This platform provides semantic data modeling to unify data meaning across fragmented sources. It enables data engineers, analysts, and developers to build consistent, explainable data experiences for BI dashboards, embedded applications, and AI agents. The system leverages an open-source foundation to ensure portability while offering enterprise governance and AI-assisted modeling capabilities.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations struggle to derive value from their data due to inconsistent data representation, siloed data warehouses, and a lack of semantic understanding, hindering the development of effective AI-powered applications. Traditional data tools often lock users into specific ecosystems, further fragmenting the landscape and making reliable analysis difficult.
Solution
MS2 provides a data platform that transforms messy, one-of-a-kind data into AI-ready semantic models, enabling developers to build AI-powered data applications with flexible, scalable, and trusted data access. The platform offers a consistent, high-level representation of data, serving as a foundation for building, sharing, and understanding data products with ease. By using AI to build semantic models and providing enterprise-grade data APIs, MS2 empowers users to transform embedded analytics into ergonomic AI data experiences. The platform aims to accelerate data intelligence by providing clear, governed meaning to data, which is key to trustworthy insights and reliable AI applications.
Target Audience
MS2 targets data engineers, data scientists, and AI developers who need a scalable and consistent data foundation for building AI-powered applications and deriving actionable insights from complex datasets.
Features
- AI-powered data modeling copilot to build semantic models.
- Enterprise-grade data APIs for managing access with scale, security, and performance.
- Embedded data experiences to transform analytics into AI-driven interfaces.
- Open semantic layer using Malloy to consistently define business logic and metrics.
- Standard packaging and serving interface with REST and MCP APIs.
- Developer-focused workflows embracing Git, CI/CD, and packaging for managing semantic models.