QueryStory, an AI data analytics startup, emerged from stealth on August 26, 2026, with $6 million in seed funding and a platform designed to help large enterprises trust and act on AI-generated insights from complex, proprietary databases.
The company was co-founded by CEO Shapor Naghibzadeh, a former Google systems engineer and co-founder of Chronicle, a cybersecurity data startup built inside Google’s X Labs in 2016. He is joined by CTO Stanley Yang, a former Google engineer and ex-lead engineer at EvolutionIQ, and CPO David Glusic, a veteran of Accenture.
Naghibzadeh’s approach draws directly from his cybersecurity background. While working at Google during Operation Aurora — a 2009 cyberattack attributed to Chinese state-backed hackers — he learned to trace threats across disparate data networks, a process that was both slow and resource-intensive. QueryStory applies similar investigative techniques to general business analytics, using large language models to assemble data queries into coherent, verifiable narratives.
The seed round, raised in late 2025, was led by Brightmind Ventures and New York Life Ventures at a valuation of $60 million. Tim Del Bello, a partner at New York Life Ventures, said he is using the platform to replace the work of several people and produce a quarterly business review. “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,” he said.
A key feature of the platform is a confidence indicator that surfaces why AI agents consider a given analysis accurate. SQL queries generated during analysis are automatically visible to users, who can flag results for human review — a step that must be manually requested in competing tools from frontier AI labs.
QueryStory is currently model-agnostic and targets enterprises that need predictable costs and auditable outputs. “The thing that we are selling is the trust in the answers,” Naghibzadeh said, noting the platform is designed to give CFOs clarity on what the service will cost rather than relying on token- or compute-based pricing models.
Source: TechCrunch