QueryStory launches enterprise AI analytics platform with $6M seed
QueryStory has emerged from stealth with a $6 million seed round raised in late 2025 at a $60 million valuation. The enterprise analytics startup was founded by CEO Shapor Naghibzadeh, CTO Stanley Yang and CPO David Glusic, and is backed by Brightmind Ventures and New York Life Ventures.
The company is building a platform for large enterprises that need to analyse complex, proprietary databases while retaining visibility into how AI-derived answers were produced. Its stated aim is to unite data analysis and review for business users such as sales teams and operations managers.
From cybersecurity investigations to enterprise analytics
Naghibzadeh traces the product’s premise to his work as a Google sysops engineer during Operation Aurora in 2009. Investigating activity across disparate networks showed him the importance of verified knowledge, but also the cost and time required to assemble it.
He later spent six years working at the intersection of data and cybersecurity, building tools that enabled analysts to query complex datasets. In 2016, he co-founded Chronicle within Google X Labs to extend that capability to other companies. QueryStory applies the same investigative pattern to broader analytics: users ask a series of questions, then assemble the resulting evidence into a narrative grounded in the underlying data.
Making AI analysis inspectable
QueryStory has spent the period since its financing developing and piloting its product with customers. In a demonstration using a database of space activity, the platform generated visualisations, dashboards and analysis in a few hours for work that had previously taken a developer several weeks.
A central feature is a confidence indicator that explains why the AI agents regard an analysis as accurate. The platform also surfaces the SQL queries automatically, rather than leaving users to request them separately. Users can flag analyses for review by human colleagues, and those reviews are recorded within the product.
The design responds to a governance problem created when many employees independently query an LLM chat interface. Naghibzadeh argues that this can produce a sprawl of presentations and conclusions without a shared place that connects the content back to the data. QueryStory is intended to preserve that connection while allowing teams to work from a common analytical record.
Model choice and operating economics
The platform is model-agnostic, although it currently relies mainly on the latest models offered by frontier labs. QueryStory competes with those providers at the product layer, but its founders argue that a purpose-built service can preserve context and offer more control than a general-purpose agent.
New York Life Ventures partner Tim Del Bello said he uses the platform to replace work previously performed by several people for a quarterly business review, with the aim of moving towards a real-time dashboard. He described the product as relevant to decision-makers who manage disparate data sources without a data science or business-intelligence team, particularly in regulated industries.
For businesses adopting AI analytics, the practical implication is to evaluate not only the answer a model returns, but also whether teams can inspect its queries, understand its confidence signals, retain review records and trace decisions back to the underlying data.