QueryStory Debuts With $6M Seed To Bridge AI Data Trust Gap

AI-driven data analysis startup QueryStory has emerged from stealth after securing $6 million in seed funding to help large enterprises trust AI-generated insights. The company aims to eliminate the "trust gap" that prevents regulated industries from relying on large language models (LLMs) for high-stakes decision-making.

The funding round, closed in late 2025, was led by Brightmind Ventures and New York Life Ventures, valuing the company at $60 million. QueryStory provides a model-agnostic platform that allows enterprises to query proprietary databases using LLMs. To ensure accuracy, the system includes confidence indicators and automatically surfaces the generated SQL queries, allowing human analysts to verify the logic behind every answer.

The Engineering Pedigree

The company was founded by a team of industry veterans. CEO Shapor Naghibzadeh previously served as a Google sysops engineer during Operation Aurora and co-founded Chronicle within Google's X Labs. Naghibzadeh's background in cybersecurity and the tracing of cyberattacks across disparate networks served as the blueprint for QueryStory, applying similar verification and tracing techniques to enterprise data analytics. He is joined by CTO Stanley Yang, formerly of Google and EvolutionIQ, and CPO David Glusic, a veteran of Accenture.

Solving the Brittle AI Problem

For many large organizations, AI systems remain too "brittle" for production use, often producing hallucinations or inconsistent results when faced with complex database schemas. According to Tayler Sipperly, a partner at Brightmind Partners, AI is more fragile than many realize when scaled for durable business operations. By productizing a form of human-like judgment and providing a transparent audit trail, QueryStory attempts to move AI business intelligence from experimental to operational.

"What we’re doing is bridging that trust gap for AI to give enterprises answers that they can act on," Naghibzadeh said.

The Path to Enterprise Adoption

As enterprises move beyond simple chatbots toward autonomous data agents, the ability to audit AI logic becomes a requirement rather than a feature. QueryStory's focus on transparency—specifically the visibility of SQL queries—addresses the primary fear of data leaders: the "black box" effect where an AI provides a confident but incorrect number.

Moving forward, the company will focus on scaling its platform across regulated sectors where data integrity is non-negotiable. While the seed funding provides a strong runway, the ultimate test for QueryStory will be whether its verification tools can sufficiently reduce the manual oversight currently required to keep AI-generated data accurate.