Query Story wants you to believe what AI is telling you | Tech Crunch

Overview

Shapor Naghibzadeh learned the value of a good story in 2009 as a Google sysops engineer. When hackers backed by China set their sights on the search giant as part of an effort dubbed Operation Aurora, he was called into a hastily assembled war room to explain what exactly was going on in the company’s servers.

Tracing cyberattacks through disparate networks taught Naghibzadeh the value of verified knowledge. But it was a costly and time-consuming task. He thinks that LLMs can bring that same functionality to databases of all kinds in a fraction of the time.

Details

Naghibzadeh would spend the next six years focused on the nexus of data and cybersecurity, using Google’s resources to build tools that allowed security analysts to query complex data. In 2016, he co-founded a startup in Google’s X Labs called Chronicle that would bring that same functionality to other companies.

Last year, as large language models took a larger role in data analysis, Naghibzadeh saw a new opportunity to take the techniques he developed for cybersecurity and apply them to a variety of analytics. He co-founded Query Story, where he is CEO, alongside CTO Stanley Yang, a former Google colleague and lead engineer at Evolution IQ, and CPO David Glusic, an Accenture veteran. The startup emerged from stealth today.

“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 Query Story. It’s about telling stories with data, right? Putting a narrative together that’s grounded in truth.”

Query Story raised a

“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. He is also using the platform to replace the work of several people and produce a quarterly business review, which he now hopes will become 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 Tech Crunch.

I shared a database of space activity that’s useful for understanding what companies like Space X are doing on orbit. Query Story produced a visualization of that data in a few hours, a project I once did with a developer that took several weeks. It produced sophisticated dashboards and analysis, and perhaps most notably, broke out a confidence indicator that showed why the AI agents believed the analyses were accurate.

A visualization of satellites in orbit around the Earth. Image Credits: Query Story / Query Story

A set of queries derived from the space data. Image Credits: Query Story / Query Story

Query Story’s confidence indicator. Image Credits: Query Story / Query Story

This kind of work can be done with co-working tools built by the frontier labs, but those tools are intentionally limited in their user experiences. Part of the bet that Query Story is making is that users, especially at large companies, want more transparency, reliability and control as they integrate AI into their workflows.

As an example, an executive at a tech company recently told Tech Crunch about querying a company database using Claude Cowork, then asking the model to show him the SQL queries it wrote to make sure they made sense before he sent them off to a data analyst for human review. In Query Story, those SQL queries surface automatically, and users can flag analyses for human coworkers to review, with those reviews then 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 Tech Crunch.

Naghibzadeh points out that when companies connect their data to an LLM’s chat UI, “you get hundreds or thousands of people within an organization all asking their questions and getting their version of the truth and putting that in a slide deck and sharing it—you just end up with this huge sprawl of content, and there’s no real place to hang that content that ties back to the data.”

There are also economics to contend with. Query Story is built to be model-agnostic, although for now it mainly uses the latest models provided by frontier labs. While his company competes with frontier labs on a product basis, Naghibzadeh believes that customers will prefer working with a service provider that isn’t incentivized to sell as much intelligence as possible.

“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. He argues that a purpose-built tool like Query Story can be more efficient and accurate than a general-purpose agent by understanding and preserving context.

“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?’”

Key Takeaways

• Shapor Naghibzadeh learned the value of a good story in 2009 as a Google sysops engineer
• Tracing cyberattacks through disparate networks taught Naghibzadeh the value of verified knowledge
• Naghibzadeh would spend the next six years focused on the nexus of data and cybersecurity, using Google’s resources to build tools that allowed security analysts to query complex data
• Last year, as large language models took a larger role in data analysis, Naghibzadeh saw a new opportunity to take the techniques he developed for cybersecurity and apply them to a variety of analytics
• “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