Make enterprise documents searchable with RAG. Retrieve relevant information from authorised sources and use it to ground AI responses in company knowledge.
Let’s talk about your AI projectRetrieval-augmented generation, or RAG, finds relevant information in your sources before the model answers a question. It supplies context at response time, rather than retraining the model’s weights for every document.
PDFs, office documents and suitable data sources are processed with their content, versions and access rules. Extraction and document structure affect answer quality, particularly when sources contain tables, scanned pages or technical terminology.
A useful answer begins with a readable, current source.
The system searches for and ranks passages relevant to the user’s question. Permissions are designed into retrieval and response generation: indexing a document does not mean every user should be able to see it.
Unavailable information and information outside a user’s permissions require deliberate handling.
The model uses retrieved content to produce an answer with appropriate references. RAG does not eliminate incorrect answers: missing sources, outdated documents and poor matches can still affect results. Evaluation with real questions and clear handling of insufficient context are essential.
Fluency alone is not a measure of quality.
For each question, the system retrieves relevant information from permitted sources. AI uses that context to prepare an answer you can trace back to its source.
Define documents, databases and access permissions.
Make information searchable by its meaning.
Consider the question and the user’s access rights.
Find relevant passages the user is permitted to access.
Prepare a contextual answer and show its sources.
This approach uses company knowledge without requiring model retraining. When sources are insufficient, the system should say so.
RAG SystemsTell us about your processes, data and challenges. Together, we can identify a practical first use case.