Anote

Anote provides data and evaluation infrastructure for AI teams. These docs cover the core workflows behind the current Anote platform: creating high-quality datasets, evaluating models, improving domain performance, and building reliable private AI systems.

Core Platform

Anote helps researchers and developers move from raw data to production-ready AI systems with a tighter feedback loop between people, data, and evaluation.

• Label Data: Create high-quality datasets with AI-assisted annotation and review.
• Evaluate Models: Measure model behavior with benchmarks and dashboards.
• Train Models: Improve performance on domain-specific tasks with fine-tuning workflows.

Key Capabilities

• Classification: sentiment analysis, document labeling, hierarchical classification, and active learning
• Entity Extraction: named entity recognition, advanced NER, and active NER workflows
• Question Answering: summarization, semi-structured prompting, and private chatbot evaluation

Technologies

Anote combines model outputs with human feedback so teams can improve quality without losing speed. The platform is designed to make model behavior easier to inspect, relabel, evaluate, and adapt as requirements change.

• Human feedback improves edge cases and domain coverage
• Evaluations make model performance measurable across tasks
• Smaller, higher-signal datasets can produce more reliable systems

Start Here

• Data Labeler Overview
• Fine Tuning Overview
• Private Chatbot Overview
• Evaluating Classification Models