ENFRES

DRONES · AEROSPACE · SPACE

Bring intelligence onboard.
Keep the reasoning visible.

Turn onboard measurements into useful categories and inspectable evidence. Work with Celeste EcoFlyers to develop an embedded Neurytika integration around your equipment, data and mission.

See the 60-second example →

An integration workflow

From telemetry to an explained result

01 / MEASURESensor & telemetry records02 / PREPAREUseful features & known states03 / CLASSIFYERC model on your target computer04 / EXPLAINA category, with supporting evidence
Illustrative integration architecture

THREE PRACTICAL STARTING POINTS

Make each measurement useful.

Drone & aircraft equipment health

Use battery, propulsion or vibration features to classify recorded operating states. Give maintenance and engineering teams the contributing signals behind an inspection priority.

Example: identify a battery operating-state category from temperature, voltage and current summaries paired with historical inspection labels.

Satellite telemetry triage

Classify telemetry windows into defined system-state categories and attach the observed evidence. Help operators focus their review on the events that match their mission priorities.

Example: route a power-subsystem event to the right engineering review using prepared electrical and thermal features.

Payload data & event prioritization

Classify prepared measurement records by quality or operational interest. Make the criteria behind a record’s priority available to the mission team and its data workflow.

Example: prioritize records for engineering review or transmission using known observation-quality labels.

A CLEAR DIRECTION FOR THE SECTOR

Public programmes show the need.

Onboard assessment, interpretable models and greater autonomy are already concrete research and engineering priorities.

NASA / JPL — OUTLAST

JPL developed onboard health classification from flight telemetry and event logs for future autonomous multi-flight operations. Its OUTLAST software flew aboard Ingenuity in January 2024.

Read the JPL project ↗

ESA / Airbus — ORBIT-STAR

ESA describes an Airbus demonstrator that analyses Columbus subsystem telemetry for fault detection and prognosis. Onboard execution is identified as a next step for future missions.

Read the ESA programme ↗

WHY NEURYTIKA

An explained category. A practical integration.

ERC learns combinations of structured features associated with target classes. This provides an engineering basis for inspecting the evidence behind a classification and discussing it with domain experts.

Develop the model with representative historical data, then engineer and measure the inference integration on your chosen onboard computer. The pilot defines memory, processing time, interfaces and the operator’s use of each result.

Read the open-access ERC paper ↗

YOUR EMBEDDED PILOT

From mission objective to target hardware.

01

Define the useful category

Choose one equipment state or event category, its user and the moment when the result is needed. Map sensors, prepared features and historical labels.

02

Evaluate on mission data

Compare the model with your current approach on reserved historical records. Review category quality, useful evidence and operational acceptance criteria with your engineers.

03

Integrate & measure onboard

Scope target-side inference, data interfaces and monitoring. Measure resource use and processing time on your selected computer, then define the validation and deployment plan for the intended function.

Aerospace experience. Explainable AI expertise.

Celeste EcoFlyers brings together aerospace projects and the development of Neurytika. Talk to the team about your drone, aircraft equipment or space-system monitoring project.

Tell us about your onboard project.

Share the equipment, the category you want to predict, available historical data and your target computer. We will shape a focused feasibility and integration proposal with your team.

Discuss scope & demonstration ↗