Predict Human Emotions.
The foundation model that understands and predicts human emotion across content, information, messaging, and experiences.
Behavior begins in the brain
The same twenty seconds of screen time produces four different neural signatures. Each signature predicts a different action that follows.
Input
Wildlife cold-open
Visual cortex and ventral attention fire hard in the first two seconds. This is the "scroll-stop" signature.
NES
96
EII
81
CAS
54
Input
Personal narrative
Default-mode network lights up as the story resolves. Empathy and self-reference spike together. The "I felt that" moment.
NES
82
EII
95
CAS
41
Input
Structured tier list
Dorsal-attention and motor cortex respond to goal-directed, rank-ordered content. Viewers are being primed to act. High CAS.
NES
88
EII
56
CAS
93
Input
Aesthetic product shot
Visual cortex engages briefly but nothing holds. Viewers are physically watching and cognitively gone.
NES
38
EII
33
CAS
21
The full audience read, in seconds
Score variants against neural and behavioral signal, run them through twenty audience personas, and pick the winner with the full reasoning attached. No traffic split required, no two-week wait.
Score every variant
See exactly how your content lands
Drop in any hook, draft, or short-form video. The brain-encoding model scores neural response on the same metrics that predict real engagement.
Overall
94/100
Know who's bouncing and why
Twenty audiences, one custom focus group
Each variant runs through the persona jury. Skeptics, Scrollers, Feelers, Actors, and Sharers each weigh in, then we surface the splits.
Early signal
Predict winners before launch
Ship the variant that travels. Kill the one that won't
Get a consensus signal, the drivers behind it, and the tensions in the group. Decide in seconds. Skip the two-week test entirely.
Strong engagement signal. Sharers forward, Feelers lock in.
84% group confidence
Drivers
Tensions
Score, simulate, and ship. The same loop your team would run in two weeks of A/B testing, compressed into a single read on the audience.
Research
OpenAffect research
OpenAffect is building a foundation model for human emotion and cognition. Our research focuses on understanding how content, context, and experience map to predictable human response at scale.
Research
Response mapping
Mapping how humans emotionally and cognitively respond to different forms of content, messaging, and experiences.
Research
Multimodal signal learning
Learning patterns across text, video, audio, and behavioral data to build unified representations of human perception.
Research
Prediction and evaluation
Measuring alignment between predicted and real-world human responses to continuously improve model accuracy.
Research
Human feedback systems
Incorporating real human feedback loops to refine understanding of perception, emotion, and decision making.