How do you prevent made-up answers from an AI document search?+
We ground every response in your own content using retrieval (RAG) with citations, and add guardrails so the system says when it does not know instead of inventing an answer.
What is RAG and why does it matter for knowledge search?+
Retrieval-augmented generation fetches the most relevant passages from your documents at query time and gives them to the model as context, so answers are based on your trusted, current knowledge with sources — dramatically reducing hallucination.
What document sources can it index?+
Docs, wikis, PDFs, support tickets, intranets, and databases — most internal knowledge can be connected and kept fresh with automatic re-indexing as content changes.
Can it respect our user permissions?+
Yes — access controls ensure each user only gets answers drawn from content they are allowed to see, so restricted information stays restricted.
How much does an AI knowledge base and document search system cost?+
It depends on the number and complexity of sources, access-control needs, expected query volume, and integrations. We scope a pilot on your highest-value content first, then expand once it proves useful.
