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What is composite AI?

Composite AI integrates multiple artificial intelligence models and technologies to create a more comprehensive and advanced AI system.

Instead of relying on a single AI model, composite AI combines multiple types of artificial intelligence—such as causal, predictive, and generative AI—to address aspects of a problem or task for a more holistic intelligent solution. The integration enables more advanced reasoning and brings precision, context, and meaning to the outputs produced by generative AI.

An overview of the composite AI process

Identify tasks and components

Define specific tasks or problems to address and identify individual AI components or models that excel at those tasks.


Integrate selected AI components into a cohesive system by combining models, data, and algorithms in a way that allows them to work together congruently.


Ensure the integrated AI components can communicate and share information effectively with standardized interfaces or protocols for data exchange.

Data flow

Design a data flow architecture that enables information to flow among the different components to contribute to the overall decision-making process.

Learning and adaption

Implement mechanisms for the composite AI system to learn, adapt, and improve performance over time, such as causal AI, predictive AI, machine learning techniques, reinforcement learning, or other algorithms.

Decision making

Take input from multiple AI components to develop a strategy for more informed, precise decision making, such as combining the strengths of different AI techniques.

Feedback loop

Establish a feedback loop to continuously improve the composite AI system's performance to analyze outcomes, gather feedback, and refine individual components or the overall integration.


Provide scalability for the composite AI system, such as the ability to handle increasing amounts of data and adapt to continuously evolving requirements.