Continual is an AI platform for operational purposes that aims to simplify the process of building predictive models on modern data stacks. The platform offers several key features and advantages:

• Compatibility: It is compatible with popular cloud data platforms such as BigQuery, Snowflake, Redshift, and Databricks.
• Simplified process: Building models is made easy as there is no need for complex engineering or MLOPS platforms; models can be built using SQL or dbt declarations.
• Shared features: Model development can be accelerated by sharing features across teams.
• Continual improvement: Models continuously improve over time to ensure up-to-date predictions.
• Direct storage: Both data and models are stored directly on the warehouse, allowing for easy access with operational and BI tools.

Continual caters to various business needs, with use cases including:

• Predicting customer churn to enhance retention strategies.
• Forecasting inventory demand for more efficient supply chain management.
• Estimating customer lifetime value to optimize marketing efforts.

Designed for modern data teams, Continual is accessible to both SQL and dbt enthusiasts, as well as data scientists integrating Python.