LLMs, Enterprise Search, and the Future of Information Retrieval with Vikram Srinivasan

Episode Summary

In this episode of The Bhairav Show, Suhas Bhairav speaks with Vikram Srinivasan, Founder of needl.ai, about large language models, enterprise search, semantic retrieval, distributed systems, knowledge graphs, and the practical challenges of building AI-powered information discovery systems for organizations. The discussion begins by examining how enterprise search has changed since the arrival of large language models. Traditional search systems primarily returned ranked lists of documents or records based on keywords, metadata, filters, and relevance signals. LLMs have introduced the possibility of interpreting complex questions, synthesizing information from multiple sources, summarizing documents, and allowing users to interact with enterprise information through natural language. However, the conversation makes clear that adding a conversational interface does not automatically solve the underlying retrieval problem. Enterprise search remains fundamentally difficult because company information is fragmented across documents, emails, messaging platforms, databases, cloud storage systems, ticketing tools, CRM platforms, internal applications, and employee knowledge. The.