It's a little difficult to say. One space where there seems to be very little research that I've been investigating recently is compression for database records (or in our case, serialised complex objects stored in a data grid) where the statistical model is build across many records and then is constant during compression and decompression, as opposed to being adaptive. This means that you can exploit global redundancy across many records and you don't need to store the model with every item, which is good if your items are small, you store zillions of them and space is at a premium (i.e. you're storing it in RAM).
These use cases are normally pretty application specific though, so I imagine a lot of in-house code gets written for things like this. Seems like a pretty similar use case to this, I'd be interested in seeing details of their algorithm - I can't see it anywhere obvious on their site.
There's a lot to DB compression depending on the type of data access. The PFOR approach is amazing and simple for data arranged in columns without much deviation. For serialized trees you usually end up in LZ* land. But with careful organization it can be tolerable.
These use cases are normally pretty application specific though, so I imagine a lot of in-house code gets written for things like this. Seems like a pretty similar use case to this, I'd be interested in seeing details of their algorithm - I can't see it anywhere obvious on their site.