QueryStory is entering the enterprise AI market with a simple premise: companies do not just need faster answers from large language models. They need answers that can be checked, reviewed and trusted before they shape business decisions.
The startup emerged from stealth today after raising a $6 million seed round in late 2025 from Brightmind Ventures and New York Life Ventures at a valuation of $60 million. Its target customers are large enterprises with big, proprietary databases and teams that need useful analysis without relying entirely on data science or business intelligence specialists.
From cybersecurity investigations to enterprise analytics
Shapor Naghibzadeh, QueryStory's CEO and co-founder, traces the idea back to his time as a Google sysops engineer in 2009. During Operation Aurora, when hackers backed by China targeted Google, he was brought into a quickly assembled war room to explain what was happening inside the company's servers.
That work involved following cyberattacks across different networks and turning scattered signals into verified knowledge. It also showed him how expensive and slow that kind of investigation could be.
Naghibzadeh then spent six years working at the intersection of data and cybersecurity. At Google, he used the company's resources to build tools that helped security analysts query complex data. In 2016, he co-founded Chronicle, a startup in Google’s X Labs designed to bring similar capabilities to other companies.
Last year, as large language models became more prominent in data analysis, Naghibzadeh saw a chance to apply the same investigation pattern beyond cybersecurity. He co-founded QueryStory with CTO Stanley Yang, a former Google colleague and lead engineer at EvolutionIQ, and CPO David Glusic, an Accenture veteran.
“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,” Naghibzadeh said. “That became the genesis for the name QueryStory. It’s about telling stories with data, right? Putting a narrative together that’s grounded in truth.”
The product pitch is trust, not just speed
QueryStory is built for enterprise users such as sales teams and operations managers who work with complex company data. The platform aims to bring data analysis and review into one place, so users can ask questions, see the work behind the answers and keep the resulting analysis connected to the underlying data.
Naghibzadeh describes the product as a way to close the trust gap around AI-generated analysis. Instead of depending on large teams of specialists or forward deployed engineers, QueryStory is trying to turn that process into software.
“What we’re doing is bridging that trust gap for AI to give enterprises answers that they can act on,” Naghibzadeh said. “Instead of, you know, like renting human judgment and armies of forward deployed engineers, we productized that.”
Tim Del Bello, a partner at New York Life Ventures, invested in the company and is also using the platform. He told TechCrunch that QueryStory is replacing work previously handled by several people and producing a quarterly business review, which he now wants to evolve into a real-time dashboard.
“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,” he told TechCrunch.
Why AI answers need a record
The issue QueryStory is trying to solve is not whether AI can generate analysis. It is whether that analysis can hold up inside a large organization where many people may be asking different questions of the same data.
In one TechCrunch test, QueryStory was given a database of space activity useful for understanding what companies like SpaceX are doing on orbit. The platform produced a visualization in a few hours, compared with a similar project that had previously taken several weeks with a developer. It also created dashboards and analysis, including a confidence indicator that explained why the AI agents considered the results accurate.
That focus on confidence and review is central to the product. Frontier lab co-working tools can perform related work, but QueryStory is betting that enterprise users want more transparency, reliability and control as AI becomes part of routine workflows.
One example in the source article involved an executive at a tech company using Claude Cowork to query a company database, then asking the model to show the SQL queries it created before sending them to a data analyst for review. In QueryStory, those SQL queries appear automatically. Users can also send analyses to human coworkers for review, and those reviews are recorded in the platform.
“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,” Tayler Sipperly, a partner at Brightmind Partners, told TechCrunch.
A model-agnostic bet on enterprise AI economics
QueryStory is model-agnostic, although it currently mainly uses the latest models provided by frontier labs. That creates an unusual position: the company competes with those labs at the product layer while also relying on their models.
Naghibzadeh argues that a purpose-built analytics product can be more efficient and accurate than a general-purpose agent because it can understand and preserve context. He also says enterprises may prefer working with a provider whose business is not built around selling more compute, storage or tokens.
“We have a lot of things going for us here in not being one of those companies that built their business around this consumption model of compute or storage or tokens,” Naghibzadeh said.
For large companies, the question is not only whether AI can produce an answer. It is whether the answer has a visible path back to the data, whether coworkers can review it and whether the organization can understand what the system will cost.
“The thing that we are selling is the trust in the answers, right?” he said. “The thing that we’re selling them is the value that we’re adding to the business, and our whole goal is giving the CFO the ability to understand ‘what is this thing going to cost?’”
That is the market QueryStory is trying to define: enterprise AI analytics where the output is not just a chart or a slide, but a documented business narrative that teams can inspect before they act.