A case study of Generative AI in MSX Sales Copilot: Improving seller
  productivity with a real-time question-answering system for content
  recommendation
Manpreet Singh Ravdeep Pasricha Nitish Singh Ravi Prasad Kondapalli Manoj R Kiran R Laurent Boué
In this paper, we design a real-time question-answering system specifically
targeted for helping sellers get relevant material/documentation they can share
live with their customers or refer to during a call. Taking the Seismic content
repository as a relatively large scale example of a diverse dataset of sales
material, we demonstrate how LLM embeddings of sellers' queries can be matched
with the relevant content. We achieve this by engineering prompts in an
elaborate fashion that makes use of the rich set of meta-features available for
documents and sellers. Using a bi-encoder with cross-encoder re-ranker
architecture, we show how the solution returns the most relevant content
recommendations in just a few seconds even for large datasets. Our recommender
system is deployed as an AML endpoint for real-time inferencing and has been
integrated into a Copilot interface that is now deployed in the production
version of the Dynamics CRM, known as MSX, used daily by Microsoft sellers.