Organizations struggle with fragmented data meaning across disparate systems, leading to inconsistent reporting, unreliable AI outputs, and a general lack of trust in data-driven insights. This data chaos creates a central bottleneck, hindering agility and preventing teams from leveraging their data effectively for decision-making and product development.

Credible Data provides a semantic data modeling platform that unifies data meaning across an organization, establishing a single source of truth. The platform leverages AI-assisted modeling within familiar IDEs to help data engineers, analysts, and developers build and manage reusable data models. These models serve as a governed foundation for all data experiences, including BI dashboards, embedded analytics, and AI agents, ensuring consistent and explainable answers. By treating data models as production code with Git integration and CI/CD support, Credible enables scalable, trustworthy data experiences.

AI-assisted semantic model creation and refinement within IDEs (e.g., VS Code)

Support for the open-source Malloy language for expressive and reusable data modeling

Git-based version control and CI/CD integration for managing data models as production code

Multiple API endpoints for serving data: REST API and Publisher SDK for embedded analytics, MCP API for AI agents, and standard SQL API for BI tools

Federated governance model with role-based and fine-grained access controls for secure data access

AI-first workbook for interactive data exploration and dashboard creation

Data lineage tracking and usage analytics for visibility and cost optimization

Support for connecting various data sources, including databases and data files

Modular and composable data modeling capabilities with inheritance and encapsulation

Primary customers include data engineers, data analysts, and developers within organizations seeking to establish a unified, governed, and trustworthy data foundation for their BI, embedded analytics, and AI initiatives.