Generic AI chatbots give generic answers. When your team or customers need accurate responses grounded in your actual business data — internal docs, knowledge bases, SOPs, product documentation — you need Retrieval-Augmented Generation (RAG). Our RAG Knowledge Assistant delivers exactly that: an AI that answers questions using your own data with high accuracy.

Built on Weaviate vector search, this system implements production-grade RAG with optimized chunking strategies, metadata filtering, and carefully selected embedding models. Documents are ingested, split into semantically meaningful chunks, embedded into vector space, and made instantly searchable. When a question comes in, the system retrieves the most relevant context before generating a precise, grounded response.

The difference between a toy demo and a production RAG system is in the details — chunk size tuning, overlap strategies, metadata-aware filtering, and embedding model selection all dramatically impact answer quality. We’ve built this system with those production lessons baked in, so you get reliable, accurate answers from day one.