat.
Human sign-off before anything becomes operational truth.
Models assist; they do not replace judgment or your change process.