Not much tooling I do it all on Linux.

I use audacity to capture the audio stream live, whisper.cpp with person segmentation to extract the text with notations on who was talking and a fine tuned llama model which is trained on data specific to the companies market to extract the minutes. It's a couple of passes for the model to write the prompts to extract the data into minutes.

The RAG system is just untrained sbert weights of every sentence in the recording and minutes shoved into the vector postgres. Queries are @mentions in the company chat.

Basically a bunch of python scripts held together by duct tape and chewing gum.