Enterprise AI has an answer problem: getting a response is easy, but proving that the response deserves trust is much harder. QueryStory is stepping into that gap with a platform designed to connect data analysis, review, and narrative-building for companies managing large proprietary databases.
The startup emerged from stealth on August 26, 2026, backed by a $6 million seed round raised in late 2025 from Brightmind Ventures and New York Life Ventures. That round valued QueryStory at $60 million, giving the company room to push its vision into large enterprises and highly regulated industries.
Turning Data Questions Into Auditable Stories
QueryStory was co-founded by Shapor Naghibzadeh, Stanley Yang, and David Glusic. Naghibzadeh serves as CEO, Yang is CTO, and Glusic is CPO. Their platform is built for decision-makers who need to ask questions across complex, separate data sources without relying on a data science or business intelligence team.
The product follows an investigation from question to conclusion. Users ask questions of their data, the platform generates SQL queries for analysis, and those queries appear for review before they are sent to data analysts. The system can then produce a narrative grounded in the results of those data queries.
“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 said.
That approach targets a problem inside large organizations: different teams can ask similar questions, receive different answers, and place those answers into slide decks for other people to share. QueryStory is developing a way to unite the analysis and review process instead of leaving each answer isolated from the next.
The platform is designed to let users flag analyses for human review and record those reviews. That creates a trail around the answer, showing how people assessed the analysis rather than treating an AI result as a final, unquestionable response.
Confidence Indicators Put the Reasoning on Display
QueryStory’s platform includes a confidence indicator that shows why AI agents believed an analysis was accurate. The company is also building its product around transparency, reliability, and control in AI workflows, with the goal of bridging trust gaps between enterprise users and AI-generated answers.
For a company operating with regulated data, that visibility can matter as much as the answer itself. Tim Del Bello, a partner at New York Life Ventures, described the product as one built for decision-makers who need ground truth from complex, separate data sources but lack a data science or BI team, especially in highly regulated industries.
“The product was built for people like me: decision-makers seeking the ground truth who need to work with complex, disparate data sources but don’t have a data science or BI team at their disposal, especially when operating in a highly regulated industry,” Del Bello said.
QueryStory has spent time developing, testing, and piloting its product with customers. One demonstration produced a visualization of satellites in orbit around the Earth in a few hours, a task that previously took several weeks. The example shows the platform working with space activity data while also pointing to the larger promise: turning difficult analysis into something decision-makers can inspect and use.
The company positions its platform as more transparent and reliable than frontier labs’ co-working tools. Its focus is not only on generating an answer, but also on surfacing the SQL behind the analysis, showing the confidence behind the result, and preserving human review.
Why Enterprise AI Needs More Than Bigger Models
The scale of modern AI creates a striking challenge for any system that handles business data. One set of figures describes 5 trillion pieces of data in a single prompt, equal to 5 million times the size of what Anthropic and Google’s flagship models can typically consume.
That scale makes trust a practical issue, not an abstract one. Tayler Sipperly put the risk plainly: “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.”
Naghibzadeh’s interest in verified knowledge reaches back to 2009, when he learned its value at Google. In 2016, he co-founded Chronicle in Google’s X Labs. Now, QueryStory is applying that experience to enterprise data analysis, where a confident answer must also show how it was produced.
QueryStory is entering a field that includes other efforts aimed at complex information systems. Accelerated Understanding, co-founded by Anima Anandkumar and Benedikt Jenik, is developing a physics-focused AI model. Keenable, co-founded by Andrey Styskin and Matthias Petri, is indexing the web for AI agents. Alturra AI announced its intelligence operating system on August 21, 2026.
QueryStory’s bet is distinct: enterprise AI earns trust when users can question the result, inspect the query, understand the confidence indicator, and send the analysis to human review. As the platform moves beyond stealth, those controls will shape whether its answers become material for decisions—or remain another set of AI-generated claims waiting for verification.
Based on
- QueryStory wants you to believe what AI is telling you — techcrunch.com
- Founders once linked to Bezos project pitch AI that models physics, not language – The Japan Times — japantimes.co.jp
- Accel-backed Keenable is indexing the web for AI agents | TechCrunch — techcrunch.com
- Alturra AI Announces Intelligence Operating System Designed to Help Organizations Understand Complex Systems | AP News — apnews.com
