Is there a paper or some other explanation of what they're doing under the hood?

Here is a quick overview, doesn't really explain the deep details though: https://www.youtube.com/watch?v=ikYsr6nvbdE

Basically think mixture of experts, but each expert is an encoder with it's own input tokenization/feature engineering part and then there is some machinery to parametrize these encoders based on natural language queries and stitch their outputs together to form unified vectors for objects in the index and the queries coming in.

Given that the whole framework is Apache 2.0, you can also check https://github.com/superlinked/superlinked and the docs: https://docs.superlinked.com/ https://docs.superlinked.com/concepts/overview

We have some proprietary tech besides that - mainly embedding models for things like location and then executors for the Superlinked DAG that help you run it on GPU-accelerated Spark and something analogous to that for streaming workloads - those proprietary things are how we make money, we call it Superlinked Cloud.