
Overview
Rapidly and precisely build ML models to quantify and analyze complex sentiments in virtually any text.
Faster, Lower-cost Development
Use programmatic labeling to develop high-quality AI applications in hours instead of spending weeks or months on expensive hand-labeling.
Rapidly Adaptable
Monitor for changes in the data, and rapidly adapt using built-in error analysis tools. Zoom in on errors to fine-tune training data & models with guided iteration.
Easier SME Collaboration
Enable Subject Matter Experts (SME) to define polarity, subjectivity or tone, and refine schematic boundaries programmatically using a no-code or Jupyter notebook-based interface.
Flexible Integrations
Easily integrate labeling, training, and analysis pipelines defined over diverse input types–text, PDF, HTML, and more–with downstream applications using APIs or a Python SDK.
Use Cases
Research
An End-to-end ML Platform
Designed for Collaboration
Data Scientist Friendly
- Integrated Jupyter notebooks
- Instant analysis tools
- Ready-to-use models
Domain Expert Friendly
- Intuitive, no-code UI
- Rich dashboards and visualizations
- Full-featured, push-button error analysis
Developer Friendly
- Platform access via Python SDK
- Online or batch API deployment
- Containerized software for cloud or on-premises deployments