Detect what transcripts
can't capture
Send a video, get structured JSON - 12 social signals with explanations, engagement level, and conversation quality metrics. One endpoint.
{
"type": "uncertainty",
"start": 30.0,
"end": 40.0,
"probability": "high",
"rationale": "The speaker looks away, rubs their neck, and shifts their gaze while speaking. They also use phrases like “I don’t know if” and “maybe”. Together, these verbal and nonverbal cues suggest uncertainty and self-doubt."
}
One call, three layers of intelligence
Every response contains all three layers: social signals, an engagement level, and actionable conversation quality scores - from a single endpoint.
What notable moments happened?
How attentive was the person?
How good was this interaction?
Every signal your AI needs to read the room
Each signal includes a probability level and a human-readable rationale explaining what triggered it.
Agreement
Alignment with another person's position, intent, or understanding
Confidence
How firmly and assuredly someone communicates their position or decision
Confusion
A breakdown or gap in understanding during an interaction
Disagreement
Active divergence from another person's viewpoint or proposal
Disengagement
Reduction in attention, involvement, or investment in the interaction
Engagement
Sustained focus and active participation in the interaction
Frustration
Mounting tension or irritation when progress toward a goal feels blocked
Hesitation
Uncertainty or delay before committing to a response or action
Interest
Attention or curiosity toward something unexpected or stimulating
Skepticism
Questioning or doubtful stance toward a claim, proposal, or explanation
Stress
Heightened tension or unease during an interaction
Uncertainty
A temporary disruption in speaking or responding to a question
Agreement
Alignment with another person's position, intent, or understanding
{ "type": "Agreement", "start": 1.2, "end": 8.7, "probability": "high", "rationale": "The speaker explicitly states, "Yeah, that was great," providing clear verbal confirmation of his stance. He also nods his head while speaking and maintains steady eye contact with the camera, demonstrating active participation and alignment with the topic." }
Detecting buy-in during sales calls, confirming understanding in training, measuring alignment in negotiations
Engagement, tracked continuously across the interaction
Unlike other signals such as hesitation or agreement, engagement is estimated at every moment and shown as a continuous timeline of engaged, neutral, or disengaged states.
{ "engagement_level": [ { "start": 0, "end": 12.3, "state": "engaged" }, { "start": 12.3, "end": 18.5, "state": "neutral" }, { "start": 18.5, "end": 35.2, "state": "engaged" }, { "start": 35.2, "end": 38.8, "state": "disengaged" }, { "start": 38.8, "end": 52.6, "state": "engaged" } ] }
Interaction quality, scored automatically
The API computes a Conversation Quality Index (CQI) from the detected signals - a single 0-100 score, broken down into 5 behavioural dimensions. Updated throughout the conversation.
The API returns a single 0-100 score summarising interaction quality, computed from the detected signals. Provided as a snapshot and a rolling timeline.
5 behavioural dimensions
Each dimension measures the balance between supportive and undermining behaviours detected in the conversation. Higher is always better.
How easy the speaker is to follow, reflecting the organization, concision, and coherence of their ideas.
How confident, decisive, and credible the speaker comes across in their delivery.
The speaker's level of vitality and active engagement throughout the interaction.
The warmth and emotional quality of the interaction, reflecting how acknowledged and at ease the other person feels.
How openly the speaker reflects, tests ideas, and adapts, capturing their curiosity and growth orientation.
Powered by Inter-1
An omni-modal model purpose-built for understanding human social signals. It processes video, audio, and text together - in temporal alignment.
Inter-1 is trained specifically for social signal detection - it leverages a proprietary dataset combining behavioural science anchored ontology and expert-led labeling.
Evaluated against a wide variety of models including both commercial and open-source frontier omni and vision LLMs. Inter-1 was found to be best in terms of accuracy and speed.
In blind A/B evaluation, behavioural science experts chose Inter-1's rationale over competitor output 83% of the time (76% on evidential grounding, 91% on clarity).
Sentiment gives you positive/negative. We give you 12 specific signals with timestamps and rationale.
Don't rely on any fragile patchwork of disparate models. Inter-1 natively processes all modalities to deliver insights via one unified endpoint.
Every signal includes the observable cues that triggered it - which modalities, which behaviours. Auditable and actionable.
What you get from every API call
12 social signals
Agreement, confusion, frustration, hesitation, and 8 more - each with a probability level and a rationale explaining the observable cues that triggered it.
Multimodal analysis
Video, audio, and text processed together in temporal alignment. Gaze direction, vocal prosody, posture, gestures, and speech - not just transcripts.
Engagement tracking
Continuous attention monitoring with three levels - engaged, neutral, or disengaged - indicating how oriented a person is to the interaction at every moment.
Conversation Quality Index
A 0-100 score derived from social signals across five behavioural dimensions - Clarity, Authority, Energy, Rapport, and Learning - provided as both a snapshot and a timeline.
Explainable by default
Every signal includes its rationale, with the modalities and behaviours involved. Auditable, actionable, and ready to forward to your LLM.
REST
Upload a recording or connect live video. Pass your API key directly or exchange for a short-lived JWT. One response shape for both. Any format ffmpeg supports.