Validate Product Features With Simulated Users.
Feature decisions get validated by tiny user-interview cohorts (6–12 people willing to talk to you) and analytics from people already using your product. Both miss the audience you don't have yet: the segment that would adopt if you got the positioning right.
HOOKiQ simulates how target personas (your potential users, not just your current ones) would discuss your feature on Reddit and Twitter. Read the thread, see the objections, see the segments that get excited, before you spend an engineering quarter on the wrong thing.
How it works
Describe the feature concept. One paragraph for directional signal, a full PRD for nuanced reactions.
Define the personas: current users, target users, churned-but-could-return users, or specific competitor users.
Read the simulated subreddit thread, ranked objections, and the feature requests that emerged unprompted from the discussion.
Common questions
Won't AI personas just tell me what I want to hear?
They surface known patterns from public discussion of adjacent tools, products, and problems. They reflect recurring objections from real Reddit and Twitter threads, not synthesised praise. If a feature lands flat in the simulation, that's because the model has seen the same skepticism in real discussions of similar features elsewhere.
What are the AI personas based on?
Each persona is built from two ingredients: the demographic parameters you give it (age, location, profession, interests, beliefs) and the AI's training knowledge of how real people with those characteristics talk and behave online. No real person's data is used, and there is no database of individuals we draw from. The personas are statistical composites built from the AI's training on public-domain patterns (Reddit, Twitter, news, blogs, books), shaped by the targeting parameters you specify.
How specific does my feature description need to be?
One paragraph is enough for directional signal. A full PRD gets you nuanced reactions. The model adapts to the specificity of your input. There's no minimum length.
Can it replace user research?
No. It's a pre-flight check before you commit research budget. Run the simulation on three concept variations, pick the one that scores best, then validate that one with real users. Sequence simulation before research, not instead of it.
Does it work for early-stage products with no users?
Especially well. Pre-launch is the highest-value use case because you have no analytics, no usage data, and no current users to interview. Simulation gives you a starting hypothesis for what your target audience will actually care about.
Can I simulate competitive feedback?
Yes. Describe a feature, specify the persona as 'current user of [competitor]', and the simulation surfaces what they'd compare against and what they'd find missing. Useful for positioning a competitive replacement.