As businesses increasingly rely on artificial intelligence to make decisions, a new challenge is emerging: different AI tools can produce different answers to the same business question. London-based startup Querio is aiming to solve this problem by giving companies a shared foundation for their data, helping employees access consistent and reliable information wherever they work. The company has raised $5 million in funding as it launches its platform, with plans to invest in further technology development.
Investors Back Querio’s AI Vision
Querio’s funding round includes backing from BMW i Ventures, Haatch Ventures, Arāya Ventures, Forum Ventures, Secways and LAUNCH, the investment fund associated with Jason Calacanis. The company has also received support from angel investors with backgrounds at OpenAI and Google. BMW i Ventures led the seed financing.
Founded in 2024, Querio is building an analytics platform designed to connect business data, company definitions and AI agents. Its goal is to make data analysis more accessible to employees who may not have technical expertise while allowing data teams to maintain control over how business information is interpreted.
The newly raised capital will support investment in Querio’s technology as the company develops its platform for organisations adopting AI across their operations.
The Problem of Conflicting AI Answers
AI assistants such as Claude and ChatGPT are changing how employees interact with business information. Instead of waiting for a report from a data analyst, staff can ask questions about sales, revenue, customer behaviour and operational performance directly through conversational interfaces.
However, these tools may interpret business data differently when they lack access to the same definitions and rules. One assistant might calculate revenue using one set of criteria, while another relies on a different interpretation.
This creates confusion for employees and additional work for data teams, which must investigate discrepancies, correct reports and ensure that business decisions are based on accurate figures.
Querio aims to address this issue by establishing a common layer of business knowledge that AI assistants and analytics tools can use. Rather than allowing each tool to maintain its own interpretation, the platform helps organisations define important metrics and business rules in one place.
Putting Data Teams in Control
According to Querio’s official website, the platform stores business context, analytical logic and definitions in a Git repository controlled by the data team. This approach allows organisations to track changes, review updates and maintain ownership of their business logic rather than keeping it locked inside a proprietary analytics product.
Its tools support notebooks, dashboards, reports and integrations with AI assistants through interfaces such as Slack, APIs and the Model Context Protocol, or MCP.
The platform is also designed to work with existing data warehouses and analytics systems, allowing businesses to build on their current infrastructure rather than replace every tool they already use.
AI Agents That Help Maintain Accuracy
Querio’s approach goes beyond answering questions. Its AI agents can help identify changes in underlying data models and determine which definitions, reports or analyses may need updating.
For example, if a sales team renames a customer relationship management field, existing dashboards and automated reports could stop working correctly. Querio aims to help identify the change, update dependent logic and present the proposed modifications for review.
Human oversight remains central to the process, with data teams retaining control over changes that affect business reporting.
Co-founder and CEO Rami Abi Habib says the company wants businesses to own their analytical logic so that every AI tool can draw on the same definitions. Co-founder and CTO Javier Bonilla is also helping develop the platform around this shared approach to analytics.
Competing in the AI Analytics Market
As AI becomes a more common interface for business intelligence, companies are looking for ways to make data accessible without sacrificing reliability or governance.
Querio’s challenge will be to demonstrate that its shared context approach can reduce inconsistencies while fitting into complex enterprise data environments. Its focus on version control, reusable definitions and human reviewed changes positions it within the growing market for AI powered analytics infrastructure.
With $5 million in funding, Querio plans to strengthen its technology and support its market launch. Its broader ambition is to help employees across an organisation use AI to answer business questions while ensuring that the numbers behind those answers remain consistent.