AiAssist

// The Challenge

Support teams spend hours answering the same questions repeatedly, and knowledge base articles go unread. We wanted to build a chatbot that could actually answer questions accurately using existing documentation — not hallucinate — and get smarter over time as subject matter experts correct its responses.

// The Solution

We built a RAG pipeline that syncs knowledge base articles on a schedule, chunks them into ~400-token segments with semantic overlap, and generates vector embeddings using OpenAI's text-embedding-3-large model. When a user asks a question, the system performs cosine similarity search to find relevant context, then generates an answer with GPT-4o — always citing the source articles. The key differentiator is the ground truth system. When an expert reviews a conversation and corrects an answer, that correction is embedded and stored as ground truth. Future queries that match closely (>0.95 similarity) return the expert-verified answer directly, creating a feedback loop that continuously improves accuracy.

// Key Features

// Results