A conversational assistant fine-tuned on a company's internal document base — able to answer business questions with verifiable citations and sources.
The client had over 12,000 internal documents (procedures, technical sheets, contracts) spread across SharePoint, Notion, and a legacy intranet. Finding the right answer took an average of 18 minutes per query, and 30% of new employees asked the same questions repeatedly during their first three months. The challenge: an assistant capable of reasoning over the internal corpus without hallucinating, citing its sources.
RAG (Retrieval-Augmented Generation) architecture built in Python with LangChain. Documents are vectorised via OpenAI embeddings and stored in a Pinecone vector database. The LLM is called with a strict system prompt that forbids any response outside the retrieved context and enforces source citation. An automatic indexing pipeline detects new documents and re-embeds them without manual intervention. On-premise deployment to guarantee corpus confidentiality.
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