We build the Large Bio-Vision Model. Instead of beaming down raw imagery, we deploy encoders in orbit and run decoders on Earth, equipping every robot, vehicle, and drone with the most spatially aware real-time intelligence in the world.
Every intelligent agent in the real world needs spatial awareness. An autonomous car navigating a city, a drone surveying a grid, a robot working inside a facility. Today, that awareness is stuck behind the old model of Earth observation: heavy raw images, ground stations, slow downlinks. It's expensive, and most of the bandwidth is wasted.
We're building the foundation for data centres in space. Instead of transmitting billions of pixels back to Earth, the LBVM processes what it sees from orbit. We put visual encoders directly onto space architecture, compressing the physical world into dense, semantic representations right at the source.
Those compressed representations get beamed down to decoders running on edge devices. A robot doesn't need a photo from space. It needs the meaning of its surroundings. Encoders in orbit, decoders on devices. That split is what makes machines on Earth dramatically more intelligent about the world they operate in.
Raw satellite feeds are a thing of the past. We put the LBVM's vision encoders directly into space-based data centres, processing Earth observation where it happens. Only the essential intelligence comes down to the surface.
An autonomous vehicle or mobile agent runs a lightweight decoder that turns orbital latents into immediate spatial awareness. The device understands its broader environment, filling in the context that ground-level sensors can't provide on their own.
The LBVM is a Joint Embedding Predictive Architecture. It learns representations of the physical world not by reconstructing pixels, but by predicting what comes next in representation space. It takes in everything: satellite, CCTV, drone, mobile, telematics, text, architectural layout.
The old way of doing Earth observation, bouncing heavy image files through ground stations, is too slow for real-time AI. The Large Bio-Vision Model works across that divide. It encodes spatial intelligence in orbital data centres and decodes it on edge devices. Once that neural link is live, machines on the ground get dramatically smarter about the planet they're operating on.
Guided synthetic satellite imagery is the training signal that gives downstream AI systems an understanding of space, context, and change they could never acquire from ground-level data alone. These are the machines that inherit the view from above.
Self-driving systems trained on guided synthetic overhead views build richer spatial priors. They learn road topology, intersection geometry, and terrain from perspectives grounded in real satellite observations.
Delivery robots, warehouse bots, and industrial arms gain spatial awareness that goes beyond their onboard sensors. They pick up facility layout and terrain context from synthetic orbital pretraining anchored in real observation.
Drones surveying pipelines, farmland, or disaster zones navigate with models that already know the terrain. They were trained on synthetic views guided by actual satellite passes of the area.
Commercial and military aircraft use synthetic terrain awareness for approach planning and situational understanding, especially in GPS-denied or low-visibility environments.
Guided synthetic imagery provides training data for classifying installations, detecting change, and maintaining persistent surveillance awareness without depending on tasked real satellite passes.
Smartphones and edge devices run lightweight models distilled from the LBVM, enabling on-device spatial reasoning for navigation, AR, environmental sensing, and location intelligence.
Factory automation systems use synthetic aerial views grounded in real site imagery to understand facility topology, monitor changes, optimize logistics, and spot structural anomalies.
Precision agriculture models trained on guided synthetic multispectral imagery monitor crop health, predict yield, track deforestation, and detect environmental change at planetary scale.
Satellites, CCTV, drones, mobile cameras, internal files, telematics, architectural layouts. Ingest through API or Model Context Protocol (MCP).
No SQL. No GIS expertise. Ask "What changed in sector 7 this week?" or "Show me anomalies near the eastern perimeter" and the LBVM reasons over your data.
The model doesn't just describe. It forecasts. JEPA-based prediction produces future-state estimates, anomaly alerts, and trajectory projections before events occur.
geo.qa is the interface to the Large Bio-Vision Model. Connect real sensors. Generate guided synthetic imagery. Train ground-level AI. Query any place on Earth.
