Back to all cases
CASE STUDY / AI & Data Science
AutoLLM — No-Code RAG Chatbot Platform
Enterprise Software, Knowledge Management & GenAIProduction-grade RAG platform with document upload, vector retrieval, multi-LLM switching (OpenAI / Gemini / Claude), dual-layer Redis + PostgreSQL storage, and Docker microservice deployment.
Delivery Details
AutoLLM is a No-Code RAG chatbot platform. Users upload documents, and the system automatically parses, chunks, generates embeddings, and stores vectors (pgvector). Cosine similarity retrieval finds relevant chunks to build context for LLM-generated answers with citations. Built with FastAPI + Next.js full-stack, Redis for short-term conversation cache (3-day TTL), PostgreSQL for permanent audit storage, and a provider abstraction layer for one-click switching between OpenAI / Gemini / Claude. All services orchestrated via Docker Compose, supporting streaming responses (SSE), JWT auth, rate limiting, and automatic versioning.
Industry Context
Enterprise knowledge is fragmented across documents, systems, and teams, while a general chatbot without retrieval, permissions, and citations can produce unverifiable answers. A product-ready RAG platform must manage the document lifecycle, vector search, provider switching, conversation storage, security, and operations together.
Business Problem
Enterprise Q&A often stalls on upload, permissions, model switching, and auditability before it can become a product.
What I Built
Built a no-code RAG workflow across pgvector, Redis, PostgreSQL, and multiple LLM providers.
Business Impact
Reduced document Q&A setup from weeks to hours with citations, streaming, and audit support.
For career conversations, advisory work, or a concrete Data & AI project, feel free to get in touch.