When dealing with online harms on social media in India you come across challenges that are unique to India. Firstly a majority of the data is in the form of images, videos and audios and secondly its in various Indian languages. These challenges make it hard to study the extent of harms on a large scale in an automated manner. Some of the foundational tools and datasets that we are building aims to close this game. Feluda is one such tool that makes analysing multimodal and multilingual content easier. As part of this project we reviewed state of the art ML models, evaluated their capabilities and limitations in working with the kind of data we find on Indian social media and built the capability to analyze a collection of videos and run automations to group similar videos together and assign tags to them. This will feed into downstream annotation and review workflows that will help surface thematic trends in videos.