What problem does Myriade solve for enterprises?
Myriade solves the problem of unreliable AI analytics on messy enterprise data. It plugs into a data warehouse as-is to run an autonomous reverse-engineering workflow that maps tables, detects anomalies, validates metrics, and documents relationships, making AI analytics reliable from day one without requiring a pre-built semantic layer or months of data cleanup.
Who is the target audience for Myriade?
Myriade is for enterprise data teams, including Data & AI leaders, AI Engineers, Business Leaders, Chief Data Officers, Data Engineers, and Chief Operating Officers who want to make data work for everyone. It is designed for organizations struggling with enterprise data that is messy due to incomplete catalogs, contradictory metrics, and undocumented tables.
What features and integrations does Myriade offer?
Myriade is an AI-native data platform that connects directly to warehouses and databases including Local MongoDB, MySQL, Postgres, dbt, Snowflake, BigQuery, Databricks, Redshift, Fabric, and Oracle. Its core feature is an autonomous reverse-engineering workflow that documents the entire warehouse by mapping tables, detecting anomalies, validating metrics, and finding zombie tables.
How does Myriade's autonomous workflow function?
Myriade's autonomous workflow plugs into a data warehouse as-is and runs a reverse-engineering process that maps all tables, detects anomalies, validates business metrics, and documents relationships between data entities. This process automatically documents a warehouse in under a week, replacing months of manual work with the data team.
What is Myriade?
Myriade is an AI-native data analytics platform that provides reliable AI analytics even on messy data. It connects directly to an organization's data warehouse to deliver trusted answers quickly, without requiring a pre-built semantic layer.
5 of 6 research questions are answered for this product. The rest need source evidence we have not collected yet, so they are left unanswered rather than guessed.