Use cases

Research & Interviews

People don't always say what they mean.

Ask someone why they chose a product and you'll get an answer. But interviews contain more than answers. There is hesitation, enthusiasm, uncertainty, contradiction and emphasis. Sometimes the most interesting part of a response is how someone says it. Interhuman helps research systems capture more of that interaction.

What you can build

12 participants
Amara Diaz02:14

It's fine — I'm sure we'd figure it out.

Stress
78%

Richer interview analysis

Surface behavioural patterns alongside traditional transcript-based insights.

Adapting live

I don't really understand why I'd need this.Confusion

NARROW THE QUESTION

Which part is unclear: the problem it solves, or how it fits your workflow?

AI-moderated research

Enable research agents to respond differently depending on how participants engage.

2 to follow up
Worth investigating

It's fine — I'm sure we'd figure it out.

More nuanced customer research

Identify moments of uncertainty, enthusiasm or friction that may warrant deeper investigation.

100% analyzed
Interest
486
Uncertainty
371
Skepticism
312

Scalable qualitative research

Bring some of the interpretive depth of human interviewing to much larger datasets.

The problem

Qualitative research has always relied on human interpretation.

Researchers listen for what participants say, but also for the moments that feel important: a pause before an answer, a sudden change in energy, uncertainty around a particular topic.

As interviews become increasingly automated, much of that context disappears into transcripts and structured datasets.

What Interhuman adds

Interhuman brings behavioural signal analysis into spoken research.

Instead of treating an interview as text to be processed, AI can consider the way a participant communicates alongside what they actually say.

Why it matters

The goal isn't to replace the researcher. It's to give them more of the conversation to work with.