Think of HARNESS as a digital safety officer that constantly watches what’s happening on a dangerous worksite, learns from past incidents, and warns your team before accidents are likely to happen.
High-risk construction and industrial environments (like Department of Energy facilities) struggle to predict and prevent accidents in real time; hazard recognition is manual, slow, and inconsistent across workers, leading to costly incidents, injuries, and downtime.
If deployed in practice, the moat would come from proprietary incident/sensor data from high‑risk DOE-style sites, validated safety prediction models, and deep integration into safety workflows and regulations rather than from the underlying algorithms alone.
Hybrid
Time-Series DB
High (Custom Models/Infra)
Real-time ingestion and processing of heterogeneous sensor, video, and operational data from large, complex facilities while keeping latency low and costs manageable, plus stringent data privacy and compliance constraints in DOE environments.
Early Adopters
Focus on proactive, model-based hazard forecasting specifically tuned to high-risk DOE and similar industrial/construction environments, rather than generic workplace safety analytics; blends human safety expertise with autonomous agent-based monitoring and forecasting loops.