QueryStory Bets Enterprises Will Trust AI Only When It Shows Its Work

QueryStory has emerged from stealth with a simple but ambitious pitch: enterprises should not just get answers from AI, they should be able to verify them. The startup, founded by former Google engineer Shapor Naghibzadeh, launched publicly on August 26 after raising a $6 million seed round and building a product designed to make AI-generated analysis more transparent, auditable, and useful for large businesses.

The company is positioning itself as a data analysis platform for organizations that rely on proprietary databases and need AI outputs that can stand up to human scrutiny. Its core argument is that enterprise users will adopt AI faster if systems can show the underlying SQL, keep a record of reviews, and preserve the chain of evidence behind an answer.

That idea reflects Naghibzadeh’s experience during one of Google’s most consequential security crises. In 2009, as hackers linked to China targeted the company in the Operation Aurora campaign, he was pulled into a response room to help explain what was happening across Google’s systems. That early exposure to cyber investigations shaped his belief that trustworthy answers depend on verified data, not just fast data.

QueryStory now aims to take those lessons from cybersecurity and apply them to enterprise analytics. The startup is betting that businesses want not only speed from generative AI, but also context, traceability, and the ability to hand off work between AI and humans without losing confidence in the result.

What QueryStory is trying to change about enterprise AI

QueryStory is built around a problem many companies are already confronting: AI can summarize and search data quickly, but it can also be opaque, inconsistent, and difficult to validate at scale. The startup’s software is meant to bridge that gap by turning AI-assisted analysis into something closer to a documented workflow.

Rather than treating AI chat as the end product, QueryStory acts more like a layer on top of enterprise data systems. It is intended for sales, operations, finance, and other business teams that need answers from large databases but do not necessarily have a data science group or business intelligence team on standby.

According to the company, the product helps users ask questions of their data, review the logic behind the results, and keep the final analysis connected to the original sources. The goal is not just to deliver an answer, but to create a record that shows how that answer was reached and whether a person reviewed it.

Why the company believes trust is the real product

QueryStory’s leadership argues that confidence in AI output has become a bigger barrier than raw capability. In many enterprise environments, a model’s answer is only useful if managers can explain where it came from, how it was built, and whether anyone checked it before it was shared.

That concern is especially acute in highly regulated industries, where a mistaken data point can affect reporting, planning, or compliance decisions. QueryStory says its software is designed for the people making those decisions, not just the engineers building the tools behind them.

Shapor Naghibzadeh said the company was built around the idea of “telling stories with data” while keeping those stories grounded in what the data actually supports.

The startup’s backers see that same issue as a practical business problem. Tayler Sipperly, a partner at Brightmind Ventures, said enterprise AI remains more fragile than many companies appreciate, particularly when organizations depend on durable systems rather than experimental demos.

How QueryStory grew out of cybersecurity and Google’s data culture

QueryStory did not begin as a general AI startup looking for a market. It grew out of a long-standing effort to make complex investigations easier for analysts and decision-makers.

Naghibzadeh spent much of his career working at the intersection of data and security at Google. After the 2009 Operation Aurora incident, he focused on building tools that helped security teams interrogate complicated datasets more efficiently. In 2016, he co-founded Chronicle inside Google’s X Labs, a cybersecurity company intended to bring those capabilities to other organizations.

The new startup extends that logic into a broader analytics context. Instead of limiting the workflow to security investigations, QueryStory is aimed at business intelligence, operational analysis, and executive reporting. The central premise is the same: when the stakes are high, users need to know not only what the system says, but how it got there.

Naghibzadeh is joined by two other executives with enterprise software experience. Stanley Yang, a former Google colleague who also served as lead engineer at EvolutionIQ, is the company’s CTO. David Glusic, previously with Accenture, is CPO. Together, they are trying to package a familiar enterprise expectation — trustworthy reporting — in a form that works with modern large language models.

What does QueryStory actually do?

At a high level, QueryStory allows enterprise users to ask questions of complex data and then inspect the logic behind the answers. The platform is designed to surface SQL queries automatically, show the reasoning behind the output, and let users flag analyses for review by human colleagues.

