head to head
Where each one is strong, and how Soma differs.
vs Realeyes
webcam facial-coding + eye-tracking
Where they're strong
Real human reactions, not predicted. An established brand with agency trust and years of normative data.
How Soma differs
We predict from the file. No webcam, no recruited panel, no scheduling, in minutes at a fraction of the cost, so you test every variant. Their read is a facial proxy for an internal state; ours is the predicted cortical state, with the evidence tier on every claim.
Why won't they build it? A public, from-the-file model undercuts their own pitch of recruiting a panel and charging per study. It is a business-model conflict, not an engineering gap.
vs Neurons Inc
AI attention / gaze prediction
Where 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 predicts the full cortical response, attention and a coarse affect read, from a brain-encoding model. We are priced for performance teams, not five-figure enterprise contracts, and we carry the honesty boundary they don't.
Honest caveat: their attention model is mature; ours is validating, not validated. We differ on breadth of signal, price, and honesty, not on an accuracy claim we haven't earned. Pricing figure (about €15,000/year for five seats) is from a third-party page; verify before quoting.
vs System1 & Nielsen
panel emotion + normative databases
Where 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 model
Where 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
Three real forks. Honesty: they present the activation-to-engagement step as settled fact while admitting they run no validation of their own; we show the tier and run the held-out test. Buyer: they sell creators a hook score; we sell performance teams a decision instrument tied to real media spend and a data flywheel. Disclosure: we volunteer the negative prior; they quote only the flattering number.
Since the model is shared, the race is honest validation and real outcome data. Whoever earns those first wins. Verify their current claims and pricing before citing them by name in public.
vs Aaru & Simile
synthetic research, LLM personas
Where 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 predict the actual neural response from a model trained on real fMRI, reproducible run to run.
Honest caveat: it is partly a different job. They run broad simulated surveys; we read a second-by-second neural arc on video. The contrast is grounded versus ungrounded for the video-reaction question, not strictly either-or.