QueryStory brings verifiable answers to enterprise AI data analysis

QueryStory, a data analytics platform that marries AI-powered querying with human oversight, emerged from stealth mode today. The startup was founded by Shapor Naghibzadeh, who drew on his experience responding to the 2009 Operation Aurora cyberattack at Google to build tools that make disparate datasets more accessible while preserving trust in the results.

The founding team also includes Stanley Yang, a former Google engineer and EvolutionIQ lead, and David Glusic, who previously worked at Accenture. The company secured a $6 million seed round in late 2025 from Brightmind Ventures and New York Life Ventures, valuing it at $60 million.

The problem QueryStory solves#

When enterprise teams integrate large language models into their workflows, they often end up with fragmented results. Multiple departments query the same databases independently, each receiving slightly different analyses based on how they framed their questions. These scattered insights then proliferate across the organization as inconsistent reports and presentations, untethered from the underlying data.

Naghibzadeh characterizes the challenge this way: "You get hundreds or thousands of people within an organization all asking their questions and getting their version of the truth and putting that in a slide deck and sharing it—you just end up with this huge sprawl of content, and there's no real place to hang that content that ties back to the data."

How the platform works#

QueryStory positions itself as a purpose-built alternative to general-purpose AI agents. The platform automatically surfaces the underlying SQL queries that models generate, allowing users to verify the logic before acting on results. Findings can be flagged for human team members to review, with those reviews logged in the system for accountability.

The startup demonstrated its capabilities on a proprietary space industry database—a task that traditionally required weeks of developer time but QueryStory completed in hours, generating sophisticated dashboards alongside confidence indicators that explain why the AI assessed each analysis as reliable.

A different business model#

Unlike frontier AI labs that profit from token consumption, QueryStory operates on the premise that enterprises will prefer a vendor focused on delivering trustworthy insights rather than maximizing compute usage. The platform remains model-agnostic, currently using leading foundation models but designed to work across providers.