We are Meneltir Labs: a research-driven team building planetary-scale weather intelligence powered by novel, AI-native satellites and state-of-the-art machine learning. Our goal is simple and testable—to turn raw, global signals into timely, trustworthy guidance that improves decisions in shipping, energy, insurance, agriculture, and public safety. We work like a modern research group: co-designing sensing and models, publishing methods and benchmarks, and validating results with partners who run real operations. The output is not a dashboard for show; it is an evidence-backed stream of forecasts and risk indicators that people can use with confidence.
Meneltir unifies observation, modelling, and decision support. Our novel satellites are engineered to observe what matters for high-impact weather and to run inference on-orbit, reducing latency and bandwidth while increasing reliability. On the ground, we fuse these observations with authoritative external datasets and probabilistic, spatio-temporal ML to produce consistent global fields—precipitation structure, temperature and heat indices, wind and gust risk, and short-range evolution. Everything is delivered through stable APIs, SDKs, and applications that integrate with routing, grid, claims, and emergency-management systems. We emphasise calibration and interpretability throughout: models are scored on open benchmarks, uncertainty is quantified, and every alert can be traced to the evidence that generated it.
Weather forecasting is undergoing a revolution. For decades, numerical models have dominated—vast simulations running on some of the largest supercomputers ever built. But they are slow, expensive, and limited by the very gaps in data that Meneltir is designed to fill. A new wave of machine learning models, from Aardvark to Aurora, has shown that data-driven methods can rival and even surpass physics-based forecasts.Meneltir is uniquely positioned at the intersection of these two revolutions: we are not only building an orbital sensor network that closes the data gap, but also designing AI systems that learn directly from it. By fusing harmonized multi-sensor observations with end-to-end, physics-informed learning—self-supervised pretraining on radiances, differentiable data assimilation, and neural-operator cores—we deliver rapid, calibrated forecasts that sharpen as the constellation densifies.
This is not just an upgrade. It is a step change in how humanity sees the skies.
Our team is made up of satellite engineers, atmospheric scientists and AI researchers who have designed and operated systems at planetary scale. Collectively we have led pioneering work in orbital system design, frontier machine learning, climate science and large‑scale AI architecture at institutions like UCL and the Alan Turing Institute as well as in industry . We founded Meneltir because we see the data gap in weather intelligence as a solvable engineering problem and believe we are uniquely positioned to solve it.Patrick Newton is co‑founder and CEO. As the founding chief executive of Rezatec, he commercialised satellite data and machine learning for infrastructure monitoring and grew the company from inception to scale. That track record of building and leading deep‑tech companies anchors Meneltir’s commercial and strategic direction.Indrajit Pawar is Co‑founder & CTO. An applied geospatial technologist with hands‑on experience across agriculture, forestry, water, energy, and defence, he brings a cross‑sector perspective rarely found in satellite AI — the recognition that an orbital signal carries different meaning depending on who is making the decision and what is at stake. At Meneltir he leads satellite systems engineering and on‑orbit AI, turning that breadth into a planetary sensing platform built for real operational contexts.Mark Maslin is co‑founder and director of climate science. A professor of Earth system science at UCL with more than 200 published papers and £75 million in research funding, he provides the authoritative understanding of planetary systems and the Anthropocene that underpins Meneltir’s scientific approach.James Hetherington is co‑founder and director of AI. An honorary fellow of the Alan Turing Institute and former director of research engineering there, he has devoted his career to building the tools and systems that make AI work at scale in scientific environments. His expertise in turning cutting‑edge research into robust, scalable infrastructure is core to the engineering challenge Meneltir is tackling.
This team is research-engineering, not research-theater. We write the forward models we need, the flight code we can sleep on, and the ablations that close the gap between a paper figure and an operational timeseries. We value slope over pedigree, clear writing over cleverness, and reproducibility over conference applause. You’ll see thermal budgets, RF chains, compiler flags, and reliability plots in the same review. You’ll ship something that orbits and something that publishes, and both will survive contact with reality.If you want a lab in orbit, if you care about microphysical truth and operational impact in the same breath, if you believe the right abstraction boundary between physics and learning is built—not debated—come build it here. Professors with groups to co-lead retrievals and DA; postdocs who want to turn a method into an instrument; engineers who know why an interrupt storm ruins a pass; statisticians who wake up thinking about calibration curves—this is your invitation.
The age of blind spots ends when we decide to measure them.
indrajit [at] meneltir [dot] com
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