Read from the file. Not a panel, a webcam, or an LLM guessing.
Everyone else measures reactions from recruited humans (panels, webcams, surveys) or simulates them by prompting an LLM to role-play a person. Soma reads the actual cortical response straight from the video file.
Same output shape. Different source.
Per-second attention curves and weak-spot callouts already exist. The question that matters is where the signal underneath them comes from. That is the whole comparison.
The comparison in one table.
| Human panelsRealeyes · Neurons · System1 | SyntheticAaru · Simile | VidCognitionsame model | Soma | |
|---|---|---|---|---|
| Signal source | faces, gaze, self-report | LLM guess | cortical response | cortical response |
| Needs recruited people | –yes, per study | ✓none | ✓none | ✓none |
| Result in hours | –days | ✓ | ✓ | ✓ |
| Cheap enough for every cut | – | ✓ | ✓ | ✓ |
| Grounded in biology | ~a proxy | – | ✓ | ✓ |
Soma competes on source, speed, and cost: the actual cortical response, read from the file, in hours, cheap enough for every cut. Figures for named competitors are from their own material; verify before quoting.
Where each one is strong, and how Soma differs.
vs Realeyes
webcam facial-coding + eye-trackingWhere they’re strong
Real human reactions on camera. An established brand with agency trust and years of normative data.
How Soma differs
We read from the file. No webcam, no recruited panel, no scheduling, in hours at a fraction of the cost, so you test every variant. Their read is a facial proxy for an internal state; ours is the cortical state itself.
vs Neurons Inc
AI attention / gaze predictionWhere they’re strong
The closest shipping product to ours, polished, with a real per-frame attention model that is mature in its domain. Established with enterprise marketing teams.
How Soma differs
Neurons predicts where the eye goes. Soma reads the full cortical response, attention and an affect read, from a brain-encoding model. We are priced for performance teams, not five-figure enterprise contracts.
vs System1 & Nielsen
panel emotion + normative databasesWhere they’re strong
Outcome-linked norms and credibility with brand marketers who care about long-term brand building.
How Soma differs
Panel-based, per-study, slow, and built for a handful of hero spots at brand budgets. Soma is the high-volume, low-cost, from-the-file read for teams shipping dozens of paid-social videos a week, the segment their model is too slow and pricey to serve.
vs VidCognition
same public TRIBE v2 modelWhere they’re strong
Built on the same public encoder we use, with a clean creator funnel and a good plain-English way of describing patterns. On the model itself, we are even; neither of us owns it.
How Soma differs
Two real forks. Buyer: they sell creators a hook score; we sell performance teams a decision instrument tied to real media spend and a data flywheel. Depth: they stop at a hook score on the shared model; we turn the read into a media-spend decision backed by real outcome data.
vs Aaru & Simile
synthetic research, LLM personasWhere they’re strong
Genuinely fast and cheap, and flexible enough to answer any question you can phrase, not only second-by-second attention.
How Soma differs
No biology. They predict what a person might say, and LLM personas lean toward agreeable, plausible answers. We read the actual neural response from a model trained on real fMRI, reproducible run to run.
Why teams pick Soma.
Soma reads how an ad earns attention, holds comprehension, and lands in the brain, straight from the file, in hours, at a fraction of panel cost, so you test every cut.
- ✓The signal is the actual cortical response, read from the file, not a webcam read or an LLM guess.
- ✓A result in hours, not days, and cheap enough to run on every variant.
- ✓Built for performance teams shipping dozens of paid-social videos a week.
- ✓Every read ties to real media spend and the data flywheel that compounds with every campaign.