ORION / Vision AI lifecycle platform

Build vision models
for your mission.

Prepare and generate data, test vision models and VLMs, and automate fine-tuning.

See ORION in actionFollow one example

Operational Readiness through Intelligent Optimization

Yrikka vehicle-detection example in night conditions.
EXPAND COVERAGENIGHT
Yrikka vehicle-detection example in fog conditions.
EXPOSE FAILURE MODESFOG
Yrikka vehicle-detection example in clear conditions.
ADAPT TO THE MISSIONCLEAR

ONE MISSION. EVERY CONDITION.

One example, start to finish

Follow one model through ORION.

A drone camera has to find a truck in a forest. The detector works on a clear day. Here is how ORION takes it from one clear-day image to a model that still finds the truck in snow.

  1. 01 · DataStart with what you have.One clear-day image of the target, with its label. Bring your own images or a public dataset.
  2. 02 · GenerateCreate the conditions you're missing.ORION's Data Engine renders the same scene in fog, night, rain and snow. Labels carry over.
  3. 03 · TestFind where the model fails.Run your detector on every condition. It misses the truck in fog and in snow.
  4. 04 · AdaptFix the failure.Generate more snow scenes and fine-tune the model automatically, with agentic training.
  5. 05 · RetestConfirm it works.The adapted model finds the truck in snow. Repeat for fog, night or a new target.
01 · DataStart with what you have.
Aerial view of a red pickup truck on a forest track on a clear day, with its label box. truckyour label Your data · 1 image · clear day
Your imagesPublic datasets (e.g. Hugging Face)Auto-annotate new classes
02 · GenerateCreate the conditions you're missing.
Same scene, clear day. Same scene generated in dense fog. Same scene generated at night. Same scene generated in rain. Same scene generated in heavy snow. trucklabel carried over > same scene, clear day Generated with ORION Data Engine
ClearFogNightRainSnow
03 · TestFind where the model fails.
Clear: truck detected, confidence 0.56.Detected0.56
Clear
Fog: truck missed, confidence 0.04.Missed0.04
Fog
Night: truck detected, confidence 0.69.Detected0.69
Night
Rain: truck detected, confidence 0.61.Detected0.61
Rain
Snow: truck missed, confidence 0.13.Missed0.13
Snow
2 of 5 conditions fail.Fog and snow. Clear, night and rain pass.
04 · AdaptFix the failure.
1 · Generate targeted data
Snow scenes generated for training.

More snow scenes with the truck, labeled automatically.

2 · Fine-tune
Your modelor open weights
  1. Train
  2. Evaluate
  3. Refine

An AI agent runs the training loop and keeps the best version.

05 · RetestConfirm it works.
Before fine-tuning: truck missed in snow, confidence 0.13.Missed0.13
Before
After fine-tuning: truck detected in snow.Detectedafter fine-tuning
After
Next: fogNew targetNew sensorRepeat the loop

The same loop works for other targets and sensors. See it with thermal video for counter-UAS and sensor adaptation. Every step is also available through the APEX API.

Next: test the whole autonomy stack with ATLAS →

Let’s put ORION to work

Bring us your mission.
We’ll show you the workflow.

Walk through your data, model, and operational challenge with the team building ORION and ATLAS.

Book a technical demo