Continual is an operational AI platform designed to help build predictive models on modern data stacks. Users can easily create predictive models on data stored on popular cloud data platforms like BigQuery, Snowflake, Redshift, and Databricks. With Continual, there is no need for complex engineering or infrastructures such as MLOPS platforms, as it simplifies the process of building and maintaining predictive models through simple SQL or dbt declarations. Features can also be shared across teams, which accelerates model development. Models built on Continual improve continually, so predictions are always up-to-date. Data and models are stored directly on the warehouse, making it easily accessible to operational and BI tools. Continual is versatile and can be useful in a business to predict customer churn, inventory demand, and customer lifetime value. Continual is designed for modern data teams who love SQL and dbt, but it is also equally accessible to data scientists who can extend the platform by integrating Python. Continual’s declarative approach to AI makes it easy to work with for analytics and data teams.
