QueryStory

QueryStory is an agentic data platform for enterprises that need to explain why a number moved, not just what it is. Where a copilot stops at an answer, QueryStory carries the work through the full decision lifecycle: it reasons across your data, assembles individual queries into a single business narrative, and publishes the result as a deck, doc, dashboard or chat message that keeps updating as the underlying data changes.

The design premise is auditability. Every output ships with its sources, assumptions, metric definitions and lineage attached, so an analyst or a CFO can interrogate the reasoning rather than take it on faith. Projects each carry their own context — you set the sources, permissions and business rules per project, pin shared metric definitions so numbers stay consistent between teams, and track answer quality over time against a set of golden queries.

It reads structured and unstructured context alike. Alongside warehouses and CRMs — Snowflake, Databricks, BigQuery — it ingests call recordings, slide decks and old spreadsheets, always under the permissions your organisation already enforces. QueryStory is explicitly additive: it connects to your existing stack rather than asking you to migrate off it.

Deployment keeps data in your own cloud and region, on infrastructure you already control, and the vendor states that nothing trains on customer data. The AI management system behind it has been independently audited against international responsible-AI standards.

The product is aimed at RevOps, finance, marketing, sales, operations and data teams inside large organisations — account reviews, variance analysis, campaign attribution, forecast prep, board-ready metrics with an audit trail. QueryStory emerged from stealth in San Francisco and launched the platform on 26 August 2026, backed by a $6M seed round from Brightmind Ventures and New York Life Ventures. There is no public pricing or self-serve tier: access is through a sales demo.