QueryStory emerged from stealth today with a $6 million seed round raised in late 2025 at a $60 million valuation, betting that large enterprises want confidence scores and audit trails on AI-generated data analysis, not another chat box. Brightmind Ventures and New York Life Ventures led the round. The platform is aimed at sales teams, operations managers, and executives who need to query proprietary databases without a data science team behind them.
CEO Shapor Naghibzadeh co-founded QueryStory with CTO Stanley Yang, a former Google colleague and lead engineer at EvolutionIQ, and CPO David Glusic, an Accenture veteran. The three built the company around a specific claim: that connecting an LLM to a company database and letting hundreds of employees ask their own questions produces sprawl, not truth. QueryStory surfaces the SQL queries the model writes, attaches a confidence indicator to each analysis, and lets users flag results for human review that gets recorded in the platform.
Naghibzadeh traces the idea back to 2009, when he was a Google sysops engineer pulled into the war room during Operation Aurora, the China-linked intrusion campaign that hit the search giant. Reconstructing what attackers had done across disparate systems taught him that verified knowledge was valuable and expensive. He spent the next six years on data and security tooling at Google, then co-founded Chronicle inside the company's X Labs in 2016 to commercialize the same investigative pattern for other enterprises.
“You get this pattern of an investigation — you ask a bunch of questions of the data, and after you have been able to ask a number of questions, you assemble that together into a narrative.”— Shapor Naghibzadeh, QueryStory CEO
Key facts
- 01QueryStory emerged from stealth on August 26, 2026 with a $6M seed raised in late 2025 at a $60M valuation.
- 02Brightmind Ventures and New York Life Ventures led the round; Tim Del Bello of New York Life Ventures is also a customer.
- 03CEO Shapor Naghibzadeh previously co-founded Chronicle inside Google's X Labs in 2016 after working on Operation Aurora response in 2009.
- 04The platform compressed a space-activity visualization project from several weeks with a developer to a few hours.
- 05Co-founders include CTO Stanley Yang, ex-EvolutionIQ lead engineer, and CPO David Glusic, an Accenture veteran.
The QueryStory pitch is that this investigative pattern — ask, verify, assemble, narrate — maps directly onto how analysts already use LLMs on business data, only without the guardrails. TechCrunch tested the product on a database of orbital space activity useful for tracking companies like SpaceX and reported that QueryStory produced dashboards and a narrative in a few hours; the same project had previously taken a developer several weeks. Every output carried a confidence indicator explaining why the AI agents believed the analysis held up.
Tim Del Bello, a partner at New York Life Ventures, invested and is also a user. He is using QueryStory to replace work previously done by several people to produce a quarterly business review, and he wants to turn that review into a real-time dashboard.
The competitive frame is the frontier labs themselves. Tools like Claude Cowork let users query a database and even ask the model to show the SQL it wrote, but the review step is manual and the transparency is opt-in. An executive at a tech company recently described to TechCrunch the routine of asking Claude Cowork to surface its SQL before sending queries to a human analyst. QueryStory's argument is that surfacing SQL, confidence, and review workflow by default is the actual product.
Brightmind Partners' Tayler Sipperly framed the investment thesis around durability. Enterprise systems have to work the same way tomorrow as they did today, and generic agents optimized for open-ended conversation do not clear that bar reliably.
“AI is more brittle than people realize when it comes to like building things that have to be durable and have large scale businesses relying upon them.”— Tayler Sipperly, Partner at Brightmind Partners
The economics matter as much as the reliability pitch. QueryStory is model-agnostic and currently routes to frontier lab models, but Naghibzadeh argues that the incentives of a token-priced provider — sell as much intelligence as possible — are misaligned with what a CFO signing the invoice wants. He describes QueryStory's product as trust in the answers rather than volume of compute, and says a purpose-built tool can preserve context more efficiently than a general-purpose agent burning tokens on every session.
The skeptical case is straightforward. Confidence scores on LLM output are notoriously hard to calibrate, and every enterprise AI startup of the past two years has claimed some version of grounded, trustworthy answers. QueryStory's differentiator lives or dies on whether the confidence indicator actually correlates with correctness on messy proprietary schemas, and on whether the human-review loop gets used or ignored. The company has not disclosed pilot customer names beyond Del Bello's own use, and the $60 million valuation leaves limited room for a long product-market-fit search.
The interesting bet inside QueryStory is structural, not technical. Frontier labs are pushing their consumer-style chat surfaces up into the enterprise; QueryStory is pushing the enterprise's audit, review, and provenance requirements down onto the models. If large regulated buyers keep choosing tools that make AI output defensible over tools that make it fast, the wedge between an agent that shows its work and an agent that just answers will define the enterprise AI purchasing pattern for the next two years — and predictable pricing against a token meter will look more attractive every quarter.
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