Ship a revenue/growth data layer in an afternoon instead of hiring a data engineer. Tell Claude Code what you need in plain English; it writes the dbt models and semantic layer against your real schema. The agent fails at review time, not in production — you ship the diff.

Hi Product Hunt 👋 I'm Renat, founder of RevOS.

We kept watching teams point an AI agent at their data and get back confident, wrong answers.

The model wasn't the problem. It had no idea what your "revenue" or "active user" actually means, so it guessed.

So we built the RevOS Data Engineering Agent.

One command sets up a full stack (dbt, Cube, BigQuery, Git), and Claude Code builds your models against your real schema, not a blank file. The agent proposes the work, you review the diff, and you stay in control of what ships.

Copilot writes code. RevOS gives the agent the context to know which code is right. Different thing, and they compose.

A production data layer you can build in an afternoon, without hiring a data engineer.

Free to start, no credit card.

Supported path today is BigQuery + dbt.

I'll be around all day, happy to answer anything and hear your honest feedback 🙏

I'm a software engineer on the team, not a data engineer. Before this, adding a new metric to our pipeline meant context-switching into dbt/Cube territory I wasn't confident in, or waiting for someone who was.

Now I just describe what I need, review the diff, merge. What used to take a day of back-and-forth takes minutes.