Tool Information
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.
F.A.Q (20)
Continual is an operational AI platform created to assist in building predictive models on data stacks. It eliminates complex engineering requirements, simplifying the predictive models' building and maintenance process through SQL or dbt declarations.
With Continual, you can create and enhance predictive models on data stored on various popular cloud data platforms such as BigQuery, Snowflake, Redshift, and Databricks. It enables you to easily predict customer churn, inventory demand, and customer lifetime value.
Continual allows you to build diverse predictive models. Businesses can predict parameters like customer churn, inventory demand, and customer lifetime value. These predictive models can be enhanced and are always up-to-date, continually improving their accuracy.
Continual is compatible with popular cloud data platforms like BigQuery, Snowflake, Redshift, and Databricks. It sits on top of these platforms to build, deploy, and enhance predictive models.
To create predictive models in Continual, you can utilize SQL or dbt declarations. Users can define features and models declaratively and employ SQL or dbt or extend with Python. This declarative approach simplifies the process, allowing the best use of your data without needing to write code or pipelines.
Absolutely, Continual is designed to be user-friendly for individuals knowledgeable in SQL and dbt. Its declarative approach and the ability to connect to your existing cloud data warehouse streamline the process, making it an ideal tool for modern data teams who prefer SQL or dbt.
No, there is no need for complex engineering to use Continual. It enables the building of predictive models that never stop improving without any complex engineering due to its declarative approach to AI.
In Continual, data scientists can extend the platform by integrating Python. This provides versatility, uniting analytics and AI teams with full extensibility of Continual's declarative AI engine.
In Continual, SQL or dbt plays a critical role. Users can define features and deploy state-of-the-art Machine Learning models using SQL or dbt. Furthermore, feature definitions created in SQL can be shared across your team to speed up model development.
Yes, models built in Continual can be shared across teams. This accelerates the development of models and encourages teamwork and collaboration.
Yes, the models built on Continual improve continually and as a result, predictions are always up-to-date. This leads to more accurate predictions.
Data and models from Continual are stored directly on your warehouse. This direct storage in the data warehouse makes it easily accessible to operational and BI tools.
Continual's predictive models can help in preempting customer churn and forecasting inventory demand. By selectively analyzing relevant data, predictive models can isolate key parameters promoting customer churn or impacting inventory demand, driving strategic decision-making based on these forecasts.
Yes, Continual offers a free trial. You can experience the benefits of automated AI for the modern data stack and decide if it's the right solution for your specific needs.
To request a Continual demo, you can visit their website and fill out the demo request form. You need to provide your First Name, Last Name, Company Name, and Work Email. After submitting the form, the Continual team will reach out to you very shortly.
Yes, Continual can be used by both data teams and data scientists. For those familiar with SQL and dbt, Continual is highly accessible. Additionally, data scientists can extend the platform by integrating Python, ensuring adaptability and wide applicability.
In business operations, Continual can be used to predict demand and inventory, which can help with efficient management and reduction of costs and waste. It can also be used to predict sales and revenue for budgeting and planning purposes. Essentially, Continual can be used in any situation where predictive modeling can enhance decision-making and operational efficiency.
Yes, Continual provides a centralized feature store. You can share feature definitions created in SQL across your team in a centralized manner, enhancing collaboration and accelerating model development.
Yes, predictive models in Continual can continually improve independently. The platform is designed to ensure that your predictive models never stop learning and improving, leading to continually updated and more accurate predictions.
Continual simplifies the process of building and maintaining predictive models through a declarative approach to AI. Users define features and models declaratively, eliminating the need to write complex code or pipelines. Additionally, it eliminates the need for MLOPS platforms, storing all data and models directly on the warehouse for easy accessibility.
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
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