Ai For Exceptions

Safety - Observability - Robustness

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

Watch Demo

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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Our 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

Davis, CA

Localization Error Analysis

San Francisco, CA

Perception Error Analysis

Freemont, CA

Mapping Error Analysis