Querio Ltd
Querio builds a git-native analytics platform for data teams and their AI agents. It keeps metrics, dashboards, notebooks, permissions, business context and agent instructions in a GitHub repository owned by the customer, so people and AI tools can work from consistent, governed definitions.
At a Glance
- Data teams and analytics teams
- Companies adopting AI assistants and coding agents
- Startups and growth companies rolling out governed analytics
- Enterprise organizations requiring SSO, private hosting, compliance and custom SLAs
- +1 more
AI Tools by Querio Ltd
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Querio
Git Native AI Analytics Platform
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Products & Services
An analytics workspace where users ask questions about connected data, inspect and edit SQL and Python, iterate in notebooks, and save work as shared explores, boards and reports.
A customer-owned, Git-backed context layer for definitions, metrics, table descriptions, relationships, business rules and instructions used by agents and tools.
MCP server, Slack agent, CLI and API that let Claude, ChatGPT, Cursor or a customer's own agent work against governed Querio data and context.
Agent-assisted notebooks for asking a question, viewing the analysis steps, inspecting SQL/Python and results, then refining and reusing the work.
Market Position
Querio positions itself as a git-native, agent-ready alternative to conventional BI and semantic-layer products: unlike Tableau, Looker and Power BI, it puts analytics definitions, dashboards, permissions and context in a customer-owned Git repository and exposes them to multiple AI tools through MCP, Slack, CLI and API. Its closest competitive set includes those incumbent BI tools, modern semantic/metrics layers and warehouse-native self-service analytics products; its differentiator is combining governed context-as-code, human review and agentic analysis in one workflow.
Leadership
Founders
Rami Abi Habib
Engineer and product builder; his public profile describes seven years building products including robotics and applied machine learning at Amazon and Data.world before founding Querio. He is Querio's Co-founder and CEO.
Javier Bonilla
Technology/product operator and University of Texas at Austin alumnus; SEC offering materials describe him as an early employee at two acquired startups. He is Querio's Co-founder and CTO.
Executive Team
Rami Abi Habib
Co-founder & CEO
Engineer and product builder with experience at Amazon and Data.world in robotics, applied ML and product development; founder of Querio.
Javier Bonilla
Co-founder & CTO
Technology operator and early employee at two acquired startups; University of Texas at Austin alumnus.
Founding Story
The founders said that decades of BI, cloud-warehouse and dbt tooling left company logic trapped inside vendor products, while employees asking the same data question in Claude, Slack and ChatGPT could receive different answers. They started Querio to put analytics logic and context in a GitHub repository owned by the data team, allowing agents to use it while people review changes before they reach the business.
Business Model
Revenue Model
Subscription SaaS with a free plan and paid Core and Enterprise plans; usage above included AI-credit allowances is billed per credit, while Enterprise pricing is custom.
Pricing Tiers
Free forever; unlimited users and data connections, 500 AI credits/month, agent, notebooks, dashboards, Slack agent, MCP server, CLI and support.
For companies rolling out governed analytics; 10,000 AI credits/month, SSO and group sync, permissions, API access, SSH tunneling/VPN, guided pilot and private Slack support. The comparison table also shows $2,499 as the monthly list price.
Custom AI-credit allowance, single-tenant hosting, AWS Bedrock inference, GovCloud/on-prem add-ons, dedicated solutions engineer, on-call escalation and custom SLA.
Target Markets
- Data teams and analytics teams
- Companies adopting AI assistants and coding agents
- Startups and growth companies rolling out governed analytics
- Enterprise organizations requiring SSO, private hosting, compliance and custom SLAs
- Industries with warehouse/database-driven reporting, including SaaS, healthcare and financial/operational teams
- Governed self-service analytics for business users
- Asking natural-language questions of warehouse and database data
- Giving AI assistants consistent company metrics and business context
- Data-team review of agent-generated SQL, Python, metrics and dashboards
- Replacing or migrating from Tableau, Looker, Power BI and ad hoc internal analytics apps
- Scheduled operational reports in email and Slack
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