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Meridian
Turn research into dev ready specs and develop with agents
Meridian helps AI-native teams turn customer research into specs their human and AI builders can execute. The platform helps to discover new feature ideas by running surveys on Linkedin; prioritizes and scopes features into tracks; prepares a plan where humans and Ai agents can work collaboratively to complete the track; release it and monitor for feedback to close the loop. Create a project with Astra and it will help you to know your product before you build it.
When I was building my previous venture Nestafar, I realized there was no proper tool out there which a founder can use to learn market insights before building a product. As I was building for the hospitality industry in remote towns, the general research available on google and claude were too bleak.
This was the core problem I wanted to solve and hence build the Expected commercial value ranking system which was using a probabilistic method to generate surveys based of Fletch Value mapping framework and score them to produce information on the most important features that the market is looking for.
The first version of this research tool was the MVP for meridian launched in July. However, we realized there was no way to validate if the ECV ranking was working until the feature was released and we collected the final feedback to close the loop.
Hence, we kept building to develop a full product management suite powered by our value engine. The value engine conducts deep market research to define a value based positioning; tracks the value all across the build, release stages; and finally collects the feedback to tune this understanding of value in future releases.
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About Meridian on Product Hunt
“Turn research into dev ready specs and develop with agents”
Meridian was submitted on Product Hunt and earned 23 upvotes and 1 comments, placing #66 on the daily leaderboard. Meridian helps AI-native teams turn customer research into specs their human and AI builders can execute. The platform helps to discover new feature ideas by running surveys on Linkedin; prioritizes and scopes features into tracks; prepares a plan where humans and Ai agents can work collaboratively to complete the track; release it and monitor for feedback to close the loop. Create a project with Astra and it will help you to know your product before you build it.
Meridian was featured in Analytics (172.9k followers), SaaS (43.3k followers), Maker Tools (2.8k followers) and OpenAI Day (9 followers) on Product Hunt. Together, these topics include over 69.2k products, making this a competitive space to launch in.
Who hunted Meridian?
Meridian was hunted by Shubhojyoti Ganguly. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Meridian stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Shubhojyoti Ganguly