React, RAG, LLM streaming, vector retrieval
Internal AI knowledge assistant — DXS
Senior Frontend Developer · DXS
I helped bring AI into DXS consulting workflows with an internal knowledge assistant: documents are ingested and chunked, retrieved against a vector index, and answered through a streaming UI with source citations. The goal was daily use by consultants — not a demo chat — so grounding, error states, and citation UX mattered as much as the model call.
documents → ingest / chunk → embeddings → retrieve
→ LLM stream → answer UI + citations → consultant reviewDeep dive
From prototype to daily tool
Internal AI only sticks if it survives real consulting pace. We moved from a sandbox chat to a surface with loading, retry, empty retrieval, and citation affordances so people could verify claims against the source pack before putting language in front of a client.
Streaming interface
Answers stream token-by-token with interruption and reconnect behaviour. Citations map back to retrieved chunks so the UI can show where a claim came from — critical when the audience is consultants who will not paste unchecked LLM text into deliverables.
Enablement loop
Alongside the product I trained 20+ consultants on practical AI use: when to trust retrieval, how to prompt for grounded answers, and where automation helps vs. where human judgment stays mandatory.