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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.
Lev from the RevOS team here. My favorite bit is a small one: open any metric and you see exactly how it's calculated and every dashboard that uses it. "Why is this number different?" becomes a 10-second check instead of a meeting.
Pavel from the RevOS engineering team here.
Quick technical note: every metric is defined as code in our semantic model, and changes are reviewed through a pull request. Composite metrics, like a ratio built from two other metrics, show exactly which metrics they’re made of, so you can see how a number is built. Happy to answer any technical questions today!
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.
RevOS Metric Store was featured in Analytics (174.4k followers), SaaS (45.2k followers) and Artificial Intelligence (483k followers) on Product Hunt. Together, these topics include over 215k products, making this a competitive space to launch in.
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.
Want to see how RevOS Metric Store stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
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?