AI for Aerospace

Machine learning applied where it earns its place: maintenance, inspection, autonomy and operational decisions.

Applied, not decorative

Aerospace generates enormous quantities of operational data and uses comparatively little of it. The useful AI work in this industry is rarely a novel model architecture; it is getting sensor data into a usable state, framing the problem so a prediction actually changes a decision, and earning enough trust that someone acts on the output.

We build systems where the model is one component of a working pipeline, with the data engineering and the human workflow treated as first-class parts of the problem.

Computer vision Time-series Anomaly detection Sensor fusion Edge inference

Predictive maintenanceComponent degradation and failure-pattern models built on sensor and maintenance history, surfaced into the planning workflow
Automated inspectionVision models that flag corrosion, cracking and structural defects in captured imagery, with human review kept in the loop
Autonomy supportPerception and decision models for obstacle avoidance, terrain awareness and mission adaptation in flight
Operational optimisationFuel, routing and scheduling models that account for the constraints operations teams actually work under
Data platform workThe ingestion, cleaning and labelling groundwork that determines whether any of the above is possible

A note on trust

A model that cannot explain itself will not be used for a decision that carries safety weight, and it should not be. We favour approaches whose behaviour can be inspected, bound the conditions under which a model is considered valid, and design for the case where it is wrong.

Where a system influences airworthiness or flight safety, the assurance argument matters more than the accuracy figure.

Have an aerospace programme in mind?

Tell us what you are building. We will tell you honestly whether we are the right team for it.

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