FortyGuard delivers temperature intelligence at the level where heat is actually experienced and managed: 2 meters above ground, hour by hour, at street and building scale.

At the core is a two-layer stack:

1. Large Temperature Models (LTMs)
   LTMs are purpose-built AI models trained to predict ambient air temperature at human-relevant height and high spatial resolution. They fuse satellite-derived signals, high‑resolution GIS layers (urban form, land cover, surface materials), meteorology, and in‑situ observations into calibrated temperature fields designed for direct operational use—not research outputs.
2. Temperature Operating System (tOS)
   tOS is the operational platform that standardizes ingestion, manages data provenance, orchestrates model training and serving, and delivers outputs through the Dashboard and authenticated APIs. It is built for repeatable deployments, enterprise integrations, and reliability at scale.

What makes FortyGuard technically distinct is its forecasting approach: instead of outputting a single “best guess” temperature, the system is built around probabilistic forecasting that captures extremes and local variability, so teams can plan for risk, not averages.

Finally, the measurement focus is deliberate: 2‑meter ambient temperature, not land‑surface temperature and not coarse weather grids. This is the difference between an interesting climate context and an actionable operational signal.