No crowdsourcing. No annotator workforce. Autonomous AI agents that evaluate, score, and label datasets 24/7 with consistent, auditable quality.
Meta owns 49% of Scale AI. OpenAI, Google, and every other AI lab now risks exposing proprietary training data to a competitor.
Thousands of annotators doing repetitive evaluation tasks. Slow to scale, inconsistent quality, and expensive at volume.
Crowdsourced labeling produces wildly inconsistent results. One annotator's "relevant" is another's "somewhat relevant." Models suffer.
AI agents handle the evaluation pipeline end-to-end. No human workforce to manage.
Upload your dataset or connect your data pipeline. Search results, text, images, web content, ads. ScoreHive adapts to your schema.
AI agents score each data point against your custom rubric. Relevance, accuracy, intent alignment, content quality. Consistent, every time.
Labeled dataset returned via API or export. Full audit trail. Confidence scores. Flag edge cases for human review only when needed.
| Human Annotators | ScoreHive | |
|---|---|---|
| Turnaround | Days to weeks | Minutes to hours |
| Consistency | Varies by annotator | Deterministic scoring |
| Scale | Hire more people | Spin up more agents |
| Availability | Business hours, time zones | 24/7, any volume |
| Data privacy | Exposed to annotator workforce | Never leaves your pipeline |
ScoreHive is building the future where AI evaluates AI. Autonomous agents that deliver labeled, scored, production-ready datasets, without a single human annotator.