Our ready-to-deploy resource pool goes beyond data labeling to provide post-processing and exception handling. Further reduce the burden on teams building and maintaining AI applications with the super.AI Data Processing Crowd.
Super.AI’s Data Processing Crowd offers a number of advantages over standard solutions for human-in-the-loop. The chart below summarizes key differences between our offering and the competition.
Role-based Access Control (RBAC)
Task-specific Processing Instructions
On-demand Resource Scaling
Quality Assurance Mechanisms
Pay per Headcount
How Does the super.AI Data Processing Crowd Work?
Here’s how we tap the right crowd for the job and guarantee results.
Workers must pass general skills tests that evaluate abilities such as reading comprehension, adherence to instructions, and more. Most importantly, tests that use your specific data are administered during qualification.
Tasks with known answers (ground truth) are sent to workers at random to ensure output quality is consistent. Inconsistent workers are checked more frequently, while consistent workers are checked less frequently.
Permutations of the same tasks are sent to workers to identify temporary drops in performance due to fatigue or distraction.
Internal Audit Team
If an anomaly is detected during task processing, an internal audit team is notified. Experts in writing and debugging instructions help resolve any issues.
Tasks that pass through the quality control pipeline without reaching a user-defined accuracy threshold can be routed to experts (in-house or third-party) for review. Our platform is designed to minimize the use of an expert review panel as much as possible.
To create an incentive to resolve tasks accurately and develop their skills, workers are compensated based on the value they provide to you. High-performing workers receive more responsibility and compensation over time.