QueryStory launches AI platform to verify enterprise data analysis
QueryStory, a startup founded by former Google security engineer Shapor Naghibzadeh, has emerged from stealth with an AI platform designed to help large enterprises trust the answers generated by their data analysis systems. The company applies techniques Naghibzadeh developed while investigating cyberattacks, including the 2009 China-linked "Operation Aurora" hack of Google, to build AI tools that verify and explain their findings rather than simply presenting them, aiming to close a "trust gap" that has held back enterprise adoption of AI-driven analytics.
Naghibzadeh co-founded QueryStory with CTO Stanley Yang and CPO David Glusic, and the firm raised a $6 million seed round in late 2025 from Brightmind Ventures and New York Life Ventures at a $60 million valuation. The platform is pitched at large organisations with complex proprietary databases, such as sales and operations teams, who lack dedicated data science support, and includes confidence indicators showing why its AI agents believe their analyses are accurate. An investor, Tim Del Bello of New York Life Ventures, said he already uses the tool to replace manual work in producing quarterly business reviews.
- QueryStory launches AI platform to make enterprise data analysis trustworthy
- Founded by ex-Google security engineer Shapor Naghibzadeh, raised $6M seed
- Tool provides confidence indicators explaining AI-generated analysis
New here? Start with this
Large organisations collect information across many databases, and staff often need to analyse it to make business decisions. AI tools can answer questions about that information, but it can be hard for users to tell whether an answer is reliable or how it was reached.
QueryStory was founded by Shapor Naghibzadeh, a former Google security engineer, with technology leaders Stanley Yang and David Glusic. The company draws on methods for checking evidence that Naghibzadeh used when investigating cyberattacks, and its platform is aimed at organisations whose employees need data insights but may not have specialist analysts to check them.
The issue matters because businesses may be reluctant to rely on AI-generated analysis if they cannot assess its accuracy. QueryStory’s approach is to show confidence indicators and explain the basis for its findings, so users can judge the answers they receive.
Both sides, in good faith
The strongest fair case each way — we don't pick a winner.
The case for
These AI verification tools address genuine constraints that prevent enterprises from adopting beneficial analytics. By providing confidence metrics, transparent reasoning, and validation of findings, they democratise data-driven decision-making for organisations without specialist expertise. The founder's established security background and demonstrated customer adoption suggest the technology delivers practical value and rigorous design.
The case against
Relying on AI systems to verify analytical outputs risks creating misplaced confidence where human judgment remains essential. High-stakes enterprise decisions—particularly those affecting operations, strategy, and resource allocation—demand accountability and contextual reasoning that automated verification cannot replicate. Confidence metrics from AI models may appear precise but can be misleading, and automation of analytical validation might erode the institutional knowledge and expertise organisations need to govern their own data.
AI Art Business Celebrity Companies Culture Cybersecurity Entertainment Technology
Read the full article at the source →
Originally published by TechCrunch as “QueryStory wants you to believe what AI is telling you”.