The archive · Developer & Business Tools · Product decision · 2025–2026
QueryStory bets enterprises want auditable AI answers: $6M seed, ex-Google security lead
Ex-Google security engineer Shapor Naghibzadeh's QueryStory shows SQL, sources and confidence behind every answer, so enterprises can verify AI.
QueryStory
What the business is
QueryStory is an agentic data platform for large enterprises: teams ask plain-language questions over proprietary databases and get decision-ready narratives, with the underlying SQL, sources, confidence indicators and human review records surfaced automatically.
Starting capital：$6 million seed (late 2025) from Brightmind Partners and New York Life Ventures at a $60 million valuation
How it started
In 2009 Shapor Naghibzadeh was a Google SysOps engineer pulled into a war room when hackers targeted Google in the Operation Aurora attacks; tracing the intrusion across networks taught him how costly verified knowledge is to produce. He spent six years building data tools for security analysts, co-created Google's Threat Analysis Group and co-founded Chronicle in Google X in 2016. In 2025, as LLMs moved into data analysis, he co-founded QueryStory as CEO with CTO Stanley Yang (a former Google colleague and lead engineer at EvolutionIQ) and CPO David Glusic (an Accenture veteran).
What happened
QueryStory raised a $6M seed in late 2025 from Brightmind Partners and New York Life Ventures at a $60M valuation, then spent months building and piloting the platform with customers. It emerged from stealth on 2026-08-26. New York Life Ventures' managing director Tim Del Bello says the fund uses QueryStory to replace the work of several people in producing a quarterly business review, with a real-time dashboard planned.
No ending yet — it is still running.
Background
QueryStory is an agentic data platform for large enterprises that manage big, proprietary databases. Users ask plain-language questions and receive analysis assembled into a business narrative, while the platform automatically surfaces the SQL queries behind each answer, confidence indicators, and a record of human review — so conclusions stay traceable to source data instead of becoming unverifiable chat outputs.
The idea grew out of Shapor Naghibzadeh's years at Google: after tracing the 2009 Operation Aurora attack as a SysOps engineer, he spent six years building tools that let security analysts query complex data, co-created Google's Threat Analysis Group, and co-founded Chronicle in Google X in 2016. In 2025 he co-founded QueryStory with CTO Stanley Yang and CPO David Glusic to apply that verified-knowledge discipline to enterprise analytics. The company raised a $6M seed in late 2025 from Brightmind Partners and New York Life Ventures at a $60M valuation and emerged from stealth on 2026-08-26.
The pitch is a bet on enterprise trust: general-purpose AI chats give each employee their own version of the truth with no durable link to the data, while QueryStory is model-agnostic and organized around shared definitions, lineage and governance. TechCrunch's reporter got dashboards from a space-activity database in hours — a task that once took weeks with a developer. As of early September 2026 the company is piloting with customers and positioning against frontier-lab tools, arguing its incentives differ because it does not sell compute or tokens.
What has to be true
- Naghibzadeh's war-room experience made verified knowledge the core problem: an answer nobody can trace to its source is not usable, and LLMs made that problem visible at enterprise scale.
- Visible SQL, confidence indicators and recorded review turn an AI black box into an audit trail, which is exactly what large, regulated enterprises need before they let AI drive decisions.
- The general-purpose chat pattern — hundreds of employees each getting their own version of the truth, unlinked to data — is a real enterprise failure mode a shared, governed platform attacks directly.
- Being model-agnostic and not paid per token aligns incentives with customers who worry about compute-driven vendors, making the trust story credible rather than marketing.
What can be applied
A security mindset is a product wedge: regulated enterprises will pay for the audit trail general AI chats omit — visible SQL, sources and review — because they need answers they can defend.
Aftermath
As of September 2, 2026, QueryStory is out of stealth and live for sales, operations and finance users in large enterprises, with customers piloting it during its stealth year. Its backers use the product internally — New York Life Ventures produces quarterly business reviews on it — and TechCrunch's hands-on test found SQL surfacing automatically with confidence indicators and governance controls. No revenue or customer names are disclosed; the open question is whether transparency plus a governed workflow can win against the frontier labs' increasingly capable chat tools.
Sources
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