Composable AI
Build complex AI systems from simple, interchangeable parts - like Legos for AI infrastructure
Composable AI lets you build complex AI systems by combining modular components, similar to how Legos or Docker containers work.
What is Composability in AI?
Composability in AI refers to the ability to design and assemble modular AI components that can interact, function independently, and be reconfigured to meet specific use cases.
Flexibility
Easily adapt or extend functionality to meet changing requirements
Scalability
Build systems that can grow without losing efficiency
Interoperability
Integrate seamlessly with diverse tools, APIs, and platforms
Efficiency
Reuse and repurpose components to reduce development overhead
How RAGNER Enables Composable AI
RAGNER turns every API into a standardized, easily callable building block—complete with consistent documentation and usage patterns.
- Manual API integration for each platform - Complex pipeline setups with fragile links - Siloed analytics for each platform - Rebuilding workflows for new platforms - Platform takes fees
- Manual API integration for each platform - Complex pipeline setups with fragile links - Siloed analytics for each platform - Rebuilding workflows for new platforms - Platform takes fees
- Reusable API building blocks - Plug-and-play Pipes for ingestion to output
- Unified analysis through AI Agents - Swap or add Connectors for new platforms - Usage Revenue distributed to creators
Interactive Example: DeFi Yield Optimization
Why Developers Love RAGNER
- Zero Friction: Save weeks with pre-built Connectors - Rapid Prototyping: Reuse existing Agents for quick feature additions - Cross-Platform: Extend to other platforms without breaking architecture
Always conduct your own research and risk assessment before implementing any DeFi strategy.
Composable AI
Build complex AI systems from simple, interchangeable parts - like Legos for AI infrastructure
Composable AI lets you build complex AI systems by combining modular components, similar to how Legos or Docker containers work.
What is Composability in AI?
Composability in AI refers to the ability to design and assemble modular AI components that can interact, function independently, and be reconfigured to meet specific use cases.
Flexibility
Easily adapt or extend functionality to meet changing requirements
Scalability
Build systems that can grow without losing efficiency
Interoperability
Integrate seamlessly with diverse tools, APIs, and platforms
Efficiency
Reuse and repurpose components to reduce development overhead
How RAGNER Enables Composable AI
RAGNER turns every API into a standardized, easily callable building block—complete with consistent documentation and usage patterns.
- Manual API integration for each platform - Complex pipeline setups with fragile links - Siloed analytics for each platform - Rebuilding workflows for new platforms - Platform takes fees
- Manual API integration for each platform - Complex pipeline setups with fragile links - Siloed analytics for each platform - Rebuilding workflows for new platforms - Platform takes fees
- Reusable API building blocks - Plug-and-play Pipes for ingestion to output
- Unified analysis through AI Agents - Swap or add Connectors for new platforms - Usage Revenue distributed to creators
Interactive Example: DeFi Yield Optimization
Why Developers Love RAGNER
- Zero Friction: Save weeks with pre-built Connectors - Rapid Prototyping: Reuse existing Agents for quick feature additions - Cross-Platform: Extend to other platforms without breaking architecture
Always conduct your own research and risk assessment before implementing any DeFi strategy.