Dataset curation
Balanced, edge-case-rich datasets with labeling QA pipelines.
Solution · AI Validation
Validate every inference.
Validate AI algorithms running on embedded hardware using real-world datasets, simulation environments, performance benchmarking, and regression testing.
AI TestingPerformance ValidationDataset BenchmarkingRegression Testing
/embedded-ai-testingautomata
Capabilities
What we deliver.
SIL/HIL AI benchmarks
Reproducible performance benchmarks across compute targets.
Regression harnesses
Automated regression on every model and firmware bundle.
KPI dashboards
Precision, recall, latency, and power tracked per release.
Scenario replay
Replay real-world edge cases against every candidate model.
Certification support
Evidence packages aligned to ISO 26262, ISO 21448, and UNECE R157.
Use cases
Where it applies.
- Pre-release validation of perception and prediction models
- Continuous integration for AI stacks in OEM programs
- Independent benchmarking for Tier-1 suppliers
- Safety-case evidence for regulator submissions
Technology stack
Built on proven tools.
PythonPyTorchONNX RuntimeMLflowJenkinsDockerGrafana
Ready to build with AI Validation?
Book a technical consultation with our engineering team.