Models in the loop
Connect models to the data that improves them.
Prepare versioned Physical AI datasets for foundation, custom, and in-house models. Bring supported inference outputs into review, trace corrections to source scenes, and turn model behavior into the next data decision.
Model-ready data
Foundation, custom, or in-house
Supported inference
SAM, YOLO, and compatible endpoints
Full lineage
Outputs, review, and dataset versions
Explore model ecosystems
Model catalog
Explore labeling and foundation models for Physical AI workflows
One continuous loop
One loop from prediction to better data
Avala keeps the model run, source scenes, review decisions, and next dataset release connected—so teams can improve models without losing provenance.
01
Start from a known version
Select the dataset version used for inference or evaluation, wherever the model runs.
02
Review the uncertain edge
Bring compatible low-confidence outputs and failure slices into human review with the original sensor context.
03
Publish the next release
Write verified corrections back to a new dataset version and preserve the evidence behind every change.
Build the data foundation your models can compound on
Close the data-to-model loop.
Bring us your sensor stack, model workflow, and hardest failure mode. We will map the shortest path to repeatable model improvement.