our technology
We explore the errors
in your dataset.
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.
Iterative Optimization
Data Loops
An annotation process will go through this data loop several times.
Every loop will look at a different subset of the data.
With each cycle through the loop less data will be annotated.
Primary QM use cases
Manual Curation
Annotation QA
ML Model Analysis
Secondary QM use cases
Automatic Curation
Geometry Annotation
Model Performance Review
Manual Curation
Identify taxonomy problems.
Identify edge/corner cases.
Identify model failures.
Annotation QA
Identify annotation errors.
Identify edge/corner cases.
ML Model Analysis
Identify biases.
Identify edge/corner cases.
Reducing Complexity
Nano-Tasks
We break complex annotation needs into nano-tasks that feed into an intelligent decision tree. The process tasks are highly intuitive, quickly solvable, and doable by many crowd/user types.
This process removes the struggle for an annotator to understand the entire context of the taxonomy and specifications.
Statistical Guarantees
Confidence in numbers. We annotate repeatedly until statistical significance is reached — providing you with a confidence score on every annotation.
Accuracy(%) vs. Repeats
Transparent QA
Custom quality levels. Our Quality Assurance process is transparent, and customized precisely to your product. We detect and correct your most crucial errors.
At Scale
New levels of scale. Our global user base and pipeline design can scale to a level like no other.
Fully Adaptable
All industries. Our solutions suit the needs for all industries with vision-based machine learning needs.
Medical
3D Maps
Autonomous Driving
AR/VR
Construction
+ Other industries
