Facilities
Equipment Failure Prediction
AI analyzes historical maintenance and usage data to predict equipment failures before they occur — so you fix issues before they shut down a store.
What it does
- Analyzes historical maintenance and usage data by equipment type
- Predicts which HVAC systems are likely to fail soon
- Identifies stores at risk of refrigeration or equipment outages
- Surfaces patterns that precede failure (noise, run hours, repair frequency)
- Prioritizes preemptive repairs by impact and probability
- Generates failure-risk dashboards by store and equipment
- Tracks prediction accuracy to improve the model over time
- Exports risk lists for capital and maintenance planning
What it takes
- Maintenance History / Work Orders
- Equipment Run Hours & Sensor Data
- HVAC & Refrigeration Asset List
- Failure & Repair History
- Store Criticality Matrix
What it returns
- Excel Table
- PDF Report
- Dashboard Export
- JSON Data
Written into the systems your team already runs, not into a second system of record.
How teams run it
HVAC Failure Risk
Identify HVAC systems likely to fail in the next 90 days.
Store Outage Risk
Flag stores at highest risk of equipment-related outages.
Preemptive Repair Priority
Rank equipment by failure probability and business impact.
Portfolio Risk Dashboard
Generate a risk dashboard across all stores and equipment types.
Cited, or flagged
Every value is anchored to the clause it came from. Where one is missing, Surfaice says so rather than inventing it.
Read where it lives
Documents are processed for the request and not retained afterwards. Nothing is copied into a second system of record.
Never trained on
Your documents do not train models — yours or anyone else's.
More in Facilities
See it run on your own documents.
Fifteen minutes, your files, and the clause behind every answer.