That review process matters because many companies are already using frontier models to assist with reporting and analysis, but often through interfaces that hide the full chain of work. QueryStory’s approach is to make the workflow visible from start to finish, so the result can be audited instead of merely believed.

In practice, that means a user can query a database, receive an analysis, and then see the queries and intermediate steps that led to the conclusion. If the result needs validation, the platform can record a human review and preserve that checkpoint for later use.

One of the standout features is a confidence indicator attached to the analysis. That gives users a signal about why the system believes its answer is reliable, rather than forcing them to accept a model-generated conclusion on trust alone.

Why the product is different from a standard chatbot

QueryStory is not simply another conversational interface on top of enterprise data. The company’s case is that ordinary chat-based tools are good for exploration but weak for institutional memory and repeatable decision-making.

When employees use generic AI chat tools to interrogate data, the result often lives inside a conversation thread that is hard to reuse, verify, or share in a structured way. QueryStory says it addresses that problem by giving companies a place to store the analysis itself, together with the rationale behind it.

That distinction is important for organizations where dozens or hundreds of employees may ask similar questions and then circulate different versions of the truth in slides, emails, or dashboards. QueryStory wants to reduce that sprawl and keep one authoritative trail tied to the underlying data.

In short, the company is not selling a better prompt. It is selling a system for accountable analysis.

Why enterprises care about the trust gap in AI

Enterprises are increasingly adopting AI tools for reporting, forecasting, and internal analysis, but many remain cautious about giving models too much authority. The concern is not theoretical. A flawed answer can spread quickly through a large organization, where it may be repeated by executives, embedded into presentations, or used to shape business decisions.

That risk becomes even greater when AI tools are connected directly to internal databases. At that point, the system may appear to be providing authoritative answers while still making assumptions or generating queries that need close inspection.

QueryStory’s founders believe this trust gap is one of the major reasons AI adoption inside large companies still lags behind the hype. If an enterprise cannot verify the output, it may use AI only for preliminary work rather than operational decision-making.

The company’s pitch is that the next wave of enterprise AI will be won by products that help users understand the model’s outputs instead of simply producing them faster.

How human review fits into the workflow

QueryStory is built to make human oversight part of the system rather than an afterthought. When users want an analysis checked, the platform lets them route it to a colleague and then capture the review inside the product.

That structure matters because many companies already use a patchwork of tools to manage data analysis, internal review, and executive reporting. QueryStory is trying to compress those steps into one workflow without eliminating accountability.

The company says this is especially useful for decision-makers who need ground truth but lack the internal resources to build a full analytics stack. For them, the platform can serve as a lightweight layer that organizes information, preserves evidence, and makes collaboration more consistent.

New York Life Ventures partner Tim Del Bello said the platform was designed for decision-makers who need reliable answers from complex data sources without having a dedicated data science or BI team, especially in regulated industries.

Del Bello also said he is using the software to replace work that previously required several people and to create a quarterly business review he now hopes can become a live dashboard.

How much funding did QueryStory raise?

QueryStory raised $6 million in seed funding in late 2025 at a valuation of $60 million. Brightmind Ventures and New York Life Ventures led the round, and the company has spent the months since then refining the product and testing it with customers before stepping out of stealth.

For a startup in enterprise AI, the funding level is enough to support early product development and customer pilots, but not so large that it suggests the company is already scaling broadly. That makes the launch notable because it comes with a relatively focused thesis rather than a bloated product roadmap.

The presence of a strategic investor such as New York Life Ventures also indicates that QueryStory’s target market is not consumer-facing AI but regulated, data-heavy businesses where reliability is a selling point.

What the company’s demonstration suggests about the market

A useful way to understand QueryStory is to look at the kind of work it says it can do in a few hours. The company demonstrated the product using a database about space activity, producing visualizations and analysis that might otherwise have taken a developer weeks to assemble.

That kind of turnaround is a strong sales argument in itself. Many enterprises want to speed up recurring analysis without giving up the documentation and quality control that make the work usable in a professional setting.

The demo also highlights a broader shift in AI infrastructure: the competitive edge is moving from raw model power to workflow design. In other words, companies may increasingly pay for systems that make AI usable inside a business, not just impressive in a demo environment.

