QueryStory bets on traceable AI and raises $6M at a $60M valuation

New to the market, QueryStory combines language models and data governance to make every analysis verifiable by humans. The startup, founded by former Google employees, claims to operate agnostically to models and features integrated audit mechanisms. It has raised $6 million from Brightmind Ventures and New York Life Ventures, achieving a valuation of $60 million.

Displayed SQL, Timestamped Reviews, and Trust Index

In QueryStory, SQL queries generated by AI are visible by default, and users can flag analyses for verification by colleagues, with these reviews then stored on the platform. The solution also highlights a trust indicator that explains why agents consider results to be accurate. For comparison, an executive from a tech company who used Claude Cowork asked the model to expose the SQL queries for review by an analyst, a step that QueryStory automates. While co-working tools from leading labs can perform similar tasks, they are described as intentionally limited in user experience, whereas QueryStory bets on greater transparency, reliability, and control. For Tayler Sipperly, a partner at Brightmind Partners, AI remains more fragile than one might think when it comes to building sustainable systems for large enterprises.

Limiting "Versions of the Truth" in Business

Shapor Naghibzadeh observes that a chat interface connected to internal data can lead hundreds, if not thousands, of employees to produce their own versions of the same answer and disseminate them, causing a proliferation of content without a single anchor in the data. He presents QueryStory as a means to bridge this trust gap by delivering actionable and auditable answers, as a substitute for the massive reliance on human judgment and teams of engineers. The CEO summarizes the offering by what the company sells: trust in the answers and added value for the business.

Model-Agnostic, Yet Backed by the Best of the Moment

QueryStory is designed to be model-agnostic while currently relying primarily on the latest models provided by leading labs. Shapor Naghibzadeh indicates that he is competing with these players on a product basis and believes that clients will prefer a provider that is not incentivized to sell a maximum volume of "intelligence." He emphasizes that he has not built the company on a model of consumption of computing, storage, or tokens, and argues that a dedicated tool like QueryStory can be more efficient and accurate than a generalist agent while preserving context. The stated goal includes providing the CFO with a clear reading of the costs associated with analyses.

Concrete Use Cases: From Orbit to Executive Committees

On a dataset dedicated to space activity, the platform produced visualizations and dashboards in a few hours, whereas comparable work done with a developer had taken several weeks. The example illustrated analyses and visualizations enriched by the trust indicator. On the client side, Tim Del Bello, a partner at New York Life Ventures and investor, relies on QueryStory to replace the work of several people and develop a quarterly business review, with the ambition of transforming it into a real-time dashboard. He describes a product designed for decision-makers seeking a reference truth from complex sources, even without a data or BI team, particularly in heavily regulated sectors.

Ex-Google at the Helm and $6 Million Raised at a $60 Million Valuation

Shapor Naghibzadeh's journey dates back to 2009 at Google, at the heart of the response to Operation Aurora, where tracing attacks across disparate networks proved costly and slow. He then spent six years creating, with Google's resources, querying tools for security analysts before co-founding Chronicle in 2016 to bring these capabilities to other companies. Noticing last year the role of language models in analysis, he co-founded QueryStory and took the helm, alongside Stanley Yang (CTO, ex-Google and EvolutionIQ) and David Glusic (CPO, ex-Accenture). The company is now stepping into the spotlight after raising $6 million from Brightmind Ventures and New York Life Ventures, achieving a valuation of $60 million, and claims to have refined its product with clients. It targets large enterprises rich in proprietary data and serves as a unifying platform between analysis and review. Its name, Naghibzadeh assures, embodies a promise: to tell stories with data, keeping the narrative anchored in truth.