How PaleoTech builds physics-driven climate intelligence.

PaleoTech’s climate-intelligence platform is built on a modular, first-principles framework that integrates Earth-system physics, signal analysis, orbital mechanics, hydrology, and climate-state inference.

Our technology reconstructs and predicts climate behaviour using interpretable, physics-driven signals rather than historical curve-fits or opaque machine-learning systems.

Climate-state inference allows PaleoTech to identify underlying regime shifts before they appear in traditional observations.

Each module contributes a different component of the climate system — rainfall, ENSO regime structure, axial tilt behaviour, seasonal patterning, mass redistribution, and long-lead climate signals. Together, they form an integrated engine for agriculture, climate services, and research.

Our system extends predictability windows by leveraging slow-moving physical drivers such as tilt anomalies, mass-flow redistribution, and ENSO regime transitions.

Technology Philosophy

PaleoTech’s approach is based on three principles:

1. Physics First

Climate behaviour is fundamentally driven by physical systems — not historical curve-fits.

2. Interpretable by Design

Every module exposes meaningful signal structure that can be traced and explained.

3. Built for Real-World Application

Outputs serve agriculture, climate services, environmental planning, and research organisations.