Train your models with expert human feedback. Evaluators score responses across 6 dimensions accuracy, helpfulness, clarity, safety, completeness, tone with written rationale for every preference ranking.
Catch what automated checks miss. We identify factual errors, fabricated citations, unsupported claims, and logical inconsistencies each severity-rated and documented for your training pipeline.
Production-ready labeled datasets for computer vision and NLP. Bounding boxes, segmentation, NER, sentiment, and classification delivered with inter-annotator agreement scores above 0.75.
Annotect was founded because growing AI companies faced a broken choice: pay enterprise prices for Scale AI, gamble on anonymous crowd workers, or try to build annotation capacity in-house. None of these worked for startups that needed quality data without enterprise budgets. We built Annotect to solve that a dedicated, managed annotation team with documented quality processes, Canadian incorporation for enterprise trust, and pricing that respects startup economics.
Senior annotators independently label sample items. Resolve ambiguities with client before production begins.
First 10% of every project fully reviewed by QA Lead.
Statistical outlier detection for speed, distribution, and spatial bounds.
Minimum 10% of every batch independently re-annotated.
Corrections logged, categorized, and fed back into annotator training.
