geo.qa — a Vortx product
Satellites watch from orbit. Cameras and dashcams watch from the ground. The Large Bio-Vision Model compresses both into one latent bridge, and an LLM reads from it. That's geo.qa — geographical question answers, grounded in what the world actually looked like.
Satellites × ground sensors
Satellites see every place, weekly. Cameras and dashcams see one place, every second. LBVM joins them in one latent space — so an answer about a Doha concourse, a London junction, or a Jakarta flood uses both the wide view and the close-up, on the same question, in the same second.
01 · The latent bridge
LBVM runs encoders next to the sensor — sometimes literally on the satellite, sometimes at the edge of a camera network. Only latents come down the pipe. That's the bridge: the world as a dense semantic vector any adapter can plug into — your LLM, your agent, your alerting rule, all reading the same compressed reality.
02 · Fusion creates leverage
A satellite pass gives you coverage; a dashcam gives you ground truth; a CCTV gate gives you the moment it matters. Joined in the same memory, they unlock automations a single stream never could — route re-planning before a jam forms, an apron alert before a delay propagates, a crowd flag before a gate overflows.
03 · Predict, don't guess
The predictor is a joint-embedding architecture. It rolls the world forward one step in the compressed space and flags where the next real observation diverges from that forecast. That's what a real anomaly alert is: a place where the world surprised the model.
The view from above changes everything below. Built on open data (Sentinel-1/2, Landsat, MODIS) and open architectures (JEPA, Tessera). Deployed as managed, private cloud, on-prem, or air-gapped. Used in airports, stadiums, pilgrimage sites, ports, malls, and emergency operations across the Gulf, Europe, North America, and South Asia.
How to use it
Point us at a place. Drop a satellite polygon, paste an RTSP stream, upload a dashcam archive. The world model picks up whatever you give it and starts forming a memory of that location within minutes.
Type a question in plain language, in any of ten languages. The LLM reads from the world model's memory — the imagery, the detections, the baselines — and replies with receipts attached.
Turn answers into automations. Webhooks, MCP tools, and agent actions fire when the model sees something worth reacting to — a crowd surge, a delayed apron, a perimeter breach. No custom rules to maintain.
Where it runs today
The same world model answers for a gate, a plaza, a container yard, or a flood zone. Each site gets its own scene memory; the architecture underneath is shared.
Apron movement, gate congestion, baggage-belt rhythm. Forecasts a delay before the tower sees it.
Crowd density at the gates, parking fill-rate, post-match dispersal. Spots the line that's about to break.
Concourse footfall, lot utilisation, late-night anomalies. The baseline learns your hours without being told.
Container moves, berth occupancy, yard stacking. Merges satellite passes with crane-cam feeds in one view.
Gate flow, plaza density, route shaping during peak days. Surfaces bottlenecks early enough to re-route.
Flood extent, evacuation corridors, damage triage. Live imagery stitched to historical memory the moment it matters.
Perimeter watch, vehicle provenance, night-shift anomalies. Edge-deployable for air-gapped operations.
Traffic rhythm, transit on-time, event-day routing. Learns the commute before it breaks, not after.
One model of the world — fed by satellites from orbit, cameras on the ground, drones in between, and dashcams on the move. Every live question hits it. Every alert cites it.
Satellite passes every few days. Camera streams every few seconds. The world model reconciles both into one live picture of your sites.
Agents, webhooks, and MCP tools read from the same latent bridge a human does. Your automations act the moment the world changes.
Works over Dubai or Dakar, Heathrow or Haneda, Mecca or Manhattan. One API, one answer format, global coverage.
Managed, private cloud, on-prem, or air-gapped. Edge inference available. Stays inside your perimeter when the contract says so.
"Three dashboards became one chat window. Our night-shift ops ask the model what's off — it replies with the imagery, a map pin, and a timestamp."
Dheeraj K.
Staff engineer, Gulf telecom
"The JEPA predictor catches apron congestion a day before the tower does. One averted delay per week is the whole contract."
Samantha L.
ML lead, airport group
Paste coordinates, drop a stream URL, or just type a question. The model starts learning your sites within minutes.
The view from above changes everything below.