QueryStory emerges from stealth to bring verified answers to enterprise data

Shapor Naghibzadeh learned the value of a good story in 2009 as a Google SysOps engineer, when he was called into a war room to explain what was happening on the company’s servers during Operation Aurora, a cyberattack effort backed by China. Tracing the attacks across disparate networks taught him the value of verified knowledge — but it was costly and time-consuming. He now believes large language models can bring that same functionality to databases of all kinds in a fraction of the time.

Naghibzadeh spent the next six years at the intersection of data and cybersecurity, using Google’s resources to build tools that let security analysts query complex data, and in 2016 co-founded a startup inside Google’s X Labs called Chronicle to bring that functionality to other companies. Last year, as large language models took a bigger role in data analysis, he saw an opportunity to apply the techniques he developed for cybersecurity to a wider range of analytics, and co-founded QueryStory, where he is CEO, alongside CTO Stanley Yang, a former Google colleague and lead engineer at EvolutionIQ, and CPO David Glusic, an Accenture veteran. The startup emerged from stealth today.

“You get this pattern of an investigation — you ask a bunch of questions of the data, and after you have been able to ask a number of questions, you assemble that together into a narrative,” Naghibzadeh said. “That became the genesis for the name QueryStory. It’s about telling stories with data.” QueryStory raised a $6 million seed round in late 2025 from Brightmind Partners and New York Life Ventures at a $60 million valuation, and has spent the time since developing and piloting its product with customers. The startup targets large enterprises managing big, proprietary databases, serving as a platform to unite data analysis and review for users like sales teams or operations managers; Naghibzadeh described the goal as bridging the trust gap so enterprises get AI answers they can act on, rather than renting human judgment or armies of forward-deployed engineers.

Tim Del Bello, a managing director at New York Life Ventures who led the fund’s investment, is also using the platform internally to replace work several people previously did to produce a quarterly business review, which he hopes will become a real-time dashboard; he said the product was built for decision-makers who need to work with complex, disparate data sources but lack a dedicated data-science or BI team, especially in regulated industries. In a demonstration for TechCrunch, the outlet shared a database of space activity useful for understanding what companies like SpaceX are doing in orbit, and QueryStory produced a visualization in a few hours — a project TechCrunch said once took a developer several weeks — along with a confidence indicator showing why the AI agents believed their analyses were accurate. QueryStory is built to be model-agnostic, though it currently mainly uses frontier labs’ latest models; Naghibzadeh argues customers will prefer a service provider not incentivized to sell as much intelligence as possible, and that a purpose-built tool can be more efficient and accurate than a general-purpose agent by preserving context. In QueryStory, the SQL queries behind an analysis surface automatically, and users can flag analyses for human colleagues to review, with those reviews recorded in the platform — an approach Brightmind Partners partner Tayler Sipperly said addresses how brittle AI can be when businesses rely on it for durable, large-scale work.

Why it matters

QueryStory’s bet is that enterprises adopting AI for data analysis will pay for transparency and auditability — visible SQL, confidence scores, human review trails — rather than just raw answers from a general-purpose chat interface.