AI analytics assistants · Querio

Querio

AI data platform built around version-controlled 'analytics as code' notebooks that answer questions against a defined context layer.

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Querio lets teams keep a single semantic/context layer under version control (git) and query it through AI-powered notebooks, an internal API, MCP, or a CLI, rather than pasting a question straight at raw warehouse tables. A question is compiled into a query against that context layer plus the connected warehouse, so the accuracy of an answer depends on how completely that layer describes the underlying data. It supports dashboards, scheduled report delivery to Slack and email, and white-label embedding with SSO and row-level security for customer-facing use cases. Querio connects to warehouses such as Snowflake, BigQuery, and Postgres. Its Startup plan is self-serve and free to start with a fixed AI usage credit allowance; Core and Enterprise tiers add more connections, compute, and governance but are not publicly priced.

At a glance

Vendor Querio
Pricing model Free tier + paid plans
Free tier Yes
Deployment Cloud
Open source No
Best for Teams that want their AI query layer defined and versioned as code rather than configured in a closed SaaS UI.

Pricing

Free self-serve Startup plan with a fixed AI usage credit allowance; Core and Enterprise tiers add data connections, governance, and embedding but require contacting sales for pricing.

Plan Price Notes
Startup Free 1 production workspace, 1 data connection, 10 users, $250 AI usage credits, standard support
Core Not publicly listed 3 data connections, dashboards, automations, unlimited internal users, $1,000 AI usage credits
Enterprise Custom Custom connection limits, multiple workspaces, embedded analytics, dedicated compute, GovCloud support

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • Version-controlled ('analytics as code') semantic context layer
  • AI-powered notebooks for question answering and exploration
  • MCP, API, and CLI access for agent integrations
  • Scheduled dashboard and report delivery via Slack and email
  • White-label embedding with SSO and row-level security
  • Direct warehouse connections (Snowflake, BigQuery, Postgres)

Integrations

Profile last reviewed September 21, 2026

Alternatives

Fabi.ai AI-native notebook combining SQL, Python, and chat-based analysis for building reactive dashboards and scheduled reports. Wren AI Open-source 'GenBI' engine that gives AI agents a governed semantic layer (MDL) for turning questions into SQL against a warehouse. Zenlytic Governed semantic layer and AI data analyst that lets Claude, ChatGPT, Slack, or its own app query a company's metrics consistently. Julius AI Chat-based AI data analyst that answers questions over uploaded spreadsheets or connected databases and returns charts and explanations.

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Terms to know

Semantic layer A layer that defines business metrics and dimensions once, centrally, so every tool and report uses the same numbers. Natural language query Asking a data question in plain language and getting an answer without writing code or a query language. Dashboard A visual display of the metrics a team needs to monitor, usually updated on a schedule or in real time. Row-level security (RLS) Restricting which rows of a table a user can query, rather than granting or denying access to the whole table.

Related guides

How to choose an AI analytics assistant AI analytics assistants are only as accurate as the context they're given — choose by what they query and how that context is governed, not the demo.