predict_everything()█
Forecast the coupled world, so that the institutions of free societies can foresee risk, compete productively, and secure a bright future for humanity.
cat ./intro.txt
Weather, energy, food, markets, geopolitics, and security arenas don't exist in isolation -- they move together and influence eachother, but today's forecasting systems don't treat them like that.
We're changing that.
Aulendur Labs builds tools to enhance predictions in your domain using signals from others.
Why now?
The technology finally exists. Planetary-scale graph neural networks (GNNs), highly scalable geospatial storage engines, and modern multi-domain machine learning training strategies make cross-domain forecasting possible like never before.
Who do you build for?
Our tools are built to field in real environments. We build services for businesses, governments, and defense alike, with an emphasis on deep domain trust, cleared personnel, and on-premises deployment readiness.
bash ./deeploom.sh
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DeepLoom is a planetary-scale model for cross-domain forecasting. DeepLoom is a heterogeneous graph neural network that unifies spatial nodes (planetary physics) with entity nodes (infrastructure and economics). It's designed to learn the interactions classic models miss, producing auditable, multi-domain forecasts that anticipate cascading interactions.
bash ./weavecast.sh
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The WeaveCast Platform is the gateway to DeepLoom insights: one auth, uniform interaction schemas, and forecast layers for all usecases. Receive auditable, multi-domain outputs or inject your own data and integrate new domains.
bash ./more.sh
See our full lineup of projects and experiments on our showcase.