Querio

What Querio is

Querio is an AI analytics platform that sits on top of a data warehouse. Its home page headline reads "Analytics as code. Loved by humans, built for agents," and describes it as the only product where you and your agents can build and maintain your entire analytics stack together, from ETLs to dashboards, in a git repo you own. The site's own product description calls it "an AI-native business intelligence and data analytics platform" where business users ask questions in plain English and Querio generates SQL and Python against a governed semantic layer connected live to the warehouse, "showing the query behind every answer." Its articles describe the buyer as a non-technical team, such as sales, finance, operations or product, that wants answers without waiting for a technical team. The site's footer names the company as Querio Ltd.

What you build and how

The core experience is asking questions in plain English. The company's articles say Querio works with data warehouses including Snowflake, BigQuery, Amazon Redshift, ClickHouse and PostgreSQL, using read-only, encrypted credentials, and the site's product feature list adds Databricks. An article says an AI agent converts business questions into SQL queries and automatically picks a visualization for the results, and another lists "transparent SQL generation," so a user can see the query that produced an answer. Since the connection is live, there is no need for data duplication or extracts.

Behind the questions sits a reactive notebook. The feature list describes a "Reactive notebook combining SQL, Python and BI output," and the organization description says metrics and definitions are stored as files and synced in GitHub. That is the "analytics as code" idea: definitions live in version control rather than in a dashboard tool's settings.

The home page splits the product into two actions. The first is exploring your data in Querio: ask the AI, analyze with notebooks, and create dashboards. The second is reaching your data from wherever you work, through a CLI, an MCP server, Slack or other tools, all backed by the same context. The page shows an MCP configuration snippet pointing at app.querio.ai/mcp with a bearer token, and lists agent access through MCP, API or CLI, per-user authentication, a governed runtime, shared outputs and audited questions. It also claims you can save 50% of your token costs. A customer quote from a Growdash co-founder says the team prompts through the MCP every week and diffs the answers against golden answers.

Dashboards are built in the product. An article says Querio includes a self-service dashboard builder, making it simple to create reports and track key performance indicators, and that the dashboards update automatically with live data. Another passage describes drag-and-drop dashboards that let users build custom visualizations in minutes instead of waiting on technical teams, with real-time collaboration so several team members can contribute at once. The home page adds a section on creating dashboards and scheduling reports or agents: its illustration shows an automation with a schedule trigger (every day at 9:00), a workflow step that runs a prompt, and an action that sends an email. The feature list says answers are delivered in Slack and in scheduled reports. A quote from the founder of Agree.com says the team needed to give people a list of invoices and weekly metrics without anyone writing SQL, and that Querio replaced the tools they had been running for it.

The data layer is a context system rather than a copy of your data. An article says data teams can define table joins, business rules and glossaries once through what it calls the context layering system, and the definitions apply across the organization. The point, in the article's words, is that non-technical users get consistent and accurate results while IT teams keep control of governance and security. The feature list says the governed semantic layer "imports dbt models and LookML," so a team with existing definitions does not start from scratch. The home page speaks of defining metrics and context once and using them across every customer instance and agent, and the demo shows a catalog that defines revenue as net of returns.

Embedding is a fourth use. The home page section titled "Embedded" says you can white label Querio in your product with SSO embedding, APIs and the MCP server. It lists row-level security with role-based access, audit logs and SOC2, HIPAA and ITAR compliance, white labeling with CSS, HTML and chart themes to match your brand, and customers getting dashboards and insights in seconds backed by your context. The demo is a customer portal where a client sees only its own orders, scoped by a row-level rule, and can ask a question in plain language.

Plans, limits and claims

The site's structured product data lists 3 plans that link to its pricing page:

• Startup: "$500 per month, or $5,000 billed annually," for up to 10 users and one data connection.
• Core: "$1,999 per month, or $20,400 billed annually," with unlimited users and 3 data connections.
• Enterprise: custom pricing, with 5 data connections, self-hosting and physical data separation.

An article on the same site describes a different structure: pricing from $14,000 a year, a tier with one database, 4,000 monthly prompts and unlimited viewer access, a Dashboards Add-On at $6,000 a year, extra databases at $4,000 per database per year, a self-hosted deployment at a 50% premium on recurring fees with a minimum annual cost of $60,000, and monthly billing at a 10% premium over annual pricing. The same article mentions flexible per-workspace pricing and a money-back guarantee. The 2 sources differ, so the pricing page is the one to read before budgeting. The home page carries a "Get started for free" button next to Book a demo.

On security, the home page lists SOC2, HIPAA and ITAR compliance alongside row-level security and audit logs, and an article says Querio complies with SOC 2 Type II standards and offers standard SSO integrations to align access control with an existing authentication setup. The footer links to a Security page and a Trust Center, and documentation lives at docs.querio.ai.

Industry and role links on the home page cover Product, Marketing, Sales, Customer Success, Healthcare, Finance, Retail, Media Advertising, Cybersecurity, and startup, mid-size and enterprise buyers.

Who it suits and who it does not

Querio suits a business that already has data in Snowflake, BigQuery, Redshift, ClickHouse, Databricks or Postgres and wants non-analysts to ask questions and publish dashboards without writing SQL. A data team that wants to define metrics once, keep the definitions in version control and let both people and AI agents use them is the target reader, and one that already maintains dbt models can bring them along. A SaaS company that wants customer-facing, white-labeled analytics with row-level security also fits, as does a regulated team that needs self-hosting on the Enterprise plan.

It is a harder fit for a solo founder or small team on a tight budget, since the smallest plan in the site's data starts at $500 a month. It also assumes your data already sits in a warehouse or database; Querio queries it in place rather than storing it. The Startup plan's single data connection and 10-user cap mean a growing team will move to Core sooner than it might expect.