AnnotateIQ

AnnotateIQ is a multi-agent annotation system built for JEE Physics. Coaching platforms and EdTech teams generate huge volumes of practice questions every year, but turning those questions into clean, consistent training data is still mostly manual, slow, and uneven. AnnotateIQ was designed to solve that gap. Instead of relying on a single black-box model, AnnotateIQ runs specialist agents in parallel. They label chapter and concepts, difficulty and Bloom level, mathematical structure, and language mix. A separate critic then validates the merged result against a clear rubric. High-confidence annotations can be auto-accepted, while uncertain ones are sent to a human review queue where every agent draft and disagreement stays visible. Nothing important is hidden. The system also uses honeypot checks and confidence scoring so quality is measurable, not assumed. The final output exports as ML-ready JSONL, JSON, or CSV — ready to train models that can support smarter, more personal learning help for JEE aspirants. In short, AnnotateIQ turns raw Physics questions into trustworthy labeled data, with transparency at every step and humans in the loop only where it truly matters