The challenge
Knowledge sat scattered across internal documents, PDFs, websites and repositories. People spent time hunting for answers, and a generic chatbot could not be trusted because it had no access to the organisation’s own information.
Our solution
We designed and delivered a Retrieval-Augmented Generation (RAG) assistant that finds the right content first and then lets a large language model answer from it.
- Semantic search over company documentation and knowledge sources using a vector database (Pinecone)
- GPT-based answer generation orchestrated with LangChain, with prompt engineering tuned for enterprise use
- LLM optimisation to balance answer quality, speed and cost
- A Node.js API layer and React interface, with MongoDB for application data
- Containerised with Docker and deployed to production
Results and impact
- Contextual answers drawn from the client’s own knowledge base instead of generic model responses
- Natural-language search replacing keyword hunting across multiple sources
- A production-ready foundation the client can extend to new document sources and use cases