TAWNY for Web

Quick overview

TAWNY is an advanced visual-intelligence platform that performs automatic analysis of human behavior from video. It extracts signals related to emotional states, focus, and behavioral patterns for both individuals and groups, presenting actionable insights in real time. The engine is built to run on ordinary camera setups and is suitable for use across many sectors that need emotion- and attention-related metrics.

Key capabilities

• Real-time decoding of complex affective and behavioral signals using patented processing methods refined since its inception.
• Multi-level analytics that report on individual cues as well as group dynamics and collective responses.
• Support for both live streams and pre-recorded footage, enabling flexible workflows for monitoring and research.
• Compatibility with a wide range of camera hardware, from industrial-grade imaging systems to common webcams.

Supported input types

• High-resolution feeds from production or laboratory systems as well as compressed webcam video.
• Recorded archives for post-hoc study and live video for immediate monitoring and intervention.

Privacy, security, and standards

TAWNY is engineered with privacy safeguards and complies with GDPR requirements. The platform minimizes personal exposure by focusing on behavioral signals rather than storing unnecessary identity data, and it includes configurable data-retention and access controls to meet organizational and regulatory policies.

Deployment options and adaptability

• Ready-made packages for quick deployment alongside tailor-made integrations for specialized environments.
• Configurable pipelines and output formats so organizations can adapt the analytics to their reporting, dashboarding, or research needs.

Typical use cases

• Market research and UX studies that require objective measures of attention and emotional response.
• Operational settings where crowd dynamics or team interactions inform safety and performance decisions.
• Academic and clinical research that needs scalable, automated behavioral coding across many video samples.