QueryStory’s approach is especially relevant at a time when many organizations are trying to determine where AI should sit in their internal stack. Should it live in a chat box? In a data warehouse? In a BI dashboard? QueryStory says the answer is a product that sits above the data and organizes the process of interpretation.

How does QueryStory compare with frontier-lab tools?

QueryStory competes indirectly with the enterprise tools offered by frontier AI labs, but it argues that purpose-built software can serve businesses better than general-purpose assistants. The difference is not just cosmetic; it comes down to control, memory, and incentives.

Frontier-lab interfaces may be powerful, but they are often designed to showcase model capabilities rather than to maintain the kind of durable audit trail enterprises want. QueryStory believes that businesses need a layer built specifically around trust, not just around convenience.

Naghibzadeh also says the startup has an advantage because it is not built on a business model centered on maximizing compute or token usage. In his view, that matters because a platform whose revenue depends on more model consumption may not always optimize for the customer’s efficiency.

The company says it is model-agnostic, although for now it relies mostly on leading models from the frontier labs. That gives QueryStory flexibility while allowing it to focus on product structure rather than model development.

Where the economics fit in

For enterprise customers, the economic argument is straightforward: if the platform can reduce manual analysis, shorten review cycles, and help managers produce decision-ready reports, it may save more than it costs.

For the startup, the challenge is proving that the trust layer is valuable enough to justify another software budget item. That will depend on whether customers treat the product as a convenience or as infrastructure.

QueryStory is clearly trying to become the latter. By presenting itself as a place where analysis can be checked, shared, and preserved, it is aiming for a recurring role in business operations rather than an occasional productivity boost.

Why this launch matters for enterprise AI

QueryStory’s debut matters because it captures where enterprise AI is likely headed next. The market has moved beyond asking whether language models can write, summarize, or analyze. The real question is whether they can do those things in a way that businesses trust enough to rely on every day.

That shift favors products that make model behavior legible to users. In regulated sectors and large organizations, transparency is not a nice-to-have feature. It is a prerequisite for adoption.

The startup’s launch also reflects the growing divide between consumer-oriented AI and enterprise-ready AI. Consumer tools can emphasize speed and simplicity, but enterprise tools must answer a harder question: can the output be defended when it affects revenue, compliance, or strategy?

QueryStory is betting that the answer increasingly depends on evidence, not eloquence. If it works, the platform could help define a category of AI software built around accountability rather than novelty.

What happens next for QueryStory?

The next test for QueryStory is whether early pilots translate into repeatable enterprise sales. The company has already worked with customers during its stealth phase, but it now needs to prove that the product can fit into real workflows at scale.

That will likely mean showing that the platform can handle messy, high-volume internal data, support multiple teams, and keep its review and audit features useful over time. It will also need to prove that its confidence signals and query transparency actually improve decision-making rather than simply adding another layer of complexity.

If the startup succeeds, it could become part of a broader movement toward explainable enterprise AI — software that does not just answer questions, but documents how those answers came to be. For companies that have been waiting to move beyond AI experimentation, that may be exactly the missing piece.

For now, QueryStory is making a clear wager: when it comes to enterprise AI, the winning product will be the one that earns belief by showing its work.

Key facts at a glance

• QueryStory launched publicly on August 26, 2026 after operating in stealth.
• The company raised $6 million in seed funding at a $60 million valuation.
• Founder Shapor Naghibzadeh previously worked at Google and co-founded Chronicle.
• The platform is designed for large enterprises using proprietary databases.
• Its focus is traceable AI analysis, human review, and confidence indicators.

Background: why Naghibzadeh’s history matters

Naghibzadeh’s background helps explain why QueryStory’s product philosophy is so strongly oriented toward verification. His work on cybersecurity problems taught him that information is only useful when it can be tested against reality, and his Google experience gave him firsthand exposure to large-scale, high-stakes data environments.

That perspective is unusually relevant in a moment when many startups are rushing to attach AI to databases. The difference between a flashy interface and an enterprise product often comes down to process: who can review the result, where the evidence lives, and whether the system can be trusted again next quarter.

QueryStory’s founders appear to believe those questions will shape the next stage of AI adoption more than model benchmarks will. If they are right, the startup may have arrived at the right time with the right message.