Most of the questions employees ask during the day are already answered somewhere in the company: procedures, product catalogues, price lists, HR policies, technical documents and past proposals. The problem is finding that information quickly. An AI assistant built with RAG (retrieval-augmented generation) first searches company documents for the question, finds the relevant passages and writes its answer based on those sources.
How RAG works
- Preparing documents: Content from PDFs, Word files, SharePoint, wikis and databases is split into passages.
- Indexing: Each passage is added to a search index so it can be found by meaning.
- Search at question time: The passages most relevant to the user's question are retrieved.
- Answer generation: The language model writes the answer using only the retrieved passages and cites its sources.
This approach reduces the risk of the model "making things up" and makes answers verifiable.
Use cases
- HR assistant: Questions about leave, benefits and policies.
- Sales assistant: Product features, technical compatibility and examples from past proposals.
- Technical support: Suggested fixes based on installation guides and resolved tickets.
- Operations: Step-by-step instructions from procedure and quality documents.
- Customer support team: Fast, consistent information for replies to customers.
Critical success factors
- Content quality: Outdated or conflicting documents lead to poor answers; tidy the content first.
- Access rights: The assistant must not answer from documents the user is not allowed to see.
- Citations: Every answer should link to the document it relies on.
- Data security: Clarify where data is processed, retention periods and whether the model provider uses data for training.
- Measurement: Track answer accuracy, usage and user satisfaction regularly.
Where to start
Start with one department and a limited set of documents, such as HR policies or the product catalogue. Improve content and settings with user feedback, then widen the scope.
ujans builds company-specific AI assistants with content preparation, secure cloud infrastructure, permission integration and interface development.