A RAG Knowledge Assistant That Cut Support Resolution Time
How Apex Data Cloud built a retrieval-augmented generation assistant grounded in a company's own documentation — an anonymized, representative engagement.
Summary
A services company's experts spent hours answering repetitive questions from scattered documentation. We built a RAG assistant grounded in their approved knowledge, with citations and evaluation, cutting time-to-answer while keeping responses accurate and traceable.
This is an anonymized, representative engagement. Details illustrate the type of work and outcome, not a specific named client.
The challenge
Knowledge was scattered across documents, wikis, and past tickets. Staff spent hours searching — or interrupting senior experts — to answer questions that had been answered before. An off-the-shelf chatbot wasn’t an option: answers had to be accurate, current, and traceable to approved sources.
What we did
- Built ingestion that stays current. Pipelines pull from the company’s documentation and knowledge sources and keep the index fresh (data engineering).
- Engineered retrieval for accuracy. Structured chunking, strong embeddings, and hybrid retrieval so the RAG system surfaces the right context.
- Grounded generation with citations. The assistant answers only from retrieved, approved content and links to its sources.
- Evaluated rigorously. An evaluation harness measures retrieval and answer quality, so accuracy is proven and maintained — not assumed.
The outcome
Staff get accurate, sourced answers in seconds instead of hunting through documents, freeing senior experts for higher-value work — while every answer remains traceable to an approved source.
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