AI Support Copilot
Context-aware support assistant using RAG over product documentation — reducing response time by 45% and improving first-response quality.
Context-aware support assistant using RAG over product documentation — reducing response time by 45% and improving first-response quality.
The client's support team was drowning in repetitive tickets, spending hours searching through fragmented product documentation to find answers. Response times were climbing and customer satisfaction was dropping.
I designed and built an AI-powered support copilot that ingests the entire product knowledge base, creates semantic embeddings, and retrieves contextually relevant answers in real-time — giving support agents instant, accurate responses grounded in actual documentation.
The system follows a modular architecture designed for reliability and easy scaling.
Automated crawler pulls from Notion, Confluence, and file storage. Documents are chunked and pre-processed.
OpenAI embeddings model converts chunks into vectors. Stored in Qdrant with metadata for filtering.
User query is embedded, semantically matched against knowledge base. Top-k relevant chunks retrieved.
LLM generates contextual answer grounded in retrieved documents. Source citations included automatically.
Redis caches frequent queries. Agent feedback loop improves retrieval quality over time.