That's true, I'm making recommendations based on Multinomial Naive Bayes (and SGDClassifier) over custom TF-IDF bags of words, so it is like search plus text classification. And some endpoints do just check the cosine or Jaccard distance between things. There is a lot of overlap between search and NLP.

My approach to AI is somewhat conservative because of working in a law-adjacent field where explainability is paramount. When it comes to getting "smart" I prefer forward-chaining logic over facts, and facts include predictions from models too. But at least there is a "judge"/engine to coordinate how the predictions from the ensemble of models maps to actions. I love me some pertained neural nets, but use them more as black box appliances.