Apps 2024-06-17
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Continual

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Building predictive models on the modern data stack.
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Continual is an AI platform designed to provide an embedded AI assistant, or copilot, for applications. This AI copilot from Continual offers deep integration with your application's data and APIs, aiming to empower users to work more efficiently and achieve more.

With seamless connection to application data and APIs, the copilot is designed to understand your application deeply and can both query data and execute actions to assist users.

Adding the copilot to your application and building experiences driven by a unified copilot engine can be simplified using Continual's provided React components and headless SDK.

The platform supports both AI and human feedback, enabling proficient evaluation and fine-tuning workflows. This constructive mechanism allows the AI copilot to maintain an ongoing learning process and continually improve its performance over time.

Notable features of Continual include providing instant answers, automating user workflows, and creating intelligent experiences. It also offers capabilities such as inline citations, headless interactions, and threaded conversations to enhance user interaction.

Moreover, Continual provides end-to-end visibility and analytics of the copilot, ensuring transparency in its operations. By offering such features, the platform promises to reduce engineering and maintenance costs, increase performance and reliability, and accelerate time to market.

With ultimate simplicity in initial setup and infinite customizability in future application requirements, Continual aims to serve both startups and enterprises as a trusted AI copilot platform.

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Continual was manually vetted by our editorial team and was first featured on March 17th 2023.
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Pros and Cons

Pros

Cloud-based predictive modeling
Uses SQL for app creation
Works with BigQuery, Snowflake, Redshift, and Databricks
No need for complex infrastructure
Models improve continually
Data and models stored on warehouse
Easily accessible to operational and BI tools
Suitable for customer churn, inventory demand, and customer lifetime value predictions
Equally accessible to data scientists
Facilitates Python integration
Shared features accelerate model development
Simplifies process of building and maintaining predictive models
Models are up-to-date
Centralized feature store
Extensible with Python
CI/CD friendly
GitOps workflow support
Zero infrastructure requirement
Works natively with modern cloud data platforms
Declarative model and feature definition
dbt integration

Cons

SQL-centric
Limited to cloud data platforms
Dependency on modern data stacks
No MLOPS infrastructure
Limited extensibility (Python only)
Dependent on dbt compatibility
Not suitable for traditional data management systems
Data must be on the same warehouse
No mention of multilingual support
Dependent on continuous access to data warehouse

Q&A

What is Continual?
What can I do with Continual?
What types of predictive models can I build with Continual?
Which cloud platforms is Continual compatible with?
How do I create predictive models in Continual?
Is Continual user-friendly for people who know SQL and dbt?
Is there a need for complex engineering to use Continual?
How can I integrate Python into Continual?
What is the role of SQL or dbt in Continual?
Can predictive models in Continual be shared across teams?
Are predictions from Continual always up to date?
Where are data and models from Continual stored?
How can Continual help me predict customer churn and inventory demand?
Does Continual offer a free trial?
How do I request a demo of Continual?
Can Continual be used by data teams and data scientists alike?
What are some use cases for Continual in business operations?
Does Continual provide a centralized feature store?
Can predictive models in Continual continually improve independently?
How does Continual simplify the process of building and maintaining predictive models?

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