DATA TEAM
Focus your team. Scale answers
Stay the source of truth. Let the rest of the org self-serve safely from the work you built and approved
No credit card required
Data teams like yours already benefit from Querio
Remain the owner of the truth while reducing the ad-hoc request backlog so you can focus on real analytical work
10h
saved time on analysis per week
30
dashboards migrated from Tableau to Querio
20h
back by automating manual data work
Your governed logic, in one place
Querio’s Context layer holds your skills, rules, metrics, and catalog. It’s versioned by default, self-healing over time, and lives in a flexible file system. Define a metric once and it’s the same metric in Explore, in a Board, and in the agent’s answer. Changes are reviewable; the team writes the definition, and the agent inherits it.
Build artifacts your team can actually ship
Boards refresh on a schedule from the same Notebook that produced them, so when a metric shifts, the Board updates with it. Mark a Board “verified” so business users can tell what’s data-team-reviewed from a one-off Explore answer. Subscription dashboards, churn cohorts, billing reconciliations — the artifacts your team builds stay live and reproducible.
Every answer is code you can read and edit
Every AI-generated result is real SQL or Python you can open, edit, and re-run. No regenerating from scratch, no guessing what the agent did. As your team accepts answers, those decisions feed back into Context, and accuracy compounds instead of starting over every quarter. Reviewers can audit; analysts can extend.
One platform, three surfaces
Explore is plain-language analytics for anyone in the company. Notebook is a reactive Python notebook for deep analysis. Boards are publishable, refreshable reports.
Notebook
Reactive Python. Cells recompute when dependencies change. Stored as .py
Context
Skills, rules, metrics, catalog. Versioned. Self-healing. File-based.
I've used a bunch of analytics tools and they all seem like air sellers after I tried Querio!
BI Engineer at OpenVPN
Built for data teams that can't compromise on security.
SOC 2 Type II. Querio runs against your warehouse using your existing credentials. Customer data is never used to train models. RBAC and SSO.
Built for security reviews
Documentation, architecture diagrams, and security questionnaires ready to go. We've been through the review process — we know what your team needs.
Frequently Asked Questions
How is Querio different from a notebook tool with chat added?
Querio is a reactive Python notebook plus a versioned Context layer plus plain-language Explore, all on the same source of truth. The agent works against your governed Context.
Can I trust the agent with the metrics we care about?
Will Querio fit our warehouse?
What about our dbt models?
How do I keep business users from creating a governance mess?
