This is like giving your construction site a set of always‑awake, super‑observant eyes that can spot unsafe behavior or dangerous situations in real time and alert people before accidents happen.
Traditional safety monitoring on construction sites relies on human supervisors, manual inspections, and after‑the‑fact incident reviews, which miss near‑misses, fail to catch all PPE violations, and cannot watch every area 24/7. AI vision systems automate continuous monitoring to reduce accidents, enforce PPE compliance, and provide data for proactive safety improvements.
Integration into site workflows and EHS processes plus accumulated labeled video data of real‑world safety events (near‑misses, PPE violations, unsafe behaviors) can form a proprietary dataset that improves accuracy over time and is difficult for new entrants to replicate.
Hybrid
Unknown
High (Custom Models/Infra)
Video inference cost and latency at scale across many cameras, combined with on‑prem/edge deployment constraints and data privacy requirements for continuous worker monitoring.
Early Adopters
Focus on high‑risk workplace environments (such as construction) with domain‑specific models tuned for PPE detection, fall risk, and unsafe equipment interactions, rather than generic CCTV analytics.