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Case study · AI & ML
A software company launched an assistant that answers customer questions using its own documentation.
The challenge
The company's help articles, release notes and internal runbooks lived in separate places. Support agents searched them by hand, and many tickets were questions that already had a written answer somewhere.
A general chatbot was not an option. It would invent features, mix up product versions and could expose internal notes. The business needed answers that were correct, current and limited to approved content.
Our approach
We reviewed help articles, release notes and past tickets. We marked which content is public, which is internal and which is out of date.
We chose a retrieval augmented approach. The model only answers from passages found in approved sources, and it says so when it cannot find one.
We built the search index, the answering service and a chat widget. Answers include links to the source articles so customers can verify them.
We tested with real past questions, added guardrails against off-topic requests, and gave support a view of where answers fell short.
What we built
Approved docs are split, embedded and kept in sync with the content team's updates.
The model answers only from retrieved passages and cites each source.
Unclear questions pass to a human with the chat history already attached.
Support sees which questions had no good answer, so docs can be improved.
What changed
Common how-to questions are answered without opening a ticket.
Each reply links back to the article it came from, which builds trust.
Gap reports show the content team exactly which topics need clearer articles.
Technology
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