Test Public Statements Before You Publish.
A press release, founder statement, or crisis response only needs to fail with one audience segment to trend on the wrong side of the news cycle. The phrase that reads fine to a legal-reviewed comms team can read very differently to the demographic actually affected. By the time the response is public, you can't unpublish it.
HOOKiQ runs your draft statement through 5–100 AI personas calibrated to the affected segments. You get back the simulated thread reactions, the specific phrases that trigger pushback ranked by likelihood and intensity, and the segments most likely to amplify the negative response. Use it on the draft, not the published version.
How it works
Paste the full draft statement, press release, or response. Don't paraphrase. Exact wording is what's being tested.
Specify the affected audience segments: customers, employees, regulators, community groups, anyone with skin in the game.
Read the simulated thread, the ranked list of trigger phrases, and the predicted intensity of pushback by segment.
Common questions
Can it predict whether a statement will go viral?
No. What it predicts is which phrases and framings are most likely to trigger pushback. Whether that pushback reaches scale depends on timing, news cycle, and platform dynamics. Treat the output as a list of risks to address, not a forecast of outcomes.
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.
When should I run a statement through it?
After legal review, before final sign-off. Legal catches what you can be sued for. Simulation catches what you'll be criticised for. Both matter. They catch different things.
Is the simulation safe with sensitive draft content?
Yes. All inputs are encrypted at rest, scoped to your account, and not used to train any models. See our security and privacy policy for the full data-handling specifics.
Can it surface 'silent' reactions, what people are thinking but not saying?
Partially. The personas are calibrated against what gets actually said in public discussion, which captures the loud reactions. Quiet 'I'm just disappointed' reactions are harder to surface explicitly, but they often show up as terse, low-engagement responses in the simulated thread, which is itself a signal.
Should I use this for personal social media posts?
For high-stakes ones, yes. Founder announcements, public apologies, controversial takes, layoff explanations. For everyday content it's overkill. The threshold is roughly 'would I regret this if it went wrong?'