Sense • Analyze • Predict • Act

    KLVIN's monitoring ecosystem connects every layer—from sensor to cloud—to create an intelligent feedback loop that keeps your assets at peak performance.

    01

    Sense

    Multi-parameter sensors continuously capture temperature, vibration, acoustic, electrical, and environmental data from critical assets in real time.

    02

    Analyze

    Edge AI algorithms process data locally to detect anomalies and patterns that signal emerging problems long before failure.

    03

    Predict

    Machine learning models forecast equipment failures days or weeks in advance, enabling proactive maintenance planning.

    04

    Act

    Automated alerts and recommendations guide maintenance teams to intervene precisely when and where needed.

    Patented Technology

    KLVIN's core industrial AI and predictive maintenance technology is protected by a granted patent in India, reflecting our commitment to genuine innovation in the edge-AI and IIoT space. Our intellectual property underpins the advanced anomaly detection, RUL forecasting, and automated decision-support capabilities that power every SENTINEL deployment.

    Comprehensive Asset Coverage

    Electric Motors

    AC and DC motors across all power ratings, from 30 HP to 550 HP.

    Compressors

    Air compressors, blowers, and pneumatic systems.

    Pumps

    Centrifugal, submersible, and specialty pumps.

    Thermal Equipment

    Furnaces, boilers, and heat exchangers.

    Machine Tools

    CNC machines, presses, and automated lines.

    HVAC & Environmental

    HVAC systems, autoclaves, and cleanrooms.

    Key Parameters Monitored

    Core, surface, and bearing temperatures with 0.1°C precision
    Three-axis vibration with frequency-domain decomposition
    Acoustic signatures and sound anomaly detection
    Magnetic flux variations indicating electrical imbalance
    Current draw and power consumption patterns
    Environmental gases including CO and VOCs (optional)
    Load distribution and mechanical stress indicators
    Runtime hours and operational cycle tracking

    Insights Delivered

    Early-stage fault detection weeks before visible symptoms
    Energy anomaly detection to pinpoint wasteful equipment
    Load imbalance alerts that highlight asymmetric loading
    Cooling inefficiency detection before overheating damages equipment
    Maintenance prioritization based on data-driven risk scoring
    Root cause analysis from historical trend correlation
    15%
    Energy Cost Reduction
    average reduction through optimization
    35%
    Downtime Prevention
    reduction in unplanned events
    25%
    Asset Life Extension
    increase in lifespan

    Backed by Industry & Academia

    Ministry of Electronics & IT (MeitY)MSME — Ministry of Micro, Small & Medium EnterprisesConfederation of Indian Industry (CII)iCreateiTIC — IIT Hyderabad Incubation CenterNSRCEL — IIM BangaloreDLabs — Indian School of BusinessMinistry of Electronics & IT (MeitY)MSME — Ministry of Micro, Small & Medium EnterprisesConfederation of Indian Industry (CII)iCreateiTIC — IIT Hyderabad Incubation CenterNSRCEL — IIM BangaloreDLabs — Indian School of Business