Who it is for
AI architects, platform teams, and engineers planning production AI systems.
What problem it solves
AI design is difficult to evaluate when model, data, application, and infrastructure choices live in separate tools.
How it works
Plan LLMs, agents, RAG, vector databases, model gateways, inference infrastructure, and the cloud services around them. It uses the current project or research context so each step remains connected to the work around it.
Key capabilities
- LLM and agent architecture
- RAG and vector database planning
- Model gateway and inference dependencies
- AI architecture patterns
Example use case
Design an enterprise RAG architecture and document model, retrieval, security, and infrastructure dependencies.
Limits and verification
Provider and model support should be confirmed in the product before making implementation commitments.