When you search in a help desk or a knowledge base, which gives the best results according to you?
Do you prefer something that is keyword + synonym like Algolia, or something that works kind of like Google (semantic understanding)?
...or at least that's how the medium-to-savvy users will tend to feel about it. In making your decisions you need to think not just about what users think/say they want, but also how they'll react to the system's failure modes.
Please note that my viewpoint is largely inspired by technical queries as opposed to generalized search though. In theory I'd love a mix of both with priority on keyword results under the presumption that semantic metadata is mostly curated by humans.
Of-course the sensitive info could be blacked out or the rep could willfully not add it to the solutions DB.
However, semantic search is a hard machine learning problem and it requires a good volume of search queries so that they can be mapped to the results returned and mine for patterns.
What's the middle ground? You can show a machine learning based "related searches" as a hint in the sidebar. That way, you can help the user construct their search phrases.
Shamless plug: if you like Algolia but would like to host the search engine yourself and don't want to manage Elasticsearch, take a look at Typesense: https://github.com/typesense/typesense
Private info like name, email Credit cards, pins can be blacked out.
Although between the two, I think I use keyword searching more an a regular basis. Even in semantic search engines, I tend to use a lot of quotes to get some keyword based results.