Abstract: This disclosure relates generally to screening large data-sets with multi-level clustering. The State-of-the-art methods leveraging LLMs with generative capabilities focus on scanning an extraneous and peripheral contents in text documents to retrieve contextually relevant information. This leads to hallucinations and uncertainty in response and results in complicating the generation of actionable insights. The method of the present disclosure performs a two-level clustering at both an intra-document and an inter-document levels to extract context relevant data. The method screens semantically similar sentence chunks at intra-document level to create a concise intra-document embeddings with contextually relevant text data. Further, an inter-document level clustering is performed by computing a cluster integrity and relevancy (CLIAR) index on a plurality of intra-document embeddings to isolate inter-document cluster having semantic representation of contextually relevant contents.