Architecture
Ridge uses a browser-first architecture to deliver fast, AI-enabled embedded analytics.
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Why analytics needs a new architecture
Traditional embedded analytics is slow, complex, and built for analysts—not everyday business users. Ridge AI takes a different approach: browser-based technology and open-source frameworks deliver a fast, interactive experience that combines traditional analytics with AI. Users can easily build, explore, and act on data through a standalone analytical app that runs directly in the browser.
Local, in-browser computation means selections and filters compute in milliseconds rather than tens of seconds.
Ridge scales by taking advantage of end-user hardware. No need to size a server for peak loads to ensure scalability.
Analytics can be embedded where the user is working rather than requiring them to enter a separate BI environment.
Local computation means lower server compute costs. This is a real consideration when not just users but agents are issuing queries.
How data works in Ridge
Ridge AI integrates with your existing data infrastructure. Rather than forcing users to define a complicated new data model or build a proprietary semantic layer, Ridge is designed to respect and work directly alongside any existing storage, transform, and governance layers.
This architecture allows organizations to simply connect to their data and get started, while Ridge handles the heavy lifting of managing authentication, maintaining the connection, and refreshing the data. Ultimately, this approach enables you to leverage your current data stack without the need to build or maintain redundant shadow data infrastructure.
Ridge supports data refresh and partitioning for Data Connections (enterprise data sources like Snowflake, Databricks, S3 and most databases).
Technical underpinnings of fast in-browser analytics
We build on these technical foundations to deliver AI-native, performant analytics.
DuckDB provides a high-performance analytical database engine. WebAssembly allows that engine to execute inside a modern web browser. This yields efficient columnar analytical processing close to the interface.
In Ridge’s architecture, the Mosaic open-source framework coordinates the analytical application, including query management, interactions like filtering and selections, caching and pre-aggregation.
Ridge Plot is a full-featured interactive grammar of graphics that supports declarative specifications at multiple levels of abstraction.
Ridge is secure by design
Security is critical and part of our design process. Data in-browser is deleted by default once the user closes the browser tab, although caching across browser sessions is possible, if the builder allows it.
Ridge never exposes your entire data stack to end users. You determine what subset of your data goes to a Ridge (the Dataset). Partitioning allows you to further restrict data by user identity, based on an immutable field such as an account id or customer id. Ridge uses JWT-based user and tenant scoping so that a user can always see only what they are authorized to see in a particular Dataset.
Ridge AI follows security best practices including secure credential storage and data encrypted in transit. Ridge is SOC 2 Type II compliant.
Ridge’s architecture is built from the ground up for the AI era.
Ridge changes where analytics run, how they are built, and how users interact with them. By moving computation into the browser and unifying dashboards, agents, and interactivity in one analytical application, it replaces the heavy, server-centric BI model with something faster, more composable, and more scalable.
That shift makes analytics easier to embed, cheaper to operate, and far more responsive to both people and agents. More importantly, it opens the door to analytics becoming a native part of every software workflow rather than a separate destination.