Resilience in Review for Lessons from 2024 and Strategies for 2025 - Strategize, Anticipate,Act
Know More – Know Sooner – Where It Matters Most.
Imagine standing on the edge of a vast, unpredictable ocean—the weather is changing, storms, droughts, and heat threaten your crops, and the tide of risk feels overwhelming. You need a map to navigate, a compass to guide, and more importantly, a way to look beyond the horizon. That’s what our platform is designed to do—provide the granularity and accuracy needed in order to make better decisions.
Think of our technology as a high-performance engine, fine-tuned to break through the haze of uncertainty. Traditional weather models rely on single data sources, but our platform is built on multiple global models and an unparalleled scale of data ingestion. This means we don’t just predict the weather; we map it, layering insights from across the globe to provide a more precise, nuanced view of what’s coming.
At the core is our proprietary biophysics-informed machine learning and artificial intelligence—a smart system that adapts and learns increasing the accuracy and resolution of ingested forecasts using deep generative models. Our 1km x 1km downscaled global resolution, ensures that you’re not getting just general forecasts, but hyperlocal predictions tailored to your assets/crops, your region, and your business down to the field-level.
And to build trust? We don’t just stop at gathering data. Rigorous testing and validation processes are at the heart of what we do, so every insight you receive is as reliable as the sunrise. This continuous refinement means we don’t just promise accuracy—we deliver it.
Having the right data is one thing; knowing what to do with it is another. We bring a blend of deep agricultural, climate, hydrological, data science, and engineering expertise to the table—each perspective sharpening the view, each insight grounded in experience.
Where some stop at numbers, we go further, connecting our AI-driven climate and weather models directly to business value. This is made possible through a proprietary library of asset-specific risks—think of it as a translator that converts raw data into actionable strategies tailored to your industry.
And unlike one-size-fits-all solutions, our platform is built for you—with a dedicated focus on Food & Agriculture and the Industrial supply chain. Whether you’re managing crop productivity, risk management, or raw material procurement, we provide insights that are deeply relevant and immediately impactful.
It’s not just about knowing what’s coming—it’s about knowing what to do next. Our solution offers immediate visibility into volatility across your entire supply chain, with dynamic decision tools, alerts, and data-sharing functionalities that keep you a step ahead of potential risks.
With industry-leading insights at your fingertips, you can make decisions faster, mitigate risks proactively, and maintain an edge in both speed to market and sustained competitive advantage—not just for this season but for years and decades to come.
When you’re armed with real-time intelligence, your business isn’t reacting to the weather; it’s forecasting opportunity. And that’s what sets us apart—taking the guesswork out of risk, so you can focus on growth.
Dynamic, ML-based ensembling of dozens of forecast models calibrated to ground truth to create a unique, optimized model for every point on earth
AI-integration of novel data points such as oceanic temperatures or topographical features to better capture extreme events (hurricanes, droughts, etc.)
Industry validated models covering dozens of crops and countries – validated by decades of backtesting and 50+ players across the Food & Ag value chain
One big challenge for the climate modelling sector is using data from a Global Climate Model (GCM), low resolution model, and trying to apply it at a local level to fill the growing need for risk analysis and resilience planning. Our data science team has succeeded in delivering High-Resolution (1km) outputs that are physically and scientifically accurate – accurate “on the ground” data, not pure extrapolation – to achieve substantially improved precision in complex regions.
Transparency around the skill of our models is critical to building trust with our clients, and we are committed to providing clear explanations of those models and the methods we use to develop them. We provide clients with access to performance measures of our models through a process called “hindcasting.”
Hindcasting, also known as retrospective forecasting, is a technique used in weather and climate forecasting to test the accuracy of a model by applying it to past weather and climate events. This technique allows forecasters to test the model’s accuracy and compare its predictions to the actual weather conditions that occurred in the past. In addition to aiding in our own developmental benchmarking, these hindcasts help our customers understand the value-add of our forecasts for their specific use case.
In this short video, Dave explains how ClimateAi's models performed when forecasting Heat and Low Precipitation for Mato Grasso.
In this short video, Dave explains how using personal weather stations can help to downscale probabilistic forecasts and the improvements we've seen in working with our partners' weather stations.
ClimateAi's heat forecast showed the impending heat event weeks ahead - better than any weather forecast. Our 6-month outlooks have shown to be 50-60% more accurate than historical data.
We are happy to walk you through our platform and methodologies on a quick call.