Data superpowers for Claude, ChatGPT, and every AI client.
Connect any MCP client to Querri and your agent works from the full, governed context of your business. Querri plans the analysis, finds the sources, and returns one trusted answer, scoped to your permissions.
APAC is your fastest region, up 23% QoQ, with EMEA close behind.
From real, governed dataYour AI is smart. Querri makes it fluent in your data.
Teams all-in on AI keep hitting the same wall: the assistant reasons brilliantly but can't safely reach your data. A pasted spreadsheet hits context limits and goes stale. Wiring an agent straight into your database is ungoverned and risky.
Querri closes the gap.
The same AI. Through Querri it runs lean and hands back one trusted answer.
Point your AI at Querri.
Unlock complex analysis at scale.
Querri plans the analysis, runs it across your data, and the answer lands back in your chat, ready to act on.
Millions of rows, no problem
Point your agent at millions of rows and Querri handles it. An AI alone would stall on that much data; Querri runs the full analysis and hands back the answer.
Works where you already are
Claude, ChatGPT, Cursor, Windsurf, and VS Code Copilot all connect to the same Querri server. Bring the assistant your team already lives in.
Repeatable data pipelines
Build an analysis once and rerun it anytime, from any chat. Your work lives in Querri as a reusable pipeline, not trapped in a single conversation.
Browse and go deep
Browse your sources, open your projects and dashboards, or drop to a raw SQL query when you want the rows yourself.
Querri analyzes. Your agent takes it from there.
Querri is read-only, so it never touches your source systems. It hands your agent analytics it never had: forecasts, cleanup, segmentation, and scoring on your real data. Your agent then acts on the result through its own tools.
Score, then target
Querri ranks your whole prospect list by likelihood to convert on your real data. Your agent turns that into a lookalike audience in Clay and launches the campaign.
Forecast, then act
Querri forecasts the quarter from your live pipeline. Your agent drafts the board update around the real numbers and schedules the send.
Read-only by design
Querri only ever reads your data, so the analytics layer stays safe. Any action happens through your agent's own connections, never through Querri.
What your AI can do once it knows your entire business.
Here's what teams can do with Querri from day one.
Audit your CRM history
Point your agent at years of Salesforce or HubSpot data and surface the patterns hiding in it.
Forecast revenue from your live pipeline
Have Querri model the quarter, then let your agent share it.
Score and prioritize your prospect list
Rank prospects by likelihood to convert, on your real data.
Build a lookalike audience in Clay
Querri finds your best customers, your agent builds the list.
Clean and dedupe a messy list
Turn an exported spreadsheet into something usable in minutes.
Draft the board update from real numbers
Querri runs the analysis, your agent writes the narrative.
Spin up a dashboard from a question
Ask once, get a saved view you can come back to.
Segment customers for a campaign
Slice the base by behavior, then act on each segment.
Turn an analysis into a presentation
Querri does the math, your agent builds the slides.
Your AI only ever sees what you can.
Connect once through single sign-on, and your agent inherits your role and permissions. Row-level policies apply to every question it asks, exactly as in the web app. And we never use your data to train our models.
Set up in a few minutes.
A few minutes from now, your assistant goes from blind to fully briefed on your business. The Querri MCP server lives at app.querri.com/mcp: add it, log in once, and start.
Add the server
In Claude, open Settings, then Connectors, then Add custom connector, and enter the Querri MCP server URL. ChatGPT, Cursor, and other clients take the same URL in their MCP settings.
Log in once
You're redirected to Querri's single sign-on. Approve it, and your assistant is connected. It stays connected across sessions.
Just ask
Querri's tools appear automatically when your question relates to your data. No tool names to remember.
That's the whole setup. From here, ask anything about your business and your agent answers from real, governed data instead of guesswork.
Prefer the terminal? Use the CLI.
The Querri CLI does everything the web app does, from your terminal.
Install & connect. pip install, log in once via SSO (or an API key for CI).
Build & analyze. Upload files, build projects, author views in SQL or from a prompt, run analyses, manage dashboards.
Govern & provision. Set row-level policies, provision users, mint scoped API keys, pull org-wide usage and audit logs.
Script it. Add --json and pipe into jq, run on a schedule, roll out across the org.
pip install "querri[cli]"
# Log in once via SSO, or use an API key for CI/cron
querri auth login
export QUERRI_API_KEY=qk_...
# Script a pipeline: JSON out, pipe IDs with jq
FILE_ID=$(querri --json --no-interactive file upload sales.csv | jq -r .id)
querri project new "Q3 Sales"
querri project add-source "$FILE_ID"
querri project chat -m "Top 5 products by revenue?"
# Govern and provision from the terminal
querri user new --email alice@acme.com --first-name Alice --last-name Smith
querri policy new --name "APAC only" --source-ids src_123 --row-filters '[{"column":"region","values":["APAC"]}]'
querri usage orgYour AI doesn't need more data. It needs a better foundation.
Connect your assistant to Querri and ask your first question in a few minutes. Start free, or read the docs to see exactly how it works.