|
QueryStory emerged from stealth Wednesday with $6 million in seed funding and a product aimed squarely at the biggest obstacle in enterprise AI adoption: trusting the answers. CEO Shapor Naghibzadeh learned verified knowledge the hard way — as a Google SysOps engineer in 2009 he was pulled into the war room tracing the Chinese-backed Operation Aurora intrusion through disparate networks, spent six years building tools that let security analysts query complex data, and in 2016 co-founded Chronicle, the security-analytics company spun out of Google’s X Labs. Now, with CTO Stanley Yang, a former Google colleague and lead engineer at EvolutionIQ, and CPO David Glusic, an Accenture veteran, he is applying that investigative discipline to business analytics. The pattern: ask a series of questions of the data, then assemble the answers into a narrative grounded in truth — hence the name. The trust machinery is the product: every analysis carries a confidence indicator showing why the agents believe the result is accurate; the SQL the system writes surfaces automatically for inspection; and any analysis can be flagged for a human coworker to review, with reviews recorded in the platform so an organization accumulates a corpus of verified answers instead of a sprawl of contradictory ones. That sprawl is the failure mode Naghibzadeh targets — connect a chat UI to company data and thousands of people each get their own version of the truth, put it in a slide deck, and share it, with nothing tying any of it back to the data. The platform is model-agnostic, aimed at large enterprises with big proprietary databases, and priced on value rather than token consumption. TechCrunch’s own test: a space-activity database visualization that once took a developer several weeks was produced in hours. Brightmind Partners’ Tayler Sipperly: “AI is more brittle than people realize.”
Why it matters for PMs: Every PM who has put AI in front of product data has hit the same wall: a stakeholder who doesn’t believe the number. QueryStory’s answer is worth copying even if you build your own version — show the work (surface the queries), show the confidence (explain why the system believes itself), and make human review a recorded artifact rather than a hallway check. Three more implications. First, “version of the truth” sprawl is a real failure mode in product orgs: two teams prompt the same data differently and bring conflicting numbers to the same roadmap review, and a shared, verified analysis layer is the fix; if your analytics stack doesn’t offer one, expect this category to keep growing. Second, the positioning lesson: against the frontier labs’ general-purpose cowork tools, QueryStory sells trust rather than intelligence and prices on outcomes rather than consumption — when models commoditize, verifiability is the defensible wedge. Third, note the pedigree: security-analytics founders treating enterprise data like an investigation scene, chain of custody included. That is where analytics UX is heading.
|