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RevOS Metric Store
One definition of every metric, for people and AI agents
Ask four systems for ARR and you get four numbers. RevOS Metric Store gives every metric one definition, written as code in your semantic model. Dashboards, BI tools and AI agents like Claude and ChatGPT all read the same one. Now in beta.
Every customer and revenue leader we speak to tells the same story. They start using AI on their data, ask an important question and get a plausible-looking wrong answer, very fast. Then three people spend a week explaining where the number came from, and after that nobody trusts the AI answers. Winning that trust back takes a huge effort.
The model is rarely the problem. Most data was never structured for AI: the same metric means something different in the CRM, the spreadsheet and the BI tool, so the agent picks one.
So we built the Metric Store:
every metric gets one definition, written as code in your semantic model
you can see exactly how each number is calculated, and everywhere it's used
dashboards, BI tools and AI agents like Claude and ChatGPT all read that same definition
It makes a measurable difference: a general model scored 51% on raw schemas and over 90% with a semantic model.
We're in beta. If you want to see it on your own metrics, book a demo from the link above. I'm here all day to answer questions.
Which metric does your team argue about most?
About RevOS Metric Store on Product Hunt
“One definition of every metric, for people and AI agents”
RevOS Metric Store was submitted on Product Hunt and earned 6 upvotes and 3 comments, placing #55 on the daily leaderboard. Ask four systems for ARR and you get four numbers. RevOS Metric Store gives every metric one definition, written as code in your semantic model. Dashboards, BI tools and AI agents like Claude and ChatGPT all read the same one. Now in beta.
On the analytics side, RevOS Metric Store competes within Analytics, SaaS and Artificial Intelligence — topics that collectively have 702.6k followers on Product Hunt. The dashboard above tracks how RevOS Metric Store performed against the three products that launched closest to it on the same day.
Who hunted RevOS Metric Store?
RevOS Metric Store was hunted by Renat Zubairov. 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.
For a complete overview of RevOS Metric Store including community comment highlights and product details, visit the product overview.
Renat Zubairov
Hi Product Hunt, Renat here, founder of RevOS.
Every customer and revenue leader we speak to tells the same story. They start using AI on their data, ask an important question and get a plausible-looking wrong answer, very fast. Then three people spend a week explaining where the number came from, and after that nobody trusts the AI answers. Winning that trust back takes a huge effort.
The model is rarely the problem. Most data was never structured for AI: the same metric means something different in the CRM, the spreadsheet and the BI tool, so the agent picks one.
So we built the Metric Store:
every metric gets one definition, written as code in your semantic model
you can see exactly how each number is calculated, and everywhere it's used
dashboards, BI tools and AI agents like Claude and ChatGPT all read that same definition
It makes a measurable difference: a general model scored 51% on raw schemas and over 90% with a semantic model.
We're in beta. If you want to see it on your own metrics, book a demo from the link above. I'm here all day to answer questions.
Which metric does your team argue about most?