QueryStory wants you to believe what AI is telling you
QueryStory, a new startup co-founded by Shapor Naghibzadeh, emerged from stealth with $6 million in seed funding to help large enterprises trust AI-driven data analysis by providing verifiable narratives and confidence indicators.
Intelligence analysis by Gemini 2.5 Flash
Addressing the challenge of AI reliability, QueryStory offers a platform for enterprises to query complex proprietary databases using large language models. It aims to generate trustworthy data narratives with transparency and control, mitigating the brittleness often associated with current AI systems and reducing the need for extensive human oversight.
Imagine you have a huge pile of puzzle pieces (your company's information) and you want to quickly see the full picture of what's happening. Usually, you'd need a super-smart detective (a data expert) to carefully put all the pieces together. QueryStory is like a special robot detective that can quickly look at all your puzzle pieces, figure out the story they tell, and even show you exactly how it connected them, so you know its story is true and you can trust it.
Analysis
Shapor Naghibzadeh
Shapor Naghibzadeh, the CEO and co-founder of QueryStory, brings a wealth of experience from his tenure at Google, where he learned the critical importance of verified knowledge. His background includes working as a sysops engineer during the infamous Operation Aurora cyberattacks in 2009, an experience that underscored the time-consuming nature of tracing and verifying information across disparate networks. This foundational understanding of data integrity and the challenges of complex data environments directly informed his later ventures.
Naghibzadeh spent six years at Google focusing on the intersection of data and cybersecurity, developing tools that empowered security analysts to query intricate data sets efficiently. This work culminated in his co-founding of Chronicle in 2016, a startup within Google's X Labs, which aimed to extend similar data analysis capabilities to other companies. His journey reflects a consistent drive to make complex data more accessible and trustworthy, a mission that now finds its latest expression in QueryStory.
Operation Aurora
The 2009 cyberattacks, known as Operation Aurora, were a pivotal moment in Shapor Naghibzadeh's career, profoundly shaping his understanding of data verification. As a Google sysops engineer, he was tasked with explaining the intricate details of the attacks unfolding on the company's servers. This high-stakes environment necessitated meticulous tracing of cyberattacks through various networks, a process that was both costly and incredibly time-consuming.
The experience of Operation Aurora instilled in Naghibzadeh the profound value of verifiable knowledge and the inherent difficulties in achieving it at scale. He recognized that while large language models (LLMs) offer immense potential for data analysis, they also introduce new challenges related to trust and accuracy. This realization became a core driver for QueryStory, which seeks to apply the lessons learned from cybersecurity — particularly the need for robust verification — to broader enterprise data analytics, aiming to deliver reliable insights in a fraction of the traditional time.
Brightmind Ventures
QueryStory successfully raised a $6 million seed round in late 2025, with significant contributions from investors like Brightmind Ventures and New York Life Ventures, valuing the company at $60 million. This funding has enabled the startup to develop and pilot its product with initial customers, demonstrating market confidence in its unique approach to AI-driven data analysis. Tayler Sipperly, a partner at Brightmind Partners, emphasized the brittleness of current AI systems when deployed for durable, large-scale business operations, highlighting QueryStory's value proposition in addressing this critical weakness.
Tim Del Bello, a partner at New York Life Ventures and an investor, is also a user of the platform, leveraging it to streamline quarterly business reviews and transition towards real-time dashboards. He noted that the product is designed for decision-makers who require ground truth from complex, disparate data sources but lack dedicated data science or business intelligence teams, especially within highly regulated industries. This dual role as investor and user underscores the practical utility and perceived necessity of QueryStory's solution in bridging the AI trust gap for enterprises.
Key points
- QueryStory, co-founded by Shapor Naghibzadeh, emerged from stealth with a $6 million seed round at a $60 million valuation.
- The platform aims to bridge the trust gap in AI for large enterprises by providing verifiable data narratives and confidence indicators.
- It allows users to query complex proprietary databases using LLMs, surfacing SQL queries automatically and enabling human review of analyses.
- The company's approach is rooted in Naghibzadeh's experience tracing cyberattacks and building data analysis tools at Google and Chronicle.
- QueryStory is designed to be model-agnostic and offers a more efficient, accurate alternative to general-purpose AI agents for enterprise data analysis.
QueryStory could significantly enhance trust in AI-generated insights, enabling enterprises to make faster, more informed decisions based on verifiable data narratives. This increased reliability could lead to greater efficiency, reduced operational costs by productizing human judgment, and broader adoption of AI in critical business functions.
Despite its aims, QueryStory faces the inherent brittleness of AI systems, and if not robustly implemented, could still lead to a 'sprawl of content' where different users get varying 'versions of the truth.' Competition from frontier labs and the challenge of convincing enterprises to adopt a new model-agnostic platform also pose potential hurdles.
