Enterprise AI Knowledge Assistant

An internal assistant that lets people ask questions of company documents in natural language and receive answers grounded in those documents, with the underlying sources shown alongside each response.

Organisational knowledge is spread across documents that are searchable by filename far more easily than by meaning. Keyword search finds documents; it does not answer questions.

A general-purpose language model answers questions but has no access to internal material, and will produce fluent responses that are not grounded in it — which is worse than no answer in a business context.

The system follows a retrieval-augmented pattern: documents are processed and embedded ahead of time, a question retrieves the most relevant passages, and the model answers using that retrieved context rather than from memory alone.

Grounding was treated as an interface requirement, not just a backend one. Every answer surfaces the passages it drew on, so a reader can verify a claim instead of trusting it. Responses stream as they are generated, which makes the wait legible rather than silent.

Embedding work happens during ingestion rather than at question time, so the interactive path is retrieval plus generation. Responses are streamed to the client so the interface becomes useful before generation finishes, and retrieval parameters are tuned to send the model enough context to be accurate without inflating latency.

Retrieval is permission-aware: a user's query can only match documents they are entitled to see, because filtering happens during retrieval rather than being applied to results afterwards.

Model API credentials remain server-side and are never exposed to the browser, and user content is handled deliberately given that it is sent to an external model provider.

The assistant makes internal documentation answerable in natural language, with each response traceable back to the source material it came from.

In retrieval-augmented systems, most of the quality comes from retrieval rather than from the model. Chunking strategy and retrieval design determine whether an answer can be correct at all.

Trust is an interface concern. Showing sources and streaming progress changed how much people were willing to rely on the system, without any change to the underlying model.