Shapor Naghibzadeh, a former Google systems engineer, learned the importance of a clear investigative narrative in 2009 when he was pulled into a war room during Operation Aurora to explain activity on Google’s servers. Tracing attacks across disparate networks highlighted for him the value of verified knowledge, but also how slow and costly such investigations can be.
Over the next six years Naghibzadeh worked at the intersection of data and cybersecurity at Google, building tools that helped security analysts query complex datasets. In 2016 he co-founded Chronicle out of Google X Labs to bring that capability to other companies.
As large language models began to play a larger role in data analysis last year, Naghibzadeh saw an opportunity to adapt the techniques he had developed for cybersecurity to broader analytics use cases. He co-founded QueryStory, where he is CEO; Stanley Yang, a former Google colleague and lead engineer at EvolutionIQ, is CTO; and David Glusic, who previously worked at Accenture, is CPO. The startup emerged from stealth today.
"There’s a pattern to an investigation—you ask a series of questions of the data, and after you’ve asked enough, you assemble those answers into a narrative," Naghibzadeh said. That idea informed the company’s name: QueryStory is about building data-grounded narratives.
Funding and market focus
QueryStory raised a $6 million seed round in late 2025 from Brightmind Ventures and New York Life Ventures at a $60 million valuation. Since then the team has been developing the product and piloting it with customers. The platform targets large enterprises that manage substantial proprietary databases and aims to unify data analysis and review for users such as sales teams and operations managers.
Product features aimed at trust
The company positions its product as a way to bridge the trust gap between AI outputs and enterprise decision-makers, delivering answers that can be acted on without "renting human judgment" at scale. Key features include automatically surfacing the SQL queries generated during analysis, workflows for flagging results for human review, and recording those reviews in the platform.
In one TechCrunch trial, QueryStory was given a database of space activity and produced visualizations, sophisticated dashboards and analyses in a few hours—work that previously would have taken weeks with a developer. Notably, the platform exposes a confidence indicator that explains why the AI believes its analysis is accurate.
Naghibzadeh warns that when companies simply connect data to an LLM chat UI, hundreds or thousands of employees can each generate their own "version of the truth" and spread inconsistent findings across slide decks. QueryStory aims to prevent that sprawl by offering transparent, auditable outputs tied back to source data.
Model-agnostic approach and economics
The platform is model-agnostic, although it currently uses the latest models from frontier labs. Naghibzadeh argues that customers will prefer a service provider that is not incentivized to maximize model consumption — in contrast to vendors whose business models depend on increased compute or token usage.
"What we’re selling is trust in the answers," Naghibzadeh said. The company emphasizes delivering measurable business value and helping finance leaders understand potential costs.
Investor perspectives
Tim Del Bello, a partner at New York Life Ventures who invested in the seed round, is using QueryStory to replace the work of several people and hopes to turn a quarterly business review into a real-time dashboard. Tayler Sipperly, a partner at Brightmind Partners, said AI is often more brittle in production contexts than people realize, underscoring the need for transparency, reliability and scale.
Conclusion
QueryStory’s offering combines LLM capabilities with traditional database queries to provide explainable, auditable analytics for large enterprises. By surfacing SQL, enabling human review workflows and providing confidence indicators, the startup aims to reduce the brittleness of AI-driven analysis and the proliferation of inconsistent data narratives within organizations.
