Predictive Maintenance is the leap from fix it when it breaks to fix it before it breaks — using telemetry from connected equipment plus historical patterns to flag failures days or weeks in advance. For commercial customers especially, this turns one-off service calls into ongoing relationships and break-fix margins into recurring contract revenue. This guide walks the conceptual model, the prerequisites that determine whether the feature works in your operation, and how to roll it out without overpromising to customers.
Estimated time: 8 minutes
Before You Begin
- Owner or Tenant Administrator role (Roles, Permissions, and Security)
- Asset records populated for your serviceable equipment (Equipment and Asset Tracking) — the Predictive feature has nothing to predict on without these
- (Recommended) 6-12 months of repair history linked to those assets — ML needs training data
- (Optional) IoT-enabled equipment at customer sites — modern commercial HVAC, refrigeration, and boilers report telemetry; residential equipment usually doesn’t
How predictive maintenance actually works
Three input signals power the predictions:
- Repair history — what failed, when, on what equipment, how it was fixed. Patterns emerge: capacitors fail every 5-7 years on this brand, compressors fail under specific amp-draw signatures.
- Equipment metadata — make, model, install date, warranty status. Combined with manufacturer-published failure curves, this gives a baseline expectation.
- Live telemetry (if available) — IoT sensors reporting temperatures, pressures, run-times, current draws. The richest signal, but only available on connected equipment.
The output is a risk score per asset: Low / Medium / High / Critical, with the specific failure mode that’s most likely (e.g. “Capacitor degradation likely within 2-4 weeks”).
Step 1: Make sure Assets are populated
Predictive Maintenance is downstream of Asset records. Open Assets and confirm:
- Every customer’s serviceable equipment is recorded with make, model, serial, install date
- Past Jobs are linked to the right asset (the part of the customer record that did this work)
- Condition fields are kept current (Good / Fair / Poor)
If you’ve been on Exoserva 6+ months and have done linked Jobs, this data is already there. If you’re brand-new or migrated from a platform that didn’t track assets, this is your first step — and the work is significant (a few weeks of building the asset library as you visit each property).
Step 2: Connect IoT telemetry (if available)
For commercial customers with smart equipment (Lennox iComfort, Carrier Infinity, Daikin VRV, modern Rinnai water heaters, commercial refrigeration with Modbus/BACnet):
- Open the asset’s detail page → Telemetry tab
- Click Add Sensor / Connection — Exoserva supports the major commercial protocols
- Provide credentials or sensor URL
- Verify data is flowing — within 15 minutes you should see readings
For residential customers, telemetry is rare. Predictions will rely on repair history + equipment age, which is still useful but less precise.
Tip: Don’t sell predictive maintenance to every customer. Residential customers without telemetry get probabilistic predictions (“based on this unit’s age, expect failure within 12-18 months”), not signal-based ones (“compressor draw is rising this week”). Set expectations in your sales conversation.
Step 3: Configure risk thresholds
In Settings → Operations → Predictive Maintenance (or the relevant settings entry depending on your tier), configure:
- Alert threshold — risk score that triggers notifications (default: High)
- Alert recipients — Owner only / Owner + Dispatcher / Per-customer assigned tech
- Customer notification — whether the customer gets a heads-up “we’re seeing signals of possible upcoming maintenance need” (default: Off; turn on for service-contract customers)
- Auto-create maintenance Job — when a Critical risk hits, automatically create a Job in Draft status for review (default: Off; turn on once you trust the predictions)
Step 4: Watch the Risk dashboard
Predictive Maintenance surfaces a Risk Dashboard at /predictive-maintenance (or under Assets, depending on tier). Three columns:
- Critical — failure likely within days; immediate intervention recommended
- High — failure likely within 2-4 weeks; schedule maintenance proactively
- Medium — degradation observed; monitor
Each card shows: customer, asset, predicted failure mode, confidence %, signals supporting the prediction. Click into any card for full context.
Step 5: Convert predictions into outreach
The recommended workflow when a Critical or High prediction appears:
- Open the prediction → review the signals (is the prediction reasonable?)
- Reach out to the customer: “Hi {name}, our system is seeing some patterns on your {equipment} that suggest a service visit might be a good idea — happy to swing by this week and check it out.”
- If they agree: schedule a job, link to the asset, complete the work
- After the job: feed back into the model — “prediction accurate / partially / not” to improve future precision
Tip: A 70-80% precision rate (the prediction was right) is healthy. Below 50% means the model needs more data or your asset records have errors. Don’t push out predictions you haven’t reviewed — the customer relationship damage from a false alarm exceeds the convenience of automation.
Step 6: Scale into recurring contracts
Predictive Maintenance reaches its full value when paired with service contracts — a customer pays a recurring fee for proactive maintenance, you use predictions to schedule efficiently:
- Standard quarterly visit + ad-hoc visits when predictions trigger
- Higher-tier monthly check-ins for Critical-equipment customers
- Per-asset coverage tiers (Bronze / Silver / Gold) with different prediction-response SLAs
Recurring revenue stabilizes your business through seasonal dips and creates a moat against competitors. Predictive Maintenance is the technical enabler; the business model is the contract.
Step 7: Honest expectations
Predictive Maintenance is probabilistic, not magical. Realistic outcomes:
- For commercial customers with telemetry: 70-85% precision, 1-3 weeks of advance warning
- For residential customers without telemetry: 50-65% precision based on age + history, 2-6 months of advance warning (less actionable)
- Cold-start customers (first 12 months on platform): minimal prediction value — the model needs your repair-history data to learn
Don’t lead a sales pitch with predictive maintenance unless you can deliver on it. Most contractors find it most useful for the existing book — turning their best 50 commercial customers into proactive-maintenance accounts — not as a tool for landing new ones.
Step 8: Audit and tune
Quarterly, run a model audit:
- Predictions made — total count
- Predictions acted on — how many led to a maintenance visit
- Prediction accuracy — how often the prediction was correct (post-job feedback)
- False positive rate — how often you visited and the equipment was fine
Use these numbers to tune your Alert threshold and Customer notification settings. Trade off precision vs recall based on the contractor’s appetite for false alarms (low alarm tolerance) vs missed predictions (low miss tolerance).
Real-World Example
You’re an HVAC shop with 25 commercial accounts on quarterly maintenance contracts. Six months after enabling Predictive Maintenance: 18 of those 25 customers had a Critical or High prediction trigger between scheduled visits. You proactively reached out, scheduled 14 of them in (4 declined), 11 of those 14 had real issues (78% precision). Customer outcome: their equipment kept running through the busy season instead of failing. Revenue impact: each Critical prediction averaged $850 in maintenance revenue + customer satisfaction translating to renewed (and upgraded) contracts the following year. Total payback on the feature: ~$15,000 in the first year, scales with the size of your service-contract book.
What’s Next?
- Equipment and Asset Tracking — the prerequisite data layer
- Preventive Maintenance Schedules — the foundation Predictive sits on
- Workflow Builder – Automate Your Operations — wire Predictive triggers to outreach automations
- Configuring the AI Assistant — AI tone for predictive customer messages
Need help? Post in the Tech Support category or contact support@exoserva.com.