Imagine every connected device on a construction site—cranes, sensors, cameras, worker wearables—has a constantly updating ‘credit score for security and safety’. This system uses AI to watch how each device behaves and automatically flags or fixes issues before they turn into regulatory violations, outages, or accidents.
Manual, spreadsheet-based IoT risk management on complex construction projects is slow, error‑prone, and can’t keep up with thousands of devices and evolving regulations. This use case automates continuous risk scoring and remediation across IoT fleets so companies stay compliant and reduce security and safety incidents without adding huge compliance teams.
Tight integration of AI risk scoring with IoT identity, device telemetry, and policy enforcement across large, heterogeneous device fleets; proprietary scoring models and historical risk data improve over time and become hard to replicate.
Hybrid
Vector Search
High (Custom Models/Infra)
High-volume, continuous telemetry ingestion and real-time scoring across millions of heterogeneous IoT devices; integrating with diverse construction-site networks and legacy equipment while maintaining low-latency decisions.
Early Adopters
Focus on translating complex IoT security posture into simple, actionable trust scores and tying those scores to automated remediation workflows, rather than just dashboards or one-off assessments; tailored to regulated, safety-critical environments like large construction and industrial sites.