Wit seems to be heading towards more conversation-oriented (ie multi-message) approach to NLP which is something existing NLP toolsets don't make straightforward. For example, predicting the next action a bot should take based on conversation history is a bit different than just classifying an utterance. There are hints at how to do this in literature, and Init.ai (disclaimer: I work there) is working on it as well, but it's not widespread.
There's also more to this type of slot filling than just NER. Again, the necessary techniques are available in academic literature, but not necessarily turn key. Plus, you need to handle parsing after locating the slots. For the parsing, you can take a look at Duckling (https://duckling.wit.ai/) which Wit did open source.
Both on the classification front and the slot filling, open source toolkits might get you part of the way there but not all the way.
Having a training and management UI is also a substantial value add once you use it.
There's also more to this type of slot filling than just NER. Again, the necessary techniques are available in academic literature, but not necessarily turn key. Plus, you need to handle parsing after locating the slots. For the parsing, you can take a look at Duckling (https://duckling.wit.ai/) which Wit did open source.
Both on the classification front and the slot filling, open source toolkits might get you part of the way there but not all the way.
Having a training and management UI is also a substantial value add once you use it.