In 2009, Shapor Naghibzadeh, then a sysops engineer at Google, learned a crucial lesson about the power of verified narratives. When state-backed hackers targeted Google in an operation dubbed Aurora, he was thrust into a high-stakes "war room" to decode the chaos in the company's servers. Tracing cyberattacks across fragmented networks taught him the immense value of verified knowledge—but the process was slow, costly, and labor-intensive.
Now, Naghibzadeh believes large language models (LLMs) can bring the same investigative rigor to any database, in a fraction of the time. This vision is now embodied in QueryStory, a startup he co-founded to help enterprises trust and act on AI-driven insights, which emerged from stealth today.
From Security to Storytelling
After Aurora, Naghibzadeh spent six years at the intersection of data and cybersecurity, leveraging Google's resources to build tools that let security analysts query complex data efficiently. In 2016, he co-founded Chronicle within Google X Labs to commercialize that capability. But as LLMs gained prominence in data analysis, he saw an opportunity to adapt his cybersecurity techniques for broader analytics.
"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 explains. "That became the genesis for the name QueryStory. It's about telling stories with data, right? Putting a narrative together that's grounded in truth."
A New Platform for Enterprise Trust
QueryStory targets large enterprises with proprietary, complex databases. Its platform unifies data analysis and review, catering to users like sales teams and operations managers who need clarity without deep technical expertise. The company raised a $6 million seed round in late 2025 from Brightmind Ventures and New York Life Ventures, at a $60 million valuation, and has spent recent months refining its product with pilot customers.
The core challenge, as Naghibzadeh sees it, is bridging the "trust gap" in AI. "What we're doing is bridging that trust gap for AI to give enterprises answers that they can act on," he says. "Instead of renting human judgment and armies of forward-deployed engineers, we productized that."
Real-World Validation
Tim Del Bello, a partner at New York Life Ventures, is both an investor and a user. He's replacing several employees' manual work with QueryStory to produce quarterly business reviews—and hopes to turn them into real-time dashboards. "The product was built for people like me: decision-makers seeking the ground truth who need to work with complex, disparate data sources but don't have a data science or BI team at their disposal, especially when operating in a highly regulated industry," he told TechCrunch.
I tested QueryStory with a space activity database, a project that once took me weeks with a developer. QueryStory generated visualizations and sophisticated dashboards within hours. Most notably, it included a confidence indicator that showed why the AI agents believed their analyses were accurate—a feature critical for building user trust.
As we move into 2026, the demand for transparent, auditable AI is exploding, especially in sectors like insurance and finance. QueryStory's emphasis on explainable AI and grounded narratives positions it well to capitalize on this trend, offering a practical answer to the question: How can we believe what AI tells us?
via TechCrunch AI