Same sensor data. Four different dashboards. Zero surprises.
Tuesday you saw the code that predicts failures. Today you see how operations, engineering, maintenance, and QC each use those predictions differently. Same alerts, different actions, better outcomes.
Team Workflows
See how different roles use the same system to transform their daily work.Click each role below
Before Automation
With Automation
Dashboard Metrics
Impact By The Numbers
"I manage production now, not breakdowns. My job changed completely."
— Operations Manager, 11 years automotive parts
How Teams Work Together on One Alert
AI detects pressure anomaly 10 days before catastrophic failure. Watch how four roles collaborate to prevent $180K in lost production.
High-Risk Failure Scenario: Hydraulic Press #12
✨ Scroll here to watch the workflow
Plant-Wide Impact: Before vs After
| Metric | Before | After | Improvement |
|---|---|---|---|
| Unplanned Downtime | 147 hours/month | 8 hours/month | 95% reduction |
| Emergency Repairs | 23 incidents/month | 1.2 incidents/month | 95% reduction |
| Maintenance Costs | $87K/month (reactive) | $34K/month (planned) | $636K saved annually |
| Production Efficiency | 87% uptime | 99.4% uptime | 14% improvement |
Getting Your Team On Board
Operations: 'AI will miss critical failures and we'll get blamed'
Run parallel for 30 days - AI + manual checks. Show AI caught 94% of issues 2+ weeks early vs humans catching 12% same-day.
Ops managers become biggest advocates when they see early warnings prevent their worst days.
Engineers: 'Sensor data is noisy, AI will create false alarms'
Show precision metrics - 94% prediction accuracy, 6% false positive rate. Compare to current state: 60% of failures are complete surprises.
Engineers trust the system when they see it's more accurate than their gut feel.
Maintenance: 'This will eliminate our jobs'
Show workload shift - 60% emergency → 95% planned. Same headcount, better quality of life. No more 2am calls.
Maintenance crews love it - predictable schedules, no more hero culture, home for dinner.
QC: 'We still need to inspect everything, this doesn't help us'
Show defect prevention - catch machine drift 48 hours before bad parts. Inspection becomes validation, not detection.
QC shifts from reactive inspectors to proactive quality guardians. Defects drop 95%.
Leadership: 'ROI takes too long, upfront cost is too high'
Calculate one prevented failure - $180K lost production + $25K emergency repair = $205K. System pays for itself in 6 weeks.
Finance approves when they see monthly savings exceed subscription cost by 10x.
Investment & ROI
Typical payback in 4-6 weeks from prevented downtime
Pricing
ROI Calculator
Proven Results
From Demo to Live in 3 Weeks
From demo to production in just 3 weeks
- Connect existing sensors (vibration, temp, pressure, current)
- Install additional sensors on critical machines (if needed)
- Collect 7 days of baseline data for ML model training
- Configure role-based dashboards for each team
- Train each role on their dashboard (2hr sessions per role)
- Run AI predictions alongside current maintenance schedule
- Compare AI alerts vs actual failures (build trust)
- Adjust alert thresholds based on team feedback
- Switch from parallel to primary system
- Teams begin acting on AI predictions proactively
- Daily check-ins for first week of live operation
- Measure baseline metrics vs new performance
Enterprise multi-site deployments: 4-6 weeks per site with staggered rollout
2026 Randeep Bhatia. All Rights Reserved.
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