Flight telemetry
ADS-B, GPS and time-stamped surface and airborne trajectories.
Autonomous aviation · Pilot insight · Real-world data
The Data Layer forany flying object.
We teach autonomous aviation models what it feels like to be human—and how to make a decision when every data point is against you.
CH-01
Flight telemetry
GPS · ADS-B · timestamp
CH-02
Wind field
vector · gust · shear
CH-03
Aircraft state
configuration · energy · propulsion
CH-04
Runway state
selection · occupancy · NOTAM
CH-05
Atmospherics
turbulence · visibility · ceiling
CH-06
Operational outcome
touchdown · go-around · deviation
CH-07
Illustrative sampleHuman sensory
Synchronized biometric context · cognition · attention · workload
EEG alpha
9.8 Hz
Pulse
82 BPM
HRV
46 ms
EDA
4.2 µS
Gaze forward
78%
Workload
0.64
The master plan
Autonomy learns from people.We capture the judgment pilots bring when signals conflict, conditions change and the safest choice is not obvious.
Our mission
Making the sky a safe space for all humankind.
Autonomy starts with real-world pilot data.
Autonomous aircraft must understand more than routes and coordinates. They need to learn from real flights, changing conditions and the decisions behind safe outcomes. Simulation can start the journey. Real aviation data helps make autonomy ready for operation.
3–5local airports
20–30participating aircraft
2,000+target flight records
Data types
Every flight builds intelligence.
We synchronize machine data with the pilot judgment behind operational decisions, capturing not only what happened, but why the safest action was chosen.
Human sensory data
Optional biometric context captures how pilots perceive, process and respond to operational pressure.
EEG Pulse Heart rate Eye tracking Workload
Aircraft state
Avionics, configuration, propulsion, energy and performance parameters.
Pilot judgment
The decision made, why it changed, and the pilot’s assessment of the safest action.
Communications
Legally collected ATC, ground and CTAF context aligned to each flight.
Operating conditions
Weather, visibility, wind, traffic, runway status and NOTAMs.
Outcomes
Go-arounds, deviations, interventions and normal flight completion.
Decision intelligence model
From flight conditions to a learnable outcome.
Each record preserves the chain between what was happening, how a pilot understood it, the threshold that changed the decision, and what happened next.
01
Situation
Weather, traffic, runway state and operational constraints establish the scene.
02
Observable aircraft state
Trajectory, energy, configuration and control state show what the aircraft is doing.
03
Pilot interpretation
The pilot identifies the threat, weighs uncertainty and explains what the signals mean.
04
Decision boundary
A defined threshold changes which available option carries the safest margin.
05
Action
Continue, correct, intervene, go around or divert—the decision becomes observable.
06
Operational outcome
The resulting trajectory, stability and safety margin close the learning record.
Multimodal flight recordHuman judgment becomes structured training data.Decision-labelled outcome
A lightweight field program
A network built around the way you fly.
Connect
Start with the equipment already onboard: ADS-B, EFB tracks, GPS and supported avionics exports.
Fly
Operate normally. Collection runs in the background without changing cockpit procedures or giving live guidance.
Contextualize
After selected events, answer one or two short questions that capture intent and operational context.
Who should participate
Your everyday flights can train tomorrow's aircraft.
We are building the first network with practical partners who understand local aviation, real operational decisions and the conditions behind every flight.
Private aircraft
Owners and pilots
Contribute real flight movement and short post-flight context using the equipment and workflows you already have.
Apply as a pilot
Small aviation companies
Charter and jet operators
Turn routine operations into structured training records while receiving useful safety and fleet-level insights.
Apply as an operator
Local infrastructure
Airports and flight schools
Add runway, weather, traffic and local operating context that helps autonomous systems understand the complete environment.
Apply as an airportValue returned
Your data should work for you, too.
Partners receive useful operational intelligence, not just a request to export data.
- Confidential operational and safety insights
- Automated, data-informed post-flight debriefs
- Aggregated and de-identified peer benchmarks
- Support for Safety Management System reviews
- Subsidized collection equipment where needed
- Early participation in autonomous aviation AI validation
Earn from your experience
Earn up to $800USD per hour of collected flight data and pilot judgment.
Compensation varies by aircraft, data types, record completeness and current program openings. Apply early to be considered.
De-identified by design
Personal and unnecessary aircraft identifiers are separated before model training.
Non-punitive participation
Pilot contributions are for safety learning, never employment evaluation or discipline.
Clear data rights
Partners know what is collected, how it is used, how long it is retained, and what they receive back.
Become a data partner
Help build the intelligence behind autonomous flight.
Private-aircraft owners, jet operators, charter companies, flight departments and local airports can help train safer autonomous air taxis and flying cars.
No commitment by applying
Safeguards agreed before collection
Why now
One day, everything will be autonomous.The sky is the limit.
The next decade will bring more intelligence into the sky. To make autonomy ready for real operations, aircraft must learn from real conditions, real decisions and the pilots who navigate them. That learning begins with the flights happening today.