Akash Balamurugan

Built a Multi-tenant Agentic AI content creation platform where creators automate their writing workflow with customizable agents that draft, retrieve, and analyze posts in their own style; deployed on AWS. Architected the full-stack system end-to-end (React 19/Vite, FastAPI, PostgreSQL 18 + pgvector) with a LangGraph multi-agent backend. Built a multi-agent system where specialized agents collaborate to research, draft, analyze and write content via a shared tool layer (semantic search, websearch) with a human-in-the-loop approval step. Engineered a RAG pipeline (LangChain, pgvector, HNSW, Gemini embeddings) plus an adaptive style-memory engine so the AI writes in each user’s evolving voice; multi-tenant, CI-tested with GitHub Actions.

Built a multi-tenant agentic AI content creation platform deployed on AWS. Architected the full-stack system end-to-end (React 19/Vite, FastAPI, PostgreSQL 18 + pgvector) with a LangGraph multi-agent backend. Implemented a multi-agent system where specialized agents collaborate to research, draft, analyze, and write content via a shared tool layer (semantic search, websearch) with a human-in-the-loop approval step. Engineered a RAG pipeline (LangChain, pgvector, HNSW, Gemini embeddings) plus adaptive style-memory engine to preserve evolving user voice; ensured multi-tenant architecture with CI testing via GitHub Actions.

Built a multi-tenant agentic AI content creation platform deployed on AWS. Architected end-to-end full-stack system (React 19/Vite, FastAPI, PostgreSQL 18 + pgvector) with a LangGraph multi-agent backend. Built a multi-agent system where specialized agents collaborate to research, draft, analyze and write content via a shared tool layer (semantic search, websearch) with a human-in-the-loop approval step. Engineered a RAG pipeline (LangChain, pgvector, HNSW, Gemini embeddings) plus adaptive style-memory engines so the AI writes in each user’s evolving voice; multi-tenant, CI-tested with GitHub Actions.