Achieve significant cost and time savings by improving your machine learning models with HARI, a semi-automated user interface for data curation, annotation, and model analysis of vision-based datasets.
We optimize the representativeness and diversity of your datasets, to avoid harmful biases in your end-product. A RAD dataset improves machine learning model performance through quantifiable and actionable quality metrics.
HARI manages all aspects of the dataset creation and verification process. The platform allows data scientists and machine learning professionals to gain real-time actionable insights from their vision-based datasets.
A cloud-based architecture simplifies the deployment process and reduces the time it takes to gain insights into the dataset quality from months to days. With HARI the dataset quality can be significantly improved to enhance application reliability and safety while increasing customer satisfaction and overall ROI.
From data upload to quality metrics, HARI is the new industry standard for your annotation workflow.
Discover endless possibilities to analyze your datasets. With a web based interface HARI allows you to review, filter, and sort datasets on any desired attribute. You can evaluate single annotation frames as well as compare two annotation sets to find and isolate problem areas.
- False Positive Detection
- False Negative Detection
- Edge Case Detection
- Occlusion Information
- Datasets Metrics Comparison
- Consulting Options
- Transparent Pricing Tiers
We annotate repeatedly until statistical significance is reached — providing you with a confidence score on every annotation.
Our Quality Assurance process is transparent, and customized precisely to your product. We detect and correct your most crucial errors.
Our global user base and pipeline design can scale to a level like no other.
Quality Match News and Events.