Aetherya is a cognitive simulation platform for testing ads, landing pages, products, pricing, and messaging before launch. Build a synthetic audience, run an experiment, and see where people hesitate, what they misunderstand, which objections emerge, and what may stop conversion, at both audience and individual level.
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Maker
📌
Hey Product Hunt! I'm Andrei, the founder of Aetherya.
Before publishing this launch, we tested the launch itself with Aetherya.
We compared two ways of explaining the product and simulated reactions from founders, product managers, marketers, UX researchers, agency strategists, and skeptical AI buyers.
Aetherya predicted which message would create more clarity and trust, identified the objections most likely to stop people from trying the product, and helped us change the launch before publishing it.
That's what Aetherya is built for.
Research often asks people what they think they would do. Analytics tells us what they already did. Aetherya simulates the difficult layer in between: misunderstanding, hesitation, skepticism, shifting trust, and the friction that determines whether someone continues or leaves.
You can use it to test:
— landing pages and customer journeys
— advertisements and campaign concepts
— pricing and positioning
— products and features
— launch messaging and strategic decisions
You define the audience and context, run the simulation, and inspect both audience-level patterns and the individual decisions behind them.
Aetherya is not intended to replace real customers or human research. It is a pre-flight simulation layer designed to expose likely failure points before real money, traffic, or development time is committed.
During this launch, we will compare:
— What Aetherya predicted.
— What Product Hunt visitors actually did.
— Where the simulation matched reality.
— Where the simulation was wrong.
You can run your first experiment without booking a meeting or speaking with sales.
The feedback I value most is direct criticism:
— Which insight was genuinely useful?
— Where did the output feel generic or incorrect?
— What evidence would make you trust or reject the result?
Finding where Aetherya fails is more useful than polite approval.
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Spent a few minutes building a synthetic audience for a landing page test and the hesitation heatmaps were surprisingly specific, catching a pricing objection I hadn't even noticed myself. Curious to see how the individual-level simulations hold up on something more niche.
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Maker
@orhanqpft Glad the pricing objection surfaced. Individual-level simulation is a major focus for us, but we avoid treating any single agent as ground truth. The value comes from identifying recurring patterns across the audience while still preserving the reasoning behind each reaction. Niche use cases should become especially valuable as the persona context gets more specific. I’d be curious to see what you test next.
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Have you considered adding a way to export the individual-level hesitation data as a CSV or shareable report? That would make it way easier to share specific friction points with designers or copywriters who weren't in the original test, and help prioritize what to fix first before launch.
Report
Maker
@ykdtmab We already have shareable PDF reports that capture the individual hesitation signals, the reasoning behind them, and prioritized friction points, so designers, copywriters, or clients can review the findings without being part of the original test. CSV export is also something we’re considering for teams that need the raw data.
Spent a few minutes building a synthetic audience for a landing page test and the hesitation heatmaps were surprisingly specific, catching a pricing objection I hadn't even noticed myself. Curious to see how the individual-level simulations hold up on something more niche.
@orhanqpft Glad the pricing objection surfaced. Individual-level simulation is a major focus for us, but we avoid treating any single agent as ground truth. The value comes from identifying recurring patterns across the audience while still preserving the reasoning behind each reaction. Niche use cases should become especially valuable as the persona context gets more specific. I’d be curious to see what you test next.
Have you considered adding a way to export the individual-level hesitation data as a CSV or shareable report? That would make it way easier to share specific friction points with designers or copywriters who weren't in the original test, and help prioritize what to fix first before launch.
@ykdtmab We already have shareable PDF reports that capture the individual hesitation signals, the reasoning behind them, and prioritized friction points, so designers, copywriters, or clients can review the findings without being part of the original test. CSV export is also something we’re considering for teams that need the raw data.