Semantic platform
Vizz Analytics is a semantic platform built around a governed semantic layer. Teams use it to define what their data means, how metrics are calculated, and which business rules apply. Once reviewed and published, those definitions give analysts and AI assistants a shared way to answer business questions.
How Vizz supports the semantic layer
The data catalog shows what’s available in the warehouse so teams can connect their definitions to source data. An independent reviewer checks definitions before publication. Analysts can use published definitions in Vizz Explore, and compatible AI assistants can access them through MCP.
Example: what does net revenue mean?
A column named net_revenue does not explain every business decision behind it. Does it include returns? Which date receives the adjustment? Vizz lets teams document those decisions, bind fields to a dataset, and publish reviewed metric definitions. For example, total net revenue can be defined as SUM(net_revenue), with accepted returns attributed to the original order date.
Build meaning before asking questions
Discover source metadata, document columns, define the dataset grain and fields, then validate and submit definitions for independent review. Published versions provide the context used in Vizz Explore and through MCP.
Work with your existing warehouse
Vizz stores metadata and governance records. Business data is queried in the warehouse rather than copied into a replacement warehouse. Tell us which warehouse you use. Vizz already has connectors for PostgreSQL, Amazon Redshift, and Snowflake, and we can often build, validate, and release a new connector in hours for straightforward integrations. See warehouse connector details.
Who it’s for
Vizz is for teams that use data to make decisions and need to agree on what their numbers mean. Start with a metric that comes up in different reports or conversations, like active customers, on-time delivery, or net revenue.