Clarivy Observe: Measure how AI recommends your brand

Clarivy Observe Snapshot Baseline is a one-time AI decision visibility audit. Choose 5 buyer questions, run them across 8 production model endpoints, and get a human report, AI-readable Markdown brief, and raw-data index so your team can see how AI systems find, trust, compare, and recommend your brand. Launch-week offer: 10 qualified Product Hunt makers can join a real Snapshot Baseline feedback cohort with priority review.

Hi, I am TikKnock, building Clarivy.

Clarivy Observe Snapshot Baseline is a small first read for one brand:

- 5 buyer questions

- 8 production model endpoints

- 40 raw datapoints

- human report

- AI-readable Markdown brief

- raw-data index for verification

The problem I am working on: teams are starting to ask "does ChatGPT / Claude / Gemini / other AI systems actually know how to recommend us?" Most answers today are either SEO guesses or simulated reports. Clarivy takes the boring route: run scoped buyer questions against production model endpoints, preserve the raw evidence, and make the output readable by both humans and internal AI agents.

For this launch, I would love feedback on three things:

1. Is "AI decision visibility" clear enough, or should the category be simpler?

2. Would you trust a one-time Snapshot before buying monitoring?

3. What 5 buyer questions would you test for your own brand?

For launch week, I am opening a small Product Hunt feedback cohort: 10 qualified makers get a real Snapshot Baseline with priority review. It is not tied to upvotes or reviews. Accepted makers complete the scope input and join a short feedback debrief after delivery.

This feels super timely. As more people ask AI for product recommendations, knowing how your brand is being represented is going to matter a lot.

@chen_chen57 Thanks, really appreciate that.

That is exactly the shift we are trying to measure. Buyers are starting to ask AI systems the same questions they used to ask search engines, analysts, or peers. For brands, the first problem is not optimization yet. It is knowing what the AI actually says, which sources it seems to trust, and whether the answer matches how the team wants to be understood.