So fuzzy matching only makes sense if you expect two columns having the same data more or less, otherwise you can skip that step.
And then you have to pick a threshold -> if similarity of strings is above that threshold, it's a match, otherwise, not. Threshold should be high to prevent false positives. LLM will take care of the non-matches
Yep! We have lots of examples like that where two vendors, or two customers, are completely non-matching. With LLMs and LLM web agents, you also can associate things that are not the same entity.
One example we have is merging a table of companies to a table of company websites. You get things like "Acme Corp" matching "my-logicistics.com" that no LLM has memorized, so you have to look them up using the web. ReAct web agents work really well here, but it can be very expensive, so it's all about doing this cost efficiently.
