The homepage is the promise. This page is the substance — still in plain language, still buyer-safe. No laundry list of buzzwords, and no claim that software replaces your reviewers.
Lore ingests patterns already sitting in CMMS exports: repeated failures, resolution time volatility, concentration of work in one expert, retirement exposure, and more. The output is a prioritized issue board — specific knowledge risks, not a vague “knowledge initiative.”
That is the wedge: showing gaps from data you already have, before asking technicians to adopt a new habit.
Capture is selective. What comes back goes through a reviewer workflow: approve, edit, classify destination, and track placement into real artifacts (notes, troubleshooting cards, WO context, SOP deltas). Nothing becomes “official” without human sign-off.
Lore is CMMS-adjacent: it helps produce governed knowledge and hand it to operational systems — it does not pretend to replace your system of record.
Maintenance leaders need to know whether the organization is getting smarter or leaking knowledge. Lore surfaces metrics such as how fast issues turn into captured knowledge, how long review takes, and whether approved knowledge actually reaches its destination.
The labels can get technical in-product; the idea is simple: throughput, coverage, and staleness — is knowledge moving end-to-end?
When you have written procedures, Lore can compare what technicians record in work orders to what the SOP says. Large or repeated gaps surface as drift: the field story and the book diverge. That is a governance signal — often a change-management or MOC conversation — not an auto-rewrite of your procedures.
Assets, experts, failure modes, and issues connect in a knowledge structure behind the scenes. The value is situational awareness: who holds concentrated knowledge, what breaks if they leave, what relates to what — not a flashy visualization for its own sake.
Lore may use modern models for structured extraction, similarity, or assistive checks. The product stance stays consistent: reviewers decide. Models help surface candidates and summarize evidence; they do not replace sign-off, safety culture, or your change process.
That is why we do not position Lore as a maintenance chatbot or an autonomous agent running the plant. It is a governance workspace for knowledge risk.