Perception Vs Reality
The next generation of AI developers will differentiate themselves based on their access to scenario datasets and tooling. Having precise ground truth labeled data is paramount to solving for edge cases and corner cases. Auto-labeling the scenario data is the holy grail of training ML models.
Collect Data
Upload, index and tag interesting scenarios
Autolabel
Autolabel the scenario data to prepare dataset
Review Data
Augment Autolabeling with Ground Truth Tools
Train ML
Regressively test your scenarios for ML Training
Painless Ground Truth Data
AUTONOMY AT SCALE
Bridge the Gap Between
Perception & Reality
With Our Tools
Autonomy today suffers from a series of interrupts causing a bad user experience. These interrupts are situations which are difficult to discern for a software but easy for humans. As limited autonomy deployments scale to multi-city production operations, there is a substantial rise in need to understand, classify and measure the reality. Perception is not always reality and the same principle applies to self driving vehicles. What they perceive might not reflect facts on the ground. So how can a system self correct if it doesn’t know the difference?
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SCHEDULE A CALLOur team
Sravan Puttagunta
CEO / CTO
Raj Singh
COO, Head of Product
Emily Leung
CRO, Head of Partnerships
Venkata Kolla
VP of Engineering
Sean Liu
Sr. Lead Engineer (Algorithms)
Rui Chen
Senior Engineer (Synthetics)
Saket Sonekar
Sr. QA Manager(Ground Truthing)
Abhijeet Sran
Director of Product Operations
Example Projects
Please find some example outputs of our ground truth processing
