QueryStory is an agentic data platform that helps organizations turn data into trusted decisions and actions. Unlike traditional data platforms that mainly store, manage, and analyze data, an agentic data platform understands business context, reasons across information, generates recommendations that can be acted on directly, and helps teams execute and adjust as conditions change.
Purpose QueryStory is aimed at scenarios where organizations want everyone across the company to be able to act on data. Core purposes include: asking why a metric changed and getting a defensible answer within minutes; consolidating analysis into narratives leaders can use; connecting structured and unstructured information; preserving organizational knowledge; generating explainable, traceable recommendations; and coordinating follow-through execution. The platform's goal is to bring the entire decision lifecycle (from data to context, to insight and action) into one governed, trustworthy system.
Main capabilities
- Data connection and governance: connect all existing data sources, set sources, permissions, and rules for each project, and follow existing permission and governance systems.
- Metric consistency: keep metrics consistent through shared definitions.
- Quality measurement: use golden queries to measure quality over time.
- Multiple output forms: can be published as presentations, documents, dashboards, or messages.
- Context continuity: follow-up questions take place in the same thread and automatically update as the underlying data changes.
- Narrative consolidation: combine analysis into narratives leaders can use.
- Transparency: provide transparent sources, assumptions, and lineage.
- Reusability: can be reused across teams and cycles, and saved work can be used by teams to continue building on.
- Natural language access: access data and documents in plain language.
- Built-in governance and accountability: built-in governance and accountability mechanisms.
- Enterprise-grade security: data remains in the user's own cloud, region, and controlled infrastructure; AI is governed by an independently audited management system that complies with international standards for responsible AI; data is not used for training and will not leave without permission.
How to use Users connect existing data sources, set the sources, permissions, and rules for each project, set shared metric definitions, and then ask questions in natural language, for example asking why a certain number changed. The platform reasons based on business context and generates actionable output including definitions, assumptions, owners, permissions, and lineage. Output can be published as presentations, documents, dashboards, or messages, and follow-up questions continue in the same thread and automatically update as the underlying data changes. The platform also helps operationalize decisions, including routing work, assigning next steps, managing approvals, and rechecking output when the underlying data or context changes.
Use cases QueryStory lists multiple functional scenarios: accounts based on the latest contextReview; turn pipeline changes into the next best action; use completed work to prepare forecasts; turn variance into clear actions; provide board-level metrics with a clear audit trail; understand the true forecast drivers; provide a complete customer journey for each campaign; provide campaign attribution that stands up to scrutiny; prove collections with confidence; provide action signals that drive deals; provide clear guidance on pipeline effectiveness; submit effective status updates to management; resolve process bottlenecks; provide agentic solutions for understanding total cost; and an end-to-end problem-solving system. In terms of data and analytics, the platform supports stories that drive people to act, metric definitions and governance, and analytics that are auditable and manipulable.
Limitations and notes Materials do not provide complete details on specific pricing, packages, platform availability, language support, deployment timelines, or integration lists. The platform explicitly states that it does not require replacing existing data warehouses or tools, and can work with data platforms such as Snowflake, Databricks, and BigQuery, as well as business applications, documents, and other enterprise systems. The materials emphasize that it differs from chat-based data tools that only answer questions, covering the full decision lifecycle. More information on specific usage steps, technical requirements, and limitations is not currently provided in the materials.
