VTP Altitude, Wakad, Pune, Maharashtra 411033, India
RouteSense: Last-Mile Delivery Optimization
VTP Altitude, Wakad, Pune, Maharashtra 411033, India
Ironbridge Industrial Group
Manufacturing
AI/ML Solutions, Cloud & DevOps, Software Development
Ironbridge Industrial Group was losing production days to equipment failures nobody saw coming, and running machinery reactively meant safety risk went up right alongside downtime. We built SentinelIQ, an IoT sensor network paired with a predictive-maintenance model that flags failing equipment and unsafe floor conditions before either becomes an incident.
Less unplanned downtime
To detect an anomaly, down from 9 days
Lower maintenance costs
Major safety incidents in 14 months
Unplanned factory downtime is a business cost, but the human cost runs deeper: worn or malfunctioning equipment that goes undetected until it fails is one of the leading causes of workplace injury on a factory floor. Reactive maintenance means machines are often run past the point where they should have been serviced, because nobody had visibility into their real condition. Catching that decay early isn't just about protecting output — it's about protecting the people standing next to the machine.
Ironbridge's maintenance logs were siloed by machine and mostly reactive — a technician found a problem after it already caused a stoppage. There was no unified view across the floor, no early-warning system for equipment stress, and no automated way to flag dangerous heat, vibration, or gas readings before they became a safety event.
Bearing temperature
Conveyor B2 · trending above baselineGas & air quality
All zones within safe rangeMaintenance queue
IoT sensors on critical machinery feeding a predictive-failure model
Real-time floor safety alerts for anomalous heat, vibration, and gas readings
One unified dashboard replacing siloed, machine-by-machine maintenance logs
Automated maintenance scheduling based on actual equipment condition, not fixed intervals
Unplanned downtime dropped 47%, and mean time to detect an equipment anomaly went from around nine days to under six hours. Maintenance costs fell 24% once servicing followed real equipment condition instead of a fixed calendar. Most importantly, the plant recorded zero major safety incidents in the fourteen months after launch, down from roughly three a year beforehand.
~9 days to detect a fault
Under 6 hours to detect
Predictive maintenance only earns its keep if it prevents real failures, not just logs more data. Here's what makes SentinelIQ worth the investment.
Prevents costly unplanned downtime and extends the working life of expensive equipment
Directly reduces workplace injury risk and the compliance liability that comes with it
Replaces siloed machine logs with one dashboard plant managers actually use daily
Typically pays for itself within months through avoided downtime alone
Years of experience
Building and supporting business-critical software.
Projects delivered
Across web, mobile, cloud and data platforms.
Client retention
Most clients stay on for ongoing support and new work.
Specialists on staff
Engineers, designers and analysts under one roof.
