Our evaluators score model outputs across six dimensions: accuracy, helpfulness, clarity, safety, completeness, and tone. Each response receives a 1-5 score per dimension with a written rationale. Delivered with per-batch IAA scores and annotator calibration certificates.
Side-by-side preference ranking for two or more model outputs. Annotators provide ranked choices with documented justification – structured for direct ingestion into RLHF pipelines. Minimum IAA 0.80 guaranteed.
Annotators flag factual errors, fabricated citations, unsupported claims, and logical inconsistencies. Each finding is severity-rated (minor / major / critical) and documented with source evidence for your training pipeline.
Systematic review of outputs for harmful, biased, or policy-violating content. Annotators use your safety rubric or Annotect’s default taxonomy. Results include category classification and confidence score per flag.
Systematic review of outputs for harmful, biased, or policy-violating content. Annotators use your safety rubric or Annotect’s default taxonomy. Results include category classification and confidence score per flag.
Multi-turn dialogue quality assessment. We evaluate coherence, context retention, persona consistency, and resolution quality across full conversation threads.
Bounding boxes, polygon segmentation, keypoint detection
NER, sentiment analysis, intent classification, categorization
Transcription, speaker diarization, event tagging
Cross-validation, consistency checks, edge case flagging
We learn your data, models, and annotation needs
Fixed-scope project, clear deliverables and quality benchmarks
Team aligned to your guidelines before production begins
Labeled data with IAA metrics and Batch Certificate
Expand to production volume with your dedicated annotator team
