313 Interest Score
28 Discussions
0.09 Engagement
May 2025 Launched

Velozity is an AI-powered user research tool for marketing, product, and UX teams to get speedy and reliable insights. Build your users into AI personas, run focus group discussions, and usability tests with them. Fine-tune AI personas to keep them evolving.

What the Community Said

This is such a game-changer, @gaayathri_murugan1 ! User research often ends up being a checkbox task instead of a continuous feedback loop, and Velozity seems to solve that beautifully. Love how it blends AI with real UX needs — especially the part about evolving personas and running usability tests on Figma. Congrats on the beta launch! Looking forward to seeing how teams start integrating this into their product cycles.

— [REDACTED]

I expected only personas, but you also incorporated the Moderator into the process. That is a game changer because it outlines model situations. I am currently testing it and I am positively surprised :) Wish you good luck.

— [REDACTED]

This is such a refreshing product! I had so much fun playing with the Moderator, each response was sharp and insightful. WOW!

— [REDACTED]

The moderator’s lines really immerse you in the process :) Do personas build upon each other’s responses, incorporating their own experience? (For example: ‘I agree with Emily, and from my experience…’)

— [REDACTED]

This sounds really powerful. I will definitely give it a shot to see what kind of insights it has that I've already validated with user research to see how reliable it is. I can report back when I've tested it.

— [REDACTED]

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Frequently Asked Questions

A measure of community engagement at launch. Higher means more people noticed and interacted with the product. It's a traction signal, not a quality rating.

Discussion threads divided by interest score. Above 0.30 is strong. Below 0.15 suggests the product got clicks but not conversation.

Categories come from the product's launch tags. Most products appear in 2-3 categories. The primary category is listed first.

The scores reflect launch-period engagement. Historical data is preserved and doesn't change retroactively. The build date at the bottom shows when the index was last refreshed.

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