Quick summary: The internal knowledge assistant ingests your documents, turns them into embeddings stored in a vector database, then uses RAG to retrieve the right passages and answer with source citations.

Every company sits on a huge body of knowledge: processes, contracts, technical documents, emails, meeting notes. The problem is that this knowledge is scattered and nearly impossible to search quickly. A new hire can spend weeks finding the right document; a departing expert can walk out with knowledge no one captured. An internal knowledge assistant is built to solve exactly this pain.

The first step is ingestion and digitisation. The platform connects to your data sources — document folders, SharePoint, internal wikis, databases — then extracts, normalises and splits content into small, semantically meaningful chunks. All of this happens inside your infrastructure.

Next, each chunk is turned into an embedding — a numerical representation that captures meaning, not just keywords — and stored in a vector database. So when you ask "what is the warranty policy for enterprise customers", the system understands intent and finds the right passages even if the document phrases it differently.

When a user asks a question, the assistant retrieves the most relevant passages, places them in context alongside the question, and lets a language model synthesise a coherent answer. Crucially, answers always carry source citations, so users can click through and verify against the original document.

This "grounded" answering is what separates a trustworthy assistant from a guessing chatbot. Because the model is only allowed to answer from your real documents, the risk of fabricated information drops sharply, and every answer is traceable.

The result is an in-house expert working 24/7 — one that never forgets, never takes leave, and grows smarter as your document base grows. Employees find answers in seconds instead of hours, organisational knowledge is retained rather than lost with turnover — and it all happens safely behind your firewall.

Frequently asked questions

Where does the internal knowledge assistant get its data? From internal sources such as document folders, SharePoint, wikis and databases.

How trustworthy are the assistant's answers? Every answer carries a citation to the source document so users can click through and verify directly.

Can the assistant make things up? The risk drops sharply because the model may only answer from real retrieved documents, not guesses.