Exceptional AI

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 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

Prior Work

Optimize your ML workflows with advanced ground truthing and error analysis. Join RoadMentor Accelerator Program today.

Synthetic Dataset

Sensor Fusion Validation

San Francisco, CA

GNSS Error

Your Dataset

Sensor Calibration + Error Analysis


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?


Stay Up To date

Keep up with our latest updates and news.

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Our team

Sravan Puttagunta


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