Interhuman AI provides specialized social intelligence infrastructure designed to help AI systems read, interpret, and respond to human behavior in real-time. Created to bridge the gap between simple speech-to-text transcripts and true human communication, the platform utilizes Inter-1, an omni-modal model capable of processing video, audio, and text in temporal alignment. By analyzing non-verbal, paraverbal, and verbal cues simultaneously, Interhuman AI allows developers to build applications that understand the intent and emotional nuance behind human interactions.

The platform's core functionality centers on its Signals API, which enables developers to integrate advanced social intelligence into their software. Rather than providing generic sentiment analysis, the API outputs structured JSON containing detected social signals, continuous engagement levels, and conversation quality scores. Each detected signal is supported by an evidence-grounded rationale, allowing developers to audit the AI's logic against specific observable cues.

Some of the key features are:

• Omni-modal Analysis: Processes video, audio, and text in temporal alignment to detect 12 distinct social signals including agreement, confusion, frustration, and skepticism.
• Evidence-Grounded Rationales: Provides a structured explanation for every detected signal, mapping behavioral cues like gaze, posture, and prosody to the final output.
• Continuous Engagement Tracking: Monitors user attention levels throughout an interaction, categorizing states as engaged, neutral, or disengaged.
• Conversation Quality Index (CQI): Computes a 0-100 score summarizing interaction quality across dimensions like clarity, authority, and rapport.
• Expert-Validated Ontology: Rooted in behavioral science and validated by psychologists to ensure signals are practically meaningful and scientifically sound.

Interhuman AI operates by analyzing video streams—either uploaded files or live inputs—through their proprietary model. Developers access this capability via a single API endpoint, receiving a consistent JSON response that includes confidence scores, signal timestamps, and human-readable explanations of the behavioral evidence identified by the model. This structured data is designed to be easily consumed by Large Language Models or other downstream applications that need to respond dynamically to human conversational behavior.

Some common use cases include:

• Sales Coaching: Enhancing sales training by tracking prospect signals like skepticism or interest to improve negotiation effectiveness.
• AI Tutoring: Enabling AI tutors to detect student confusion or hesitation in real-time to adjust pedagogical strategies.
• Meeting Copilots: Assisting in professional settings by summarizing engagement levels and rapport during high-stakes business meetings.
• AI Interviews: Adding behavioral intelligence to automated mock interview platforms to provide feedback on non-verbal communication.
• Healthcare Support: Improving patient counselling sessions by identifying stress or uncertainty in the patient's delivery.