Put customer evidence behind your next growth decision.
Heatseeker runs live market experiments to learn what your audience responds to, then uses that evidence to train AI customer models called synthetic personas. Explore your next idea with them, and check their predictions against real behavior.
Opens our existing AI-guided tour on Storylane. It shows an earlier version of our homepage.
“Heatseeker gives me confidence, clarity, and conviction. It lets us explore ideas before our customers even see them.”
Put the question to the market. Keep the learning.
Which benefit should lead our next product story?
Active American Outdoor Adventurers
- Market
- United States · All genders
- Interests
- Hiking, camping + backpacking
- Behaviors
- Frequent travelers + engaged shoppers
Three possible answers run as real ads in market, in front of real people, not a panel or survey. The ad is the test vehicle; the evidence is the output.
Scroll to compare all three hypotheses →
Illustrative example. The winner, the 10,000 US outdoor adventurers shown on each ad and the persona response below are not reported results, and the ads are recreated, not screenshots. A response shows what people responded to, not why or what they will buy.
In this illustrative experiment, the reliability hypothesis receives the strongest observed response from the defined audience.
Compare two ways to demonstrate reliability: protection from rain and resistance to wear. Test which gives this audience more confidence; the current experiment does not tell us that yet.
Test those two demonstrations of reliability with the audience before choosing which claim to develop.
Personas that keep learning from reality.
Predict. Test. Learn.
Live experiments compare predictions with observed market response and inform the customer model.
Apply. Observe. Compare.
Connected campaign and shopping results add further evidence. Importing data does not by itself validate a prediction.
Market scans listen to social media and product reviews, so the next question starts from what people say. A scan should end in an experiment, not a report. Illustrative restaurant example:
- 01 / Heard
A chocolate-and-pistachio dessert is getting attention.
- 02 / Tracked
Heatseeker follows the theme over time, so you can see it build.
- 03 / Ready to test
Would our customers respond to a chocolate-and-pistachio cookie? Compare it with an existing favorite, with the intended audience.
Customer insight that changes the decision.
“Every brand wants to be customer-centric. This is a world-first that confidently brings that ambition to life.”
“We had eight different creatives. We only ended up running two in paid social, and they were the highest-performing traffic campaigns we'd ever done. We eliminated the duds via Heatseeker in a few days, which would have taken a month.”
How well does your AI understand your customers?
A convincing explanation of what customers want deserves evidence. Our comparison of leading AI models with observed market responses shows why.
A selected, retrospective study of 11 live-market experiments. It evaluates general models under the study's conditions, not Heatseeker persona accuracy.
Customer models for the agentic enterprise.
As more work is done with AI, the customer model behind it matters. Heatseeker's direction is to make evidence-grounded customer models useful across teams and their AI workflows.
Bring us a decision you’re working on.
A product idea, a proposition or a campaign direction. See how Heatseeker helps you investigate it with customer evidence.
Book a demo