AI & LLM Integration

I integrate AI and large language models into production applications — not as a novelty, but as core functionality. My AI projects include narrated esports replay analysis, RAG-powered document validation, and autonomous multi-agent crypto analytics.

Experience

GAMEPLAN uses Anthropic Claude to generate AI narration for VALORANT match replay analysis, giving Cloud9 coaches automated strategic insights. VERDEX is a full RAG pipeline — Pinecone vector search over LMA framework documents with Claude for compliance analysis. TORY uses Fetch.ai's multi-agent framework with asi1.ai for autonomous tokenomics analysis across Web3 projects.

Approach

I treat AI as an infrastructure component, not a black box. For RAG, I handle the full pipeline: document chunking, embedding generation, vector storage (Pinecone), retrieval, and prompt engineering for accurate outputs. For multi-agent systems, I design specialized agents with clear responsibilities and structured communication. On the frontend, I build streaming UIs that show progressive AI responses.

Key Results

• •AI-narrated match replay analysis for Cloud9 VALORANT coaching staff using Claude
• •RAG pipeline with Pinecone vector search for LMA regulatory framework validation
• •Multi-agent architecture with Fetch.ai uAgents for autonomous crypto data analysis
• •Streaming AI interfaces showing progressive responses and real-time agent status

Tools & Technologies

Projects Using AI & LLM Integration