QueryStory Exits Stealth With $6M Seed, Promising to Turn Enterprise Data Into Decisions
QueryStory launched publicly on August 26, 2026, backed by a $6 million seed round that closed in late 2025 at a $60 million valuation. Brightmind Ventures and New York Life Ventures led the financing, according to a TechCrunch report published the same day. The company's website is querystory.ai.
The startup calls itself an "agentic data platform" — meaning it uses AI agents to automatically pull together information from across a company's systems and turn it into narratives or reports that business teams can act on (QueryStory Newsroom). In practice, the platform connects to both structured data sources (like databases and spreadsheets) and unstructured ones (like call recordings and presentation decks). It integrates with Snowflake, Databricks, and BigQuery — three of the most widely used cloud data platforms. Users can publish the platform's output as a slide deck, document, dashboard, or chat message.
QueryStory markets its platform to teams across revenue operations, finance, marketing, sales, operations, and data analytics. The company emphasizes that customer data stays in the customer's own cloud environment and region, on infrastructure the customer controls, and that nothing trains on customer data.
The founding team brings deep infrastructure and security backgrounds. CEO Shapor Naghibzadeh co-founded Chronicle, a startup that came out of Google's X Labs, in 2016. Before that, he was a Google systems engineer who worked on the response to Operation Aurora, the major 2009 cyberattack on Google. CTO Stanley Yang, a former Google colleague of Naghibzadeh, was lead engineer at EvolutionIQ. Chief Product Officer David Glusic is an Accenture veteran.
Tim Del Bello, a partner at New York Life Ventures, invested in QueryStory and uses its platform for quarterly business reviews. That a venture investor is also an active enterprise customer is a data point worth noting, though it is not uncommon in early-stage enterprise SaaS.
The architecture choices here deserve attention. Keeping all AI processing within a customer-controlled cloud boundary, with no training on customer data, aligns with the zero-trust and data-sovereignty expectations — the idea that companies should never implicitly trust an external vendor with their data — that mature enterprise buyers now bring to procurement. For teams operating under regulatory constraints or handling sensitive call recordings and financial data, that posture reduces a category of vendor risk that has slowed enterprise adoption of AI-native analytics tools.
The broader context is the gap between traditional business intelligence dashboards and decision-ready output. QueryStory's pitch is that an AI agent layer can sit between raw data, wherever it lives, and the reports and presentations a business team actually consumes — think of it as a research analyst that reads across all your systems and writes up the findings automatically. Whether agent-driven synthesis produces reliable enough output to replace human-curated reporting is an open question the market will answer through real deployment. The founding team's experience in security infrastructure and applied machine learning gives the company credibility to attempt it, but the gap between what an AI agent can produce and what an enterprise trusts enough to act on remains the key variable.
If the platform delivers on its promise of connecting disparate structured and unstructured sources into actionable narratives, it could reduce the friction between data engineering teams and the business leaders who need answers. The integration model — sitting across warehouses and CRMs without moving data out of customer-controlled environments — is architecturally sound for that use case. The $60 million valuation at seed suggests investors are pricing in meaningful enterprise demand for this category.
