Our 2030 Vision.
Autonomous Payload and Crew Transport — over the edge of space.
ASTRID (Advanced Suborbital Transport for Rapid Intercontinental Delivery) is our autonomous lifting-body spaceplane that fits inside commercial launch providers' fairings — or launches with its own propulsion — in SSTO (Single-Stage-to-Orbit) configuration.
So this is the full stack for the Orch Pathfinder model.
Rapid cargo delivery
Instead of a capsule's parachute-and-splashdown impact, ASTRID glides to a soft runway landing under gentle g-forces — delivering time-critical cargo intact. Built for airliner-level reuse: land, refuel, fly again. Cargo delivered whole — without the risk of damage or destruction in transit.
Propulsion
For simplicity and reliability, ASTRID's propulsion runs on storable hypergolic propellant — it ignites on contact, with no complex ignition system.
Project ASTRID is an experimental airframe under active development; figures are engineering estimates, not commercial schedules. “ASTRID” refers to Orchestr Aerospace’s lifting-body program and is unrelated to any similarly named aircraft or product.
AVIONICS
Alternative to ADS-B and GPS, Vision.
ML-based Traffic Detection and Navigation System.
ADS-B depends on GPS. With jamming on the rise in conflict zones worldwide, traffic awareness built only on transponders and GPS has a single point of failure. In a country like Canada, radar coverage ends not far north of the populated band, and space-based ADS-B carries surveillance the rest of the way. It closes the coverage gap, not the dependency: the position relayed from orbit is still the one the aircraft's own GPS computed.
Our computer vision detection runs on the Jetson Orin Nano, with stereo and infrared camera modules. It identifies aircraft, helicopters, drones, birds, and ground terrain in real time. Stereo depth estimates range in meters. Infrared keeps it seeing at night and through haze, when the eye out the window is least reliable. Tested on C-FPIO, our Piper Cherokee avionics system testbed.
Detection is the first half. We are building a vision–language–action model on the same stereo and infrared feeds: rather than only naming what it sees, it reads the scene in language — traffic converging, runway occupied, lined up on the wrong surface — and proposes the action. The pilot in command accepts it or ignores it. It advises; it does not fly the aircraft.
Navigation apps for Web, iOS & Android
The full Flight Computer 1 in your flight bag — 3D terrain map, international weather radar, Orch Vision AHRS, and route planning, on iOS and Android. Pairs with an existing Sentry® or any qualifying external ADS-B device like Stratux for live traffic and weather.
Orch Aerospace is not affiliated with Sentry or Stratux. Sentry® is a registered trademark of its respective owner; Stratux is an independent open-source project.
ATC Intelligence
Bringing LLMs toAir Traffic Control.
As the skies grow more congested, we bring the current power of large language models (LLMs) from machine learning to the field of air traffic control.
Live VHF radio demo.
Toronto Centre, Air Canada 510 — in the Detroit area,
World Model1
Visual positioningwithout GPS.
We are training a world model that fixes an aircraft’s position the way a person would guess a location from a street-level photo — except it looks out of the aircraft, at the terrain, the coastline, the cloud deck, the sky, and at night, the stars. 360° cameras and infrared vision go in; latitude, longitude, altitude and heading come out. No GPS. No inertial reference. No magnetic drift. Nothing to jam or spoof.
How it works
Capture
A 360° camera ring and a long-wave infrared sensor record the full surroundings of the aircraft, day and night, in and above weather.
Infer
The model has learned what the Earth looks like from the air — ridgelines, rivers, road networks, city light, the sun’s position, the star field. From one frame it estimates where the aircraft is, and from a sequence, how it is moving.
Verify
Every fix carries a confidence and an error radius. It cross-checks against the other sensors on board and flags when the world and the map disagree.
1A world model is a neural network that learns an internal picture of how the world looks and behaves, so it can predict what it should see from a given place, time and viewpoint. Ours is trained on the Earth as seen from the air. It compares what the cameras see with what it expects to see, and the place where the two agree is the aircraft’s position.
Research track. Trained on footage from our own flight-test aircraft, Piper Astrid. US provisional patent filed. Advisory only — not a certified navigation source.
Brain–Computer Interface
OpticALLY, 3D head scan for a custom-fit head interface
Our iOS app uses the iPhone’s TrueDepth / LiDAR sensors to build a real-time 3D model of your head, exportable as OBJ — so we can design a custom-fit head interface for each pilot who trains in the simulator.
Next-Gen Checklist.
The checklist app for ASTRID. It walks a crew — or the team on the ground for an uncrewed flight — through every step of running the spaceplane, from pre-flight to landing, on an explorable 3-D cockpit you can look around.
Normal & emergency checklists, approach procedure guides, FMGC operations reference, full POH data, and detailed instrument system guides.
328 pilots worldwide using the app, as of August 2026.
How we train our pilots, on Apple Vision Pro
A Level D simulator is the highest-fidelity trainer there is, and as far as we know none exists for a vehicle that leaves the atmosphere — no motion platform reproduces a climb past the Kármán line, re-entry, and a runway landing in one box. So we are building it differently. Apple Vision Pro renders the cockpit at 1:1, and a vestibular stimulation approach we are developing at DEEPBCI® is intended to supply the sensation of motion and g-force. That part is early research, not a finished trainer. If it works, it costs a fraction of a Level D simulator and saves us building a second test aircraft to train on — the real airframe stays on the ground, unworn and out of harm’s way — and it applies both to remote operation of the ASTRID spaceplane and to the crewed configurations we plan next.
Inquiries
ORCHESTR AEROSPACE INC
DEEPBCI, INC
contact@orchestrsim.com