Interlinked

Interlinked’s mission is to build an AI-native public safety intelligence platform that predicts, prevents, and mitigates wildfire risk before ignition becomes catastrophe. We fuse satellite data, weather, vegetation analytics, and real-time signals to generate high-resolution ignition probability and actionable risk insights. Our goal is to empower insurers, utilities, governments, and emergency managers with forward-looking intelligence that improves underwriting precision, resource allocation, and community resilience. Interlinked exists to close the gap between emerging climate risk and decision-making — transforming wildfire response from reactive recovery to proactive prevention.

Wildfire risk is accelerating faster than the systems designed to manage it. Climate change, vegetation shifts, grid stress, and expanding development in wildland–urban interfaces have created a new risk environment where historical loss data alone is no longer sufficient. Traditional catastrophe models are primarily backward-looking, calibrated on historical burn perimeters and loss events. While valuable, they often struggle to capture dynamic ignition conditions in real time — particularly under extreme weather volatility. Interlinked exists to address this gap. Today, insurers, utilities, and public agencies face three structural challenges: Ignition uncertainty – Most models focus on spread and loss severity after ignition occurs. There is limited forward-looking intelligence on where and when ignitions are most likely to occur in the first place. Fragmented risk signals – Critical inputs such as satellite imagery, vegetation moisture, grid infrastructure data, and hyperlocal weather are siloed across systems and not continuously fused into decision-ready intelligence. Reactive workflows – Emergency response, underwriting adjustments, and risk mitigation strategies often activate after conditions deteriorate rather than before. Interlinked builds an AI-native ignition probability and dynamic risk modeling layer that operates upstream of catastrophe. By fusing satellite data, meteorology, vegetation stress indices, topography, infrastructure exposure, and real-time signals, we generate high-resolution, forward-looking wildfire risk intelligence. This enables stakeholders to move from static risk scoring to adaptive risk forecasting. For insurers, this improves underwriting precision, portfolio stress testing, and geographic exposure management. For utilities, it enhances pre-event staging and grid hardening decisions. For public agencies, it supports resource allocation, evacuation planning, and mitigation prioritization. For communities, it translates complex climate signals into actionable prevention. As wildfire seasons lengthen and volatility increases, reliance on historical baselines becomes progressively less reliable. Risk is no longer stationary. Decision-making frameworks must evolve accordingly. Interlinked is necessary because wildfire risk is no longer a retrospective problem — it is a dynamic systems problem. Addressing it requires continuous data fusion, predictive modeling, and operational translation into real-world action. Our mission is not to replace existing catastrophe models but to complement them with an upstream ignition intelligence layer. By identifying where ignition is probabilistically elevated before disaster unfolds, we help shift wildfire management from reactive loss response to proactive risk mitigation. In an era of escalating climate-driven extremes, building adaptive, forward-looking risk infrastructure is not optional. It is foundational to economic stability, insurance market resilience, and community safety.