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.
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.
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.
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 crowed/user types.
This process removes the struggle for an annotator to understand the entire context of the taxonomy and specifications.
We annotate repeatedly until statistical significance is reached — providing you with a confidence score on every annotation.
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 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.
Our global user base and pipeline design can scale to a level like no other.
Our solutions suit the needs for all industries with vision-based machine learning needs.
Quality Match News and Events.
