Key facts
- QueryStory has launched an AI-powered platform for enterprise data analysis.
- The platform aims to provide trustworthy insights from large, proprietary databases.
- It includes a confidence indicator to show why AI agents believe analyses are accurate.
- Users can flag AI-generated analyses for human review.
- The company raised $6 million in seed funding from Brightmind Ventures and New York Life Ventures.
QueryStory, a new startup co-founded by former Google engineer Shapor Naghibzadeh, has emerged from stealth with a platform designed to enhance trust in AI-driven data analysis for large enterprises. Naghibzadeh, who previously worked on cybersecurity tools at Google and co-founded Chronicle, aims to apply his expertise in querying complex data to a broader range of analytical tasks.
The company announced it raised a $6 million seed round in late 2025 from investors including Brightmind Ventures and New York Life Ventures, at a $60 million valuation. QueryStory has spent the intervening time developing and piloting its product, which is targeted at large organizations that manage extensive proprietary databases.
Naghibzadeh explained that the platform's name, QueryStory, reflects its goal of enabling users to build narratives grounded in data. The platform seeks to bridge the 'trust gap' for AI, providing decision-makers with reliable answers without requiring extensive data science teams. It offers features like a confidence indicator to show the AI's certainty in its findings and allows users to flag analyses for human review.
In a demonstration, QueryStory processed a database of space activity, generating visualizations and analysis in hours that would typically take weeks with traditional methods. The platform differentiates itself from general-purpose AI tools by focusing on transparency, reliability, and control, allowing users to see the underlying queries and integrate AI into workflows more effectively.
QueryStory is designed to be model-agnostic, though it currently utilizes leading large language models. The company believes its approach, which is not tied to a consumption-based model of compute or tokens, offers a more efficient and accurate solution for enterprises seeking to understand the cost and value of AI-driven insights.