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Build, Train & DeployAI for Every Edge Device

Collect sensor data, build production-ready AI models, optimize them for embedded hardware, deploy to edge devices, and monitor them in real time — all from one intelligent platform.

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Enterprise Ready
GPU Training
Open Source Compatible

Petal Edge StudioLIVE

The Platform

One Platform. Endless AI Possibilities.

Every stage of the edge AI lifecycle — from raw signal to monitored fleet — lives in a single, connected workflow.

0110

01Stage 1 of 10
Collect data
Upload samples or import an existing folder tree.
Data
Data01
02Stage 2 of 10
Label
Draw boxes by hand, or start from AI-assisted suggestions.
Dataset editor
Dataset editor02
03Stage 3 of 10
Design impulse
Pair the image processing block with a learning block.
Impulse designer
Impulse designer03
04Stage 4 of 10
Generate features
Process the dataset once and reuse the features.
Feature generation
Feature generation04
05Stage 5 of 10
Train
NanoVision, Vision Pro, MobileNetV2 or transfer learning.
Training
Training05
06Stage 6 of 10
Evaluate
Confusion matrix and per-class metrics for the run.
Evaluation
Evaluation06
07Stage 7 of 10
Test the model
Score the test set and compare model versions.
Model testing
Model testing07
08Stage 8 of 10
Post-process
Threshold, class filter, NMS and IoU tracking.
Post-processing
Post-processing08
09Stage 9 of 10
Deploy
Build a package for the target you actually run.
Deployment
Deployment09
10Stage 10 of 10
Run and monitor
Heartbeats, inference logs and over-the-air updates.
Devices
Devices10