build anything

Your analytics stack. In your agent's hands.

Connect Claude through MCP, work from your terminal with the CLI, or build on the API. Give every agent the same business context and a workspace where it can run real analysis.

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Recents

June sales performance

You

June sales performance

How did we do on sales last month?

Using Querio → get_sales_data

Net revenue in June 2026 totaled $9.8M, up 12% from May. Here's the daily breakdown

Claude

mcp.json

{
"mcpServers": {
"querio": {
"type": "http",
"url": "app.querio.ai/mcp",
"headers": {
"Authorization": "Bearer "
}
}
}
}

"We prompt through the MCP every week and diff the answers against golden answers. It's faster, cheaper, and I can actually do evals compared to the BigQuery MCP."

Enver, Co-founder at Growdash

Ask in Claude. Keep the analysis in Querio.

Start an analysis from your AI assistant, get charts and tables back, and ask follow-up questions. Open the same work in Querio to inspect the SQL and Python behind the answer.

MCP

your agent

app

slack

Build on the API.

Connect your own tools to Querio with authenticated API access. Build workflows around your workspace, or bring analysis into your product with the embedded API.

API

Bring analytics into your terminal.

Let Cursor or your coding agent work with Querio through the CLI. Audit boards, update workspace files and maintain context alongside the code your team already reviews.

CLI

user@localhost ~ % curl -X POST \

"https://app.querio.ai/api/v2/embed/{customer_slug}/conversation" \

-H "Authorization: Bearer $QUERIO_API_TOKEN" \

-H "Content-Type: application/json" \

-d '{

"prompt": "What were our top 10 customers by revenue last quarter?"

Different tools. The same business logic.

Keep definitions, trusted queries and business rules in GitHub. Reuse that context across your agents, notebooks and dashboards so each workflow starts with what your team already knows.

local context