There's a well-documented phenomenon in consumer and user research that most practitioners know intuitively but rarely name directly: people don't report their experience accurately.
Not because they're dishonest. Because self-reporting is hard. In the moment of an interview, participants are performing a version of themselves. They round off hesitation. They describe their behaviour more charitably than it actually was. They say "yes, I'd probably use this" when what they felt was closer to "maybe, under the right circumstances, if the price were different." The social pressure of being in a conversation, even with an AI, shapes what gets said.
This is what we call the Say-Do Gap. The distance between what someone tells you and what they actually feel or do.
It shows up everywhere. In concept tests where participants say they love a product they'd never buy. In usability sessions where someone says "this makes sense" while visibly struggling. In brand perception studies where stated attitudes don't match purchasing behaviour.
The facial coding piece is genuinely impressive — caught subtle reactions in a test session I wasn't even expecting to register. Most "AI researcher" tools I have tried just spit out summaries, this one actually digs into how people feel.
Mira
@sleymanpelv3z3 "Summaries vs actually digging into how people feel" is a real distinction. Most tools stop at summarising what was said. The signal that actually drives behaviour lives in a different layer. That is what Mira tries to preserve.
Tried the facial coding on a quick test and the emotion read was scarily accurate to what I was actually feeling during the open ends. The auto-generated themes also saved me a solid hour of tagging.
Mira
@burhanekipepz8 Thank you for putting it through a real test. That gap between what the transcript says and what the face shows is exactly what Mira is built to catch, most tools only ever see the words.
the facial coding piece is wild, didn't think i'd see real emotional response analysis baked into a research tool at this price point
Mira
@serhatmant9b7b Accessibility was a deliberate decision. Emotion AI in research has historically been available only to large enterprise teams with big tooling budgets. Glad that came through.
Onepane
congrats! user interviews are the part of building I always end up skipping because of the effort. this could actually make me do them
Mira
@ashmil_hussain Yes our entire mission behind Decode is to democratise user research. We want all the creators and builders to have data backing their creative decisions and AI guiding them to take better decisions.