Querio is an AI-native analytics platform with governance built in. Business users ask questions in plain English and get answers they can trust. Data teams define the context once, keep it in their own Git, and let agents do the work without losing control of how the data is used.
Most AI analytics tools bolt a chatbot onto a warehouse. The results drift: every user gets a slightly different definition of revenue, every query is a fresh guess at the joins, and nobody can audit what the model did. Querio is designed around that problem. A context layer holds table relationships, metrics and business terminology as plain files that live in version control. Every agent run reads from the same context, so answers stay consistent across teams and change only when the data team says so.
Querio connects to Snowflake, BigQuery, Postgres and other modern warehouses and databases over read-only connections. Queries run on live data where it already sits. There is no data duplication and no proprietary storage.
For business users, Querio works like a conversational analyst. Ask a question, follow up with "break this down by region" or "show last year instead", and the charts update while the context is kept. Results can be pinned to boards and dashboards that stay in sync with the warehouse.
For data and analytics teams, Querio is a code-first workspace. The agent's working medium is a reactive Python notebook where prompts, generated SQL and Python, charts and narrative live together. Every line of code the agent writes is visible, editable and versioned. Analysts can stop a run, correct the query, and continue. Nothing is hidden and nothing is a black box.
Querio also powers embedded analytics. Product and platform teams can ship AI-driven questions, charts and tables inside their own applications, with APIs and theme tokens to match their design system.
Security and governance are first-class. Querio is SOC 2 Type II audited, uses read-only connections with encrypted credentials, offers granular access control, and never uses customer data or queries to train external models, on any plan.
Key Features
Natural language querying and conversational analytics. Ask questions in everyday language, refine with follow-ups, and drill into detail without writing SQL. The agent handles translation and keeps the context of the conversation.
Context layer as code. Define joins, metrics and glossary terms once, as plain files in Git. Every agent, every user and every query reuses the same definitions, so answers stay consistent and changes are reviewable.
Agentic notebooks (coding optional). Reactive Python notebooks where prompts, generated SQL and Python, charts and narrative sit together. Business users stay in prompt mode. Technical users drop into code when they need to.
Explore: instant answers on live data. Start from a prompt, iterate freely, drill down as many times as needed, and adjust filters or visualisations on the fly.
AI-assisted dashboards and boards. Generate charts and tables from questions, then organise them into dashboards and boards that track KPIs or tell a data story, kept in sync with the warehouse.
Embedded analytics and AI experiences. Put AI-powered questions, charts and tables inside your own product, with APIs and theme tokens so customer-facing analytics look native.
Schema awareness and full transparency. Tag specific database values inside prompts, see what data Querio can reach, and inspect every query it writes. No hidden logic.
Stop and edit. Pause a run, change the filters or the generated SQL and Python, and re-run without leaving the workflow. Analysts keep the control they expect with the speed of an agent.
Version control for outputs. Update analyses and dashboards while previous versions stay intact, so teams can compare past and present results without losing work.
Secure, governed access. SOC 2 Type II, read-only database connections, encrypted credentials, granular access control, and no training on customer data or queries on any plan.
Typical Use Cases
For Data and Analytics teams
Centralising metrics, joins and terminology in one version-controlled context layer
Cutting the data request backlog without giving up governance
Reviewing and hardening agent-generated SQL and Python in a transparent workspace
Replacing or modernising Tableau and Power BI estates with self-serve that the data team still controls
For Product and Growth teams
Funnel, activation and feature adoption analysis
Experiment and cohort analysis without writing SQL
Self-serve product analytics embedded directly in the app
For Revenue, Sales and Marketing
Pipeline, MRR/ARR and retention reporting
Campaign performance and attribution analysis
Shared boards for weekly and monthly business reviews
For Operations and Support
Monitoring operational KPIs and SLAs on live data
Finding bottlenecks and anomalies through follow-up questions
Answering ad hoc performance questions without Excel exports
For SaaS products
Adding self-service, AI-driven analytics to customer portals and internal tools
Average Rating: 4.6/5.0
Total Reviews: 10
How Do G2 Users Rate Querio?
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Has the product been a good partner in doing business?: 8.9/10 (Category avg: 9.2/10)
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Reports Interface: 10.0/10 (Category avg: 8.8/10)
Who Is the Company Behind Querio?
Who Uses This Product?
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Company Size: 50% Small, 40% Medium
What Do G2 Reviewers Say About Querio?
AI-generated summary from verified user reviews
Pros
- Users value the accuracy of responses in Querio, enabling quick and precise CRM data analysis effortlessly.
- Users praise the seamless AI integration of Querio, enabling quick, accurate queries and insightful visualizations effortlessly.
- Users praise the ease of use in data analysis with Querio, enabling quick, accurate insights through natural language queries.
- Users praise the data visualization capabilities of Querio, enabling instant insights and clear presentation of analyses.
- Users value the outstanding ease of use in Querio, enabling natural language queries for quick, accurate results.
