RAG, Research & Multi-Agent AI Systems

A parallel AI analysis architecture where specialized agents independently evaluate emotion, intent, and potential bias before their outputs are merged and synthesized by a final agent. The system demonstrates multi-agent orchestration for deeper, structured, and context-aware analysis.

A context-aware RAG architecture combining an AI agent with vector retrieval, embeddings, and persistent conversational memory. The system retrieves relevant knowledge from a Supabase vector store while maintaining conversation context through PostgreSQL-based memory.

A context-aware RAG architecture combining an AI agent with vector retrieval, embeddings, and persistent conversational memory. The system retrieves relevant knowledge from a Supabase vector store while maintaining conversation context through PostgreSQL-based memory.

An end-to-end RAG pipeline that transforms documents from Google Drive into searchable vector knowledge. Documents are loaded, split, embedded, and stored in Pinecone, allowing an AI assistant to retrieve relevant information and generate context-aware answers.

An automated analysis engine that receives submitted content, processes it through an AI model, applies custom JavaScript logic, and distributes structured results across connected systems including Notion, email, and Google Sheets.

An automated research workflow that dynamically builds search queries and connects directly to the Perplexity API to retrieve web-based information, creating a reusable research layer for larger AI agents and automation systems.

From retrieval-augmented generation and persistent memory to parallel multi-agent analysis, autonomous refinement, and real-time web research, these systems demonstrate how AI can be orchestrated into intelligent workflows that retrieve knowledge, reason across multiple stages, evaluate their own outputs, and execute complex tasks with minimal human intervention.