Lore works with data you already have: exports and roster files you generate today, with a short path to first insight.
Upload a CSV or XLSX export of your work order history and workforce roster. Lore's gap engine analyzes the data to surface prioritized knowledge risks from the rows you already have.
Assets with repeated corrective work where troubleshooting steps still need to be written down.
Critical tasks handled by a single technician — especially those approaching retirement.
Patterns in work orders that suggest actual practice has diverged from documented SOPs.
Expensive assets with poor troubleshooting coverage or excessive resolution times.
Experienced technicians with deep expertise on critical assets who deserve a documented backup path.
Undocumented workarounds and tribal knowledge that live outside the official system.
Screenshot: Issue board
Prioritized knowledge risks with severity, asset, state, and recommended next action
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When a work order closes on a flagged issue, Lore sends a targeted prompt to the technician who just did the work. Voice or text, 60 seconds, done — via link or SMS, lightweight by design.
Each prompt is tailored to the specific knowledge gap Lore has identified — a targeted question tied to the issue on the board.
Lore prompts rarely and only for high-signal gaps — enough to capture value, light enough to keep response rates high.
Record a short voice memo or type a few sentences. Lore transcribes and structures it automatically.
Screenshot: Capture page
Mobile-friendly capture: voice recording and text input tied to the issue
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Every piece of captured knowledge enters a governed review workflow. A reviewer approves, edits, classifies the destination, and places it where it belongs. Every go-live runs through explicit human judgment.
Pending entries from technician capture, reviewer authoring, and promoted Q&A — all in one place.
Each approved entry is formatted for its target: asset notes, troubleshooting cards, WO notes, SOP deltas, or MOC drafts.
Track whether approved knowledge has been exported, delivered, and acknowledged in the target system.
Who contributed, who approved, what changed, when it was placed. Audit trail from raw input to artifact.
Screenshot: Review inbox
Pending knowledge entries with source type, contributor, and destination
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Maintenance leaders see whether knowledge is getting stronger or leaking: throughput from detection to placement, coverage, staleness, and concentration risk. Behind the scenes, assets, experts, and failure modes connect so you can see sole-expert risk and context in the same view as pipeline health.
Screenshot: Leadership / metabolism metrics
Net knowledge position, capture rate, placement, staleness, and related pipeline
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Screenshot: Knowledge graph
Relationships between assets, experts, failure modes, and issues (summary or drill-down)
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Approved knowledge is rendered for where it actually lives: CMMS notes, troubleshooting cards, work order context, SOP change proposals, or escalation paths. Export previews show exactly what reviewers will hand off.
Screenshot: Export preview
Destination-specific artifact (e.g. troubleshooting card, asset note) ready for delivery
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Want the deeper positioning story? Read capabilities