Can someone appear confident and uncertain at the same time? We ran this clip of Elizabeth Holmes from the trailer of You Can See Everything, through Inter-2 to explore how social signals emerge, overlap and change throughout an exchange. What makes conversations interesting is rarely any single expression or gesture, but how these signals come together to shape the way we communicate and are perceived.

Today, we’re introducing Inter-2 Audio, our audio-only model, built to detect social signals from the voice without requiring video. As conversational AI becomes part of more human conversations, from voice assistants and AI interviewers to sales agents, research platforms and customer experiences, we are increasingly speaking to AI rather than simply typing to it. Yet much of what these systems understand still comes down to the words themselves, even though the voice carries considerably more information about how something is being communicated. Inter-2 Audio makes more of that information available to AI products using only the audio stream, detecting a broad range of social signals even in noisier, real-world environments. Because it does not depend on visual input, Social Intelligence can now be integrated into many more of the products where conversations already happen through voice. It is the second model in the Inter-2 family, built for a world where talking to AI is quickly becoming normal.

Today, we’re introducing Inter-2, our next step in Socially Intelligent AI. Inter-2 helps AI products understand how people communicate, beyond the words we use. It allows you to notice more by detecting social signals such as hesitation, agreement, confusion and skepticism in real time. For products built around human interaction, the model adds a layer of information that transcripts alone can’t capture allowing for a sales intelligence platform to detect how a customer is responding as a conversation unfolds; a user research platform to identify moments of hesitation or engagement; a coaching tool to understand not only the words being used, but how they are being delivered. Inter-2 is another step toward AI systems that can perceive and respond to the social dynamics of human interaction, helping products get more insight from every conversation and do more with it. Inter-2 is available today at interhuman.ai

WaveArm demo + code: 🎥 youtube.com/watch?v=32ebCxhg… 💻 github.com/AndreaMontalbano/…

A robot arm you control with gestures — no joystick. But the interesting part: it watches *you* back. If you look distracted or confused, it stops moving. Built in a weekend at the Robotic Agents Hackathon in Milan, with Interhuman AI as the safety layer. Designed for wheelchair users with limited motor function. interhuman.ai/blog/wavearm-h…

"The experience was fine" — said with 12 seconds of hesitation and a frustration spike. AI-moderated research tools transcribe what respondents said. The behavioural layer — how they said it — gets thrown away with the video. We built a research-interview demo on Inter-1 Streaming that keeps it. One WebSocket, signals pinned to the exact phrase: interhuman.ai/blog/what-they…

Your pitch deck can be flawless and still fall apart in the room. That's not a script problem. We built a demo that scores delivery in real time with Inter-1: confidence, hesitation, energy, each one flagged to the exact second it happened. Our own team's scores are already on the leaderboard, try and beat them: thepitchpractice.com Full blog write up: interhuman.ai/blog/pitch-pra…

Last week we introduced the Streaming API along with a live demo so you can see it in action. By popular request, we've now written a blog post explaining how the demo works. Full blog post: interhuman.ai/blog/stream-de… Demo: interhuman.ai/stream-demo?ut…

When we launched Inter-1 a month ago, we said streaming inference was next. Today it's live! The Streaming API brings the full set of Inter-1 capabilities to live video, delivering events as the conversation unfolds. Read the full write up: interhuman.ai/blog/inter-1-s…

“Social signal” and non-verbal social intelligence can be abstract. Real Talk Studio uses Interhuman AI to evaluate not just what people say, but how they show up empathy, composure, and real engagement under pressure. In this video, Toby from Real Talk Studio is demoing how non-verbal feedback works in practice. Try it yourself: realtalkstudio.com/start-pra… Full case study: interhuman.ai/customer-stori…

Most AI still reduces people to a few “emotions.” Real interaction is a stream of social signals: hesitation, confusion, engagement, skepticism, stress, expressed through words, voice, and body. Inter‑1 doesn’t guess a single label. It reads 12 social signals grounded in concrete cues across video, audio & text (gaze shifts, pauses, prosody, wording, posture, etc). We built a formal ontology of these signals + hundreds of cues, and trained Inter‑1 to reason over that structure. From “emotion detection” to evidence‑grounded social signal understanding. Full write‑up: interhuman.ai/blog/introduci…

When you show an AI model a video of someone speaking and ask what's going on, most outputs look like a summary of what was said. But in many cases, they miss a very important part: Someone pauses before answering. Looks away mid-sentence. Changes tone slightly on a key point. Those signals shape how the message is received. They often carry more meaning than the words themselves. Inter-1 is built to capture that layer. It processes video, audio, and text together, in temporal alignment, and detects social signals like hesitation, confusion, engagement, and uncertainty.

Inter-1 is live. An multimodal model built for understanding how people actually communicate.

Most AI describe what was said. But in many cases, that’s not where the signal is.

If AI is going to interpret human behavior, it should be able to explain why. Not just output a score. Not just label someone as “confused” or “confident.” It should point to the cues behind the conclusion. For human signal detection, explanation isn’t optional. It’s the whole point.

If AI is going to work in human settings, it has to do more than summarize what was said. It has to notice how people are reacting in real time. That means reading social signals, not just language.

A lot of multimodal AI is still just doing transcription with extra steps. It can process video and audio. It can summarize what was said. But that doesn’t mean it understands the interaction. Human communication lives in signals like hesitation, engagement, uncertainty, tone, timing, and gaze. That’s the gap.

AI can process video, audio, and text. But understanding human communication is not the same as processing inputs. The real challenge is reading the signals underneath the words: hesitation, engagement, uncertainty, confidence, confusion.