Shapor Naghibzadeh learned the value of a good story in 2009 as a Google SysOps engineer. His experience during the Operation Aurora cyberattack highlighted the need for verified knowledge across disparate systems. This insight led him to co-found a startup in Google’s X Labs in 2016 called Chronicle, which aimed to bring similar functionality to other companies. QueryStory, the company he now leads, builds on that vision by leveraging AI to help users extract insights from complex data sources.

Naghibzadeh’s journey began in 2009 when he was tasked with explaining the impact of a major cyberattack on Google’s infrastructure. The process of tracing the attack across multiple networks was both time-consuming and expensive. This experience taught him the importance of clear, verified information in decision-making. In 2016, he co-founded Chronicle, a company that aimed to simplify the process of analyzing complex data for enterprises. That early work laid the foundation for QueryStory’s current product, which focuses on making AI-generated insights more reliable and actionable for non-technical users.

QueryStory’s product was built with a specific audience in mind: decision-makers who need to work with complex, disparate data sources but lack a dedicated data science or BI team. The company’s co-founder, Naghibzadeh, emphasized that the product is designed to help users find the 'ground truth' in their data without requiring advanced technical skills. This approach aligns with the growing demand for AI tools that can simplify data analysis for a broader range of users. The company has spent the time since its seed round in late 2025 developing and piloting its platform with various organizations.

The implications of QueryStory’s approach could be significant for businesses that rely on data-driven decisions. By reducing the need for specialized technical teams, the company’s AI tools may lower the cost of data analysis and make it more accessible to a wider range of organizations. However, the reliance on AI to interpret complex data also raises concerns about accuracy, governance, and the potential for vendor lock-in. As the market for AI-driven analytics continues to grow, companies will need to carefully evaluate the trade-offs between ease of use and the reliability of AI-generated insights.

QueryStory is still in the early stages of development, with its product continuing to evolve based on user feedback and pilot programs. The company’s $6 million seed round in late 2025 at a $60 million valuation signals strong investor confidence in its vision. As the startup moves forward, it will need to address key challenges such as ensuring the accuracy of its AI models, maintaining data governance standards, and differentiating itself in a competitive market. The success of QueryStory will depend on its ability to deliver reliable, actionable insights that meet the needs of its target users.