Empowering Data-Driven Decisions with AI Credibility
The Genesis of Trust: From Cybersecurity to AI Insights
Shapor Naghibzadeh, drawing from his extensive experience as a Google sysops engineer during pivotal cybersecurity events like Operation Aurora in 2009, recognized the paramount importance of validated information. His time tracking complex cyberattacks across diverse networks underscored the immense value of verified knowledge, albeit a process that was often laborious and expensive. Naghibzadeh envisioned a future where large language models (LLMs) could streamline this verification process, applying the same rigor to various databases with unprecedented speed and efficiency.
Bridging the Gap: QueryStory's Vision for Verifiable AI
For six years, Naghibzadeh dedicated himself to the intersection of data and cybersecurity, developing tools at Google that enabled security analysts to dissect intricate data sets. This culminated in 2016 with his co-founding of Chronicle, a Google X Labs venture designed to extend similar capabilities to other organizations. The burgeoning role of large language models in data analysis last year presented a new opportunity for Naghibzadeh. He realized that the sophisticated techniques developed for cybersecurity could be adapted to enhance a broad spectrum of analytical tasks. This led to the creation of QueryStory, where he serves as CEO, alongside former Google colleague and EvolutionIQ lead engineer, CTO Stanley Yang, and Accenture veteran, CPO David Glusic. The company's official launch from stealth signifies a new chapter in AI-driven data solutions.
Narrative Intelligence: Crafting Truthful Data Stories
Naghibzadeh articulates the core philosophy behind QueryStory: "The process of investigation involves asking numerous questions of data, and once those questions are addressed, a coherent narrative emerges." This investigative approach is the essence of QueryStory's name, emphasizing the creation of data-driven narratives rooted in factual accuracy. The startup successfully secured $6 million in seed funding in late 2025 from Brightmind Ventures and New York Life Ventures, achieving a valuation of $60 million. This investment has fueled the development and piloting of their product, which is specifically designed for large enterprises managing proprietary databases. QueryStory acts as a central hub, unifying data analysis and review for various teams, including sales and operations managers.
Cultivating Trust: Actionable Insights for Enterprises
"Our objective is to close the trust deficit in AI, providing enterprises with answers they can confidently act upon," states Naghibzadeh. He highlights that QueryStory eliminates the need for extensive human intervention and large teams of engineers, instead offering a productized solution for critical data evaluation. Tim Del Bello, a partner at New York Life Ventures and an investor in QueryStory, attests to the platform's efficacy, using it to streamline quarterly business reviews and transition towards real-time dashboards. Del Bello notes that the product is ideal for decision-makers in highly regulated sectors who require profound insights from complex, disparate data sources but may lack dedicated data science or business intelligence teams.
Overcoming AI Fragility: Robust Systems for Critical Data
In a practical demonstration, QueryStory swiftly processed a database concerning space activity, generating sophisticated dashboards, analyses, and, critically, a confidence indicator for the AI's accuracy, a task that previously took weeks with human developers. While similar co-working tools exist from frontier labs, QueryStory differentiates itself by focusing on enhanced transparency, reliability, and control for enterprise users. For instance, unlike some LLM interfaces where users might manually verify SQL queries, QueryStory automatically surfaces these queries and allows for flagging analyses for human review, with all feedback meticulously recorded within the platform.
Economic Considerations and Sustainable AI Solutions
Tayler Sipperly, a partner at Brightmind Partners, emphasizes the often-underestimated fragility of AI when building durable, large-scale business solutions. Naghibzadeh points out the potential for data sprawl when organizations rely on basic LLM chat interfaces, leading to inconsistent interpretations of truth. QueryStory is designed to be model-agnostic, currently utilizing advanced models from leading labs, but distinguishes itself by offering a service provider model that prioritizes efficiency and accuracy over raw intelligence consumption. Naghibzadeh asserts that QueryStory's value lies in building trust in the provided answers and demonstrating clear business benefits, enabling CFOs to understand the direct impact and cost-effectiveness of their AI investments.
