Context-aware intelligence
Models designed to recognize meaning, behavioral patterns, and important signals within the way people actually communicate—not simply match isolated keywords.
Responsible machine intelligence
Machine intelligence built to understand context.
E-2 develops advanced language and behavioral intelligence systems for high-stakes environments where accuracy, privacy, and human context matter.
Context intelligence engine
Active
AI
Context aware
Language analysis
Privacy conscious
Responsible design
Human centered
Actionable insight
Built for high-stakes contexts
Language intelligence
Behavioral signals
Privacy engineering
Human-centered AI
Our technology
Applied AI designed for complex human environments.
We combine natural language processing, behavioral signals, privacy engineering, and trustworthy product design to build systems for environments where human context is essential.
Privacy-conscious architecture
Systems structured to minimize unnecessary data exposure while surfacing the information needed for informed and responsible action.
Actionable intelligence
Clear alerts and decision support that translate complex model output into information people can understand and use.
Research to deployment
Built on years of rigorous, interdisciplinary work.
E-2 grew from research into the automated detection of harmful online communication. The underlying work expanded across cyberbullying, predation, self-harm, relationship violence, and other forms of digital risk.
Today, that research informs a broader approach to language and behavioral intelligence: systems designed to recognize subtle patterns, interpret human context, and support timely action.
- Research initiated in 2007
- More than 65 student researchers contributed
- Multiple rounds of National Science Foundation support
- Commercialization launched in 2019
Intelligence pipeline
Signal → Context → Confidence → Action
Our flagship application
Boundrees: digital safety without constant surveillance.
Boundrees brings E-2’s contextual machine learning technology into the family digital-safety space. It is designed to help parents identify potential harm while preserving a child’s privacy.
The system analyzes communications for meaningful risk signals and sends focused alerts when a parent or trusted adult may need to pay attention.
Harm detection
Privacy-centered alerts
On-device processing
Adaptive language models
The foundation
Technology informed by real communication.
01
Interdisciplinary by design
Our systems are shaped by computer science, communication research, information science, statistics, cybersecurity, and product development.
02
Privacy built into the architecture
We design for responsible data use, limited exposure, and focused outputs that surface only the information needed for meaningful action.
03
Built for real-world use
Our work moves beyond research models into practical systems designed for complex environments where accuracy, context, and usability matter.