Vero
Description
# Vero Vero is a modern, open-source analytics stack designed to help organizations work data-driven — without relying on opaque third-party SaaS platforms or complex enterprise BI tools. It brings together best-in-class open technologies in a modular architecture, enabling you…
About
# Vero Vero is a modern, open-source analytics stack designed to help organizations work data-driven — without relying on opaque third-party SaaS platforms or complex enterprise BI tools. It brings together best-in-class open technologies in a modular architecture, enabling you to **ingest, model, visualize, and query…
Details
- Author
- kpi-zone
- Downloads
- 318
- Categories
- Other
Jump to
- Open-source, vendor-neutral analytics stack
- Self-hosted with Docker for local or on-prem deployment
- Modular architecture with replaceable layers
- Natural language query via MCP server and AI agent
- Traditional dashboards with Metabase (or alternative BI tools)
- Sample Contoso Retail dataset included
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
VeroCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Clone the repository, choose the appropriate Docker Compose file (x86_64 or ARM64), set environment variables, and run docker compose up --build. The MCP server is part of the AI Agent layer and is configured as part of the stack.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"vero": {
"Vero": {
"command": "docker",
"args": [
"compose",
"--env-file",
".env.dev",
"-f",
"docker-compose.yaml",
"up",
"--build"
]
}
}
}
}McpServers
{
"Vero": {
"command": "docker",
"args": [
"compose",
"--env-file",
".env.dev",
"-f",
"docker-compose.yaml",
"up",
"--build"
]
}
}Vero
Vero is a modern, open-source analytics stack designed to help organizations work data-driven — without relying on opaque third-party SaaS platforms or complex enterprise BI tools.
It brings together best-in-class open technologies in a modular architecture, enabling you to ingest, model, visualize, and query data — both with dashboards and natural language.
Why Vero?
> Make it easy for small and mid-sized organizations to adopt a modern, open, and self-hosted analytics stack — without giving up control.
Too many organizations face the same challenges:
- Complex and costly vendor ecosystems
- Rigid tools that don’t adapt to your workflow
- “Black box” SaaS platforms that obscure what's really happening under the hood
- Pushing sensitive internal data to external vendors just to generate a chart
- A lack of data sovereignty and limited infrastructure ownership
Vero was built to solve that — with a clean reference architecture and a commitment to open, modular design.
🚀 Our Goals:
- Easy replication: Provide a production-grade analytics stack that’s understandable, testable, and easy to deploy.
- Open-source + vendor-neutral: Use well-documented OSS tools — no hidden fees or lock-in.
- Data sovereignty: Run everything on your own servers. Keep internal data internal.
- Modern stack: Built on today’s best practices — modular, scalable, and cloud-friendly.
- Accessible to teams: Usable by data engineers, analysts, and non-technical stakeholders alike.
With Vero, you’re not just consuming analytics — you own the full pipeline, from ingestion to insight.
🧱 Architecture Overview
Vero is made up of five layers:
1. Data Ingestion – powered by Airbyte
2. Data Warehouse – default: PostgreSQL (replaceable with Apache Doris or DuckDB)
3. Semantic Modeling – Cube.js
4. AI Agent Interface – Agno + MCP server
5. BI & Dashboards – Metabase (optional: Superset, Tableau, Power BI, etc.)
Each layer is loosely coupled and can be replaced or extended as needed.
📦 What's Included?
- Prebuilt Docker environment for local or on-prem deployment
- Sample dataset based on the Contoso Retail model
- Ready-to-use semantic models and views in Cube.js
- Natural language query agent powered by GPT or Claude
- Traditional dashboards and visualizations via Metabase
🧪 Quickstart
```bash
Sign in to leave a review
Use Google, GitHub, or an email account so ratings stay tied to real people.
No reviews posted yet.
