Know exactly what your customers need you to build next
Productboard AI now supports fully automated feedback categorization. It works continuously in the background linking key user insights to related feature ideas. Now you'll know which features are top-requested and see exactly what every customer needs.
Hello product makers 👋, so great to be on Product Hunt sharing what our AI team has been hard at work on this year! We had a vision that one day product makers could wake up to find new feedback had arrived overnight, with user insights already linked to the right feature ideas… factored into your prioritization scores… and reflected on your product roadmaps. It’s remarkable we are finally there! We’ve just run a successful beta and are thrilled to be sending these new AI capabilities into gener
Centralizing scattered product feedback from colleagues & customers in one place is a major first step, but it still takes a substantial time investment to review it all. Productboard AI 2.0 changes all that with new fully automated feedback categorization that continuously monitors your feedback and feature ideas, forging connections whenever it finds a match. It means you’ll have a far more accurate picture of what your customers really need so you can prioritize the right features and del
When I started glueing together rangy.js with React at @productboard some 9 years ago to allow manual text highlighting of insights it was immediately clear that the process will be orders of magnitude better with the right application of ML. Thanks to the recent developments in LLMs we can now turn this dream into a reality! Big props to our AI team at Productboard for making this possible! 💪
In the early days of Productboard (around 2017), we started researching Machine Learning options and using NLP to provide more magic to our customers. The usual answers were, "It is impossible," "Data aren't good enough," etc. Thanks to breakthroughs in the AI field, we are delivering all the magic we were dreaming of years ago. 🚀
That looks awesome. I can easily see how that simplifies the headache of categorizing feedback at scale. Good job, people!
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.