RAG-Chat is a document chat application that processes files in the background, stores searchable vectors, and returns answers with source citations.
Document-grounded chat system with background file processing. Uploads trigger async ingestion pipeline: parsing, chunking, embedding, and vector storage. Chat queries retrieve relevant chunks with citations.
Document-grounded chat system with background file processing. Uploads trigger async ingestion pipeline: parsing, chunking, embedding, and vector storage. Chat queries retrieve relevant chunks with citations.
Next.js frontend, Express API, BullMQ workers for async processing. OpenAI for embeddings and generation. Qdrant for vector storage with HNSW indexing. Redis for job queue.
The main technical choices behind the product, from system design to the parts that make it work day to day.
Additional work across AI products, developer tooling, and full-stack systems.
Next.js 16
An AI coding workspace where developers can describe apps in plain language, generate production-ready code, inspect and edit files in real-time, run projects in isolated Docker environments, publish live previews, and sync everything directly to GitHub without leaving the product.
Next.js 16
An AI chat platform that routes each request through the right context — memory, documents, tools, or research — and acts on the answer through connected apps.
Next.js
Creative production system built at Foyer Tech — led the v2→v3 rebuild with reusable templates, multi-model routing, and faster repeat workflows for high-quality visual asset creation.