Noah.AI AI-Driven Astronomy for Research, Discovery & Exploration .
Intelligence Beyond Earth
At Noah.AI, we combine artificial intelligence, astrophysics and real-time space data to predict celestial events, discover new worlds and push the boundaries of human knowledge .
Intelligence
Without Internet.
Without Limits.
The first astronomical AI that operates entirely offline — no cloud dependency, no connectivity required, no data surveillance. Pure scientific intelligence, anywhere on Earth.
The Internet is not
a scientific right.
Intelligence should be.
Every major astronomical AI today requires a persistent internet connection. This creates a fundamental problem: the best observatories on Earth — at high altitude, in polar regions, in deserts — are precisely the places where internet connectivity is worst.
Noah.AI was built to solve this. Inspired by the offline-first architecture pioneered by Jack Dorsey's Block and the sovereign computing movement, we built an AI that carries its entire intelligence stack locally. No API calls. No cloud sync during operation. No data transmission. Just pure, local, hardware-accelerated scientific intelligence.
Scientific knowledge should not be mediated by data centres. Research data should remain under the researcher's control. And scientific capability should not be a privilege of institutions in bandwidth-rich environments.
Four pillars of
offline intelligence
Every component engineered to operate without cloud dependency — from AI inference to stellar database to sky renderer.
Online.
The Autonomous
Scientific Radar
Noah Djibril is not a search engine. It is a detection system — identifying scientific breakthroughs before they become mainstream, using the proprietary Noah Index scoring model.
It scans arXiv, PubMed, NASA ADS and IEEE continuously, scoring every publication on four dimensions: scientific impact, innovation level, methodological credibility and long-term relevance for humanity. Unlike algorithms driven by social engagement, Djibril prioritises scientific rigour.
Offline vs.
Cloud-Dependent AI
Four modules.
One offline universe.
Predict asteroid and comet trajectories with unparalleled accuracy using our optimised local AI. Neural architecture trained on 40 years of observational data — fully offline.
- Sub-arcsecond accuracy for 50-year trajectory forecasts
- Complete solar system ephemeris updated via delta-sync
- Gravitational perturbation modelling for close approaches
- Impact probability calculation for hazardous objects
- CUDA-accelerated: 40ms average query time
Model stellar evolution from protostellar collapse to endpoint. Simulate binary interactions, clusters and galactic dynamics in real-time.
- Full stellar evolution tracks for all spectral classes
- Binary and multiple system interaction modelling
- N-body solver for clusters up to 10 million particles
- Galactic merger simulation with dark matter component
- Real-time 3D visualisation — fully local GPU rendering
Never miss a significant astronomical event. Complete observable sky coverage — from exoplanet transits to gamma-ray burst alerts, all offline.
- Exoplanet transit predictions for 5,400+ confirmed planets
- Eclipse and occultation calendar to 2100 CE
- Meteor shower intensity forecasting by location
- Geomagnetic storm and auroral activity prediction
- Custom alert thresholds and telescope scheduling integration
Explore the entire observable universe from any location, at any moment in time — with no network connection, no tile streaming, no cloud rendering.
- 5.5 million deep sky objects rendered locally
- Time travel: 10,000 BCE to 10,000 CE
- Constellation traditions from 200+ cultures worldwide
- Atmospheric refraction modelling for horizon events
- Telescope field-of-view overlay for 400+ instruments
Used where the
internet can't reach.
Intelligence should
not need permission.
We are building the AI that works everywhere science happens — at the summit of a mountain, on the ice of Antarctica, aboard a vessel in the Southern Ocean, in a classroom with no wifi. Offline, sovereign, unlimited.
