Querio is a direct application of agentic principles to the Business Intelligence stack. While many tools offer a 'copilot' sidebar, Querio's core product is an AI agent that manages a notebook session, iteratively exploring data based on user intent. This transition from a single-turn query to a multi-turn, stateful exploration is what defines it as an agentic platform rather than a simple utility.
In the broader ecosystem, Querio is a notable contributor to the Model Context Protocol (MCP). By providing an MCP server, they allow external agents—such as those running in IDEs or dedicated agent frameworks—to securely access and query databases through Querio's context-aware layer. This makes them a critical 'tool' or 'skill' for other agents, effectively modularizing data analysis for the wider AI workforce. They are championing the idea that data access should be a capability provided by a specialized agent rather than a raw connection string.
Querio is a business intelligence platform built on the premise that traditional dashboards are too rigid and static for the modern pace of product development. Instead of forcing users to navigate pre-built charts or wait for a data analyst to write a SQL query, Querio provides what they call agentic notebooks. This interface allows any team member to write prompts in natural language and receive live data insights. The system is an iterative environment where the AI agent doesn't just return a single table, but allows for follow-up questions, tagging of specific data cells for context, and the assembly of these insights into sharable boards.
Technically, the platform operates as a layer on top of existing data infrastructure. It supports a wide array of integrations including PostgreSQL, MySQL, Snowflake, BigQuery, and MotherDuck. Security is a primary focus of the architecture, evidenced by their offer of a self-hosted Code Execution Environment. This allows companies to deploy Querio within their own infrastructure, ensuring that sensitive data remains behind their firewalls while still benefiting from the AI-driven analysis.
A recurring problem with text-to-SQL systems is the risk of hallucination or incorrect joins when the AI doesn't understand the specific business logic of a database. Querio addresses this through "Golden Queries." These are pre-verified SQL segments provided by technical teams that act as guardrails for the AI agent. When a user asks a question, the agent references these golden queries to understand the correct way to calculate specific metrics or join certain tables. This grounding mechanism aims to make the output reliable enough for data leaders to trust it for actual decision-making.
Beyond internal team use, Querio offers an Embedded API. This allows software companies to integrate agentic data analysis directly into their own products. Instead of building a complex, hard-coded analytics tab for their customers, developers can use Querio to provide a conversational data interface. This includes a Control Center where teams can monitor production requests, test runs, and metadata in real time. It effectively allows a SaaS company to outsource the entire 'chat with your data' feature set to a specialized agentic service.
Querio is based in London and has raised funding from investors including Forum Ventures and Haatch. The company is led by product-focused founders like Javier Bonilla, who emphasizes a problem-first approach to software development. They are active participants in the emerging agentic ecosystem, recently contributing to the Model Context Protocol (MCP) servers. This indicates a strategic move toward interoperability, allowing other AI agents to potentially use Querio as their primary interface for querying structured data. By positioning themselves as the context layer for databases, Querio aims to be the standard way technical and non-technical users alike interact with company data.
Agentic notebooks for instant data insights and exploration.
Querio is hiring.