NLP to detect duplicates for very technical language
If you think fuzzy matching does not work, you basically have two options:

Unsupervised: Try topic modeling to find „similar“ products. The problem is that you will need to pre-define the number of products (groups, aka „topics“), which can be a problem. Also topic modeling will only work well if there are sufficient differences in the descriptions. https://github.com/Bixi81/R-ml/blob/master/NLP_topic_modelling.R

An unsupervised approach seems to be a good option for you, judging from what you wrote about your problem.

Supervised: If you have a good set of descriptions with a label (e.g. product 1, product 2, etc), you can use some NLP model to predict which text belongs to which product. Not sure how much you know about it: You could start with a „Bag of Words“ and try things like Lasso, Boosting, or even Neural Nets on this. 

Here is an example on how to apply Neural Nets to text classification. But be aware: in real world applications, text classification can be very tricky, especially if you have many classes (products in your case) and not so many observations per class (descriptions in your case).