The atmosphere
from orbit.
Meneltir Labs is building an end-to-end atmospheric intelligence system, coupling orbital sensing, AI-native modelling, and operational delivery into a single integrated architecture.
One integrated system
From observation to decision — one integrated system.
AI-native orbital platform
High-cadence atmospheric observations from a constellation designed for on-orbit inference. Built to deliver atmospheric state data at a cadence existing commercial and government sources do not provide.
AI-native atmospheric modelling
Neural architecture trained on decades of reanalysis data. Produces probabilistic forecast fields with calibrated uncertainty, rather than point predictions or ensemble means.
Decision intelligence API
Sector-specific risk quantification delivered through a unified API. For route optimisation, supply-chain resilience, and energy dispatch, the system delivers structured outputs for real operational decisions.
Weather is not a notification. It is the operating condition of the global economy.
The conditions for a new observation layer exist, for the first time.
Commercial launch costs have fallen sharply over the past decade. AI weather models are now competitive with ensemble NWP in selected forecasting regimes at a fraction of the compute cost. And the sectors that pay for forecast quality — insurance, shipping, and energy — are increasingly exposed to atmospheric risk.
Rideshare and reusability have made small-satellite constellation deployment commercially viable for the first time.
Neural weather models now rival ensemble NWP systems at a fraction of the compute and operational cost.
Insurance, shipping, and energy underwrite directly against forecast quality — and pay accordingly.
The observation-to-decision pipeline remains fragmented. No single provider owns the full stack from sensing to intelligence.
The team
Meneltir Labs is built by people who have done this before — in related domains, at scale, under real operational constraints.
Previously built and scaled an Earth observation company from inception through multiple funding rounds, turning satellite-derived machine learning into enterprise decision systems. At Meneltir Labs, he leads commercial strategy, capital formation, and strategic partnerships, converting technical differentiation into durable market position.
Applied geospatial systems architect with experience across agriculture, energy, water, and defence, where operational context and risk tolerance define what environmental data must deliver. At Meneltir Labs, he leads satellite systems engineering and on-orbit AI, designing an end-to-end platform built for real operational use.
Professor of Earth System Science at UCL, with over 200 publications and more than £75 million in research funding, focused on the dynamics of a non-stationary planet. At Meneltir Labs, he anchors the scientific architecture, ensuring the weather intelligence stack remains physically credible under changing climate conditions.
Professor of Computational Science at UCL and Honorary Fellow of the Alan Turing Institute, with a career building systems that make AI reliable at scale in scientific environments. At Meneltir Labs, he leads the AI architecture that turns advanced research into operational infrastructure across sensing, compute, and modelling.
Ready to build on atmospheric intelligence?
We're working with a small group of partners across energy, shipping, and insurance. If you're solving a hard weather problem, talk to us.
indrajit@meneltir.com