QueryStory, now out of stealth, is trying to make AI-generated answers trustworthy enough for large enterprises to act on. CEO Shapor Naghibzadeh traces the idea to his work as a Google sysops engineer during the Operation Aurora attacks, when tracing hackers through disparate networks taught him the value of verified knowledge and how costly it is to assemble. He later co-founded Chronicle in Google X Labs to bring similar data-query tooling to other companies, and last year started QueryStory with CTO Stanley Yang and CPO David Glusic. 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 the intervening time developing and piloting its product with customers.

QueryStory targets large enterprises with big proprietary databases and users such as sales teams, operations managers, and executives who need answers without a dedicated data science or BI team. Its platform unites data analysis and human review: SQL queries surface automatically, users can flag analyses for coworkers, and those reviews are recorded in the system. When TechCrunch shared a database of space activity, QueryStory produced a visualization in a few hours, where a comparable project once took weeks with a developer; the output included dashboards and a confidence indicator that explained why the AI agents believed the analyses were accurate. Investor Tim Del Bello of New York Life Ventures, who is using the product inside his own work, said it was built for people like him: decision-makers seeking ground truth from complex, disparate data sources in a highly regulated industry. He is using it to replace the work of several people on a quarterly business review, which he hopes will become a real-time dashboard.

Naghibzadeh frames the product as bridging the trust gap that appears when companies wire their data into a general-purpose LLM chat UI: potential chaos from thousands of employees getting their own version of the truth and sharing it in slide decks, with nothing tying the output back to the source data. That is why QueryStory surfaces the queries and reasoning behind answers, and why it is positioned against the co-working tools built by frontier labs, which he says are intentionally limited in their user experience. Brightmind Partners’ Tayler Sipperly said AI is more brittle than people realize when it comes to durable systems that large businesses depend on. QueryStory is model-agnostic, though it now mainly uses the latest frontier-lab models, and Naghibzadeh argues the startup has a structural advantage because it was not built around a consumption model for compute, storage, or tokens. A purpose-built analytics agent can preserve context, be more efficient, and ultimately sell trust in the answers, he said — with the goal of giving the CFO a clear picture of what a deployment will cost.