FOR PRODUCT TEAMS
Ship the right things, faster.
A/B reads, MRR by product line, retention curves. Ask in plain English; the answer is ready before the next standup.
No credit card required
Product teams like yours already benefit from Querio
Stop filing tickets. Look smart in leadership reviews. The autonomy is real.
10h
saved per business employee per week
20x
faster reporting cycles
2 w → 30 m
to produce the insights Growdash's restaurant customers rely on
One definition of the metric that matters
Querio’s Context layer holds your definitions of activation, retention, and the MRR splits your team argues about. Versioned and shared, so the answer in Explore, the launch board, and the investor update all agree. When you change how you count an active user, every place that uses it updates. The team agrees on the metric once, and the agent inherits it.
Product metrics that don’t go stale between launches
Boards are notebook cells you publish. They refresh on a schedule, so the launch dashboard you built three months ago is still showing today’s numbers. MRR by product line, activation cohorts, retention curves, feature adoption — live and reproducible. Mark a Board “verified” so leadership knows what the data team has reviewed.
Ask in plain English. Stand behind the answer.
Ask your product question — “what’s our trial-to-paid conversion this quarter?”, “did variant B improve activation?”, “which features did churned users skip?” — and Querio gives you the answer with the code that produced it. You don’t need to read SQL; your data team does, which is why the number stands up in the leadership review. The data team stops being a ticket queue; you stop being blocked.
What you get out of the box
Plain-English Explore
Ask. No SQL required. Most PMs never look at the code; some pick it up from watching answers.
Querio queries your warehouse
BigQuery, Snowflake, Postgres, ClickHouse, MotherDuck, Redshift, MySQL, MariaDB, MSSQL. Product, billing, and events data wherever it lands.
Share to anyone
Boards live on a URL. Drop the link in Slack or send it to leadership; no login required to view.
“I was really impressed with the report. I mean… our ops team was probably gonna spend, like, three or four weeks on this analysis!”
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.
Read our security review ›Your data is never used for training
Querio has read-only access to your data. We cache conversation previews so your past chats load fast — nothing else is stored, and nothing is ever used for training, on our side or our model providers'.
Your queries run inside your infrastructure
Querio's code execution environment — where credentials live and queries hit your database — can run on your own infrastructure. Your data never leaves your perimeter to be queried.
SOC 2, GDPR, and HIPAA ready
Compliance documentation ready for procurement. BAAs available for healthcare, full GDPR alignment for EU data.
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
I don’t know SQL. Will this actually work for me?
Yes. Explore is plain English. The SQL is shown for the data team to verify when they want to, but you don’t have to touch it.
How accurate is the answer?
Every answer is real SQL or Python running against the Context your data team curates. As your team accepts answers, the Context gets sharper.
Does this connect to Amplitude / Mixpanel / GA?
Querio runs against the warehouse where your product data lands. If you sync Amplitude, Mixpanel, GA, or Heap into the warehouse (Fivetran, Airbyte, reverse ETL), Querio queries it the same way it queries everything else.
Can I share a board with my CEO without giving them a seat?
Yes. Boards live on a URL — drop the link in Slack or send it. No login required to view.
Can I run an A/B test analysis here?
Yes. Notebook is reactive Python. The data team can build the analysis once and it re-runs as new results come in.