Interhuman is an AI research and products studio.
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Social-Aware AI

Our technology interprets body language, facial expressions, and tone of voice in real time. By combining this with contextual awareness, we enable more natural and relevant conversations with users.

Relately:
Real-time communication coaching

Relately is designed to assist professionals with communication issues in the workplace.

The app provides personalized paths for discovery and learning and real-time actionable insights on improving communication with others. We help you prepare for difficult situations before you face them in the real world. Available anywhere at any time, providing a private safe space.

AI that deepens human understanding.

Social-Aware AI — We focus on developing AI that deeply understands human social signals and emotions, by combining research and insights from psychology, neuroscience, and computer science to create AI that models human interactions.

Helping people understand each other — By continuously integrating real-world feedback, we refine our AI to be more empathetic and context-aware, ultimately enhancing the quality of human relationships.

Why Social-Aware AI?

A few words from our CEO, Paula Petcu on why we're focusing on developing AI that understands human social signals and emotions.

Our Technology
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Social signal interpretation

Our AI interprets body language, facial expressions, and tone of voice in real time, enabling more natural and empathetic conversations.

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Emotional intelligence

Our technology recognizes and responds to human emotions, fostering deeper connections and more meaningful communications.

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Contextual awareness

By considering the context of interactions, our AI adapts its responses to fit situational nuances and individual user needs.

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Ethical design

We prioritize privacy, security, and inclusivity, working towards AI that is fair, transparent, and beneficial for all users.

Research

We research models and how to align them with human emotions and social signals to enhance interactions.

Interpretability by design using computer vision for behavioral sensing in child and adolescent psychiatry
Using computer vision for behavioral sensing in child and adolescent psychiatry, this study assesses the accuracy of ML-derived behavioral codes from clinical interview videos, comparing them with human expert ratings to improve reliability and scalability in psychiatric diagnostics.
Beyond Accuracy: Fairness, Scalability, and Uncertainty Considerations in Facial Emotion Recognition
The study examines the current state of FER models, highlighting issues of fairness, scalability, and robustness. The study proposes metrics and algorithms to assess and improve these aspects, emphasizing the importance of fair and reliable FER models in clinical applications and beyond.
Scaling-up Behavioral Observation with Computational Behavior Recognition
The study proposes using open-source AI tools to automate behavioral coding in parent-child interactions and therapy sessions. This method enhances scalability, consistency, and depth of analysis, addressing traditional human coding limitations. The study discusses privacy, bias, and validation methods, highlighting the potential for these tools in psychological research and clinical practice.

Team

Our founding team includes two data scientists, a UX-design and Marketing leader, and a Professor specializing in AI and Machine Learning.

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