OpenSearchCon India 2026

Modern AI assistants and knowledge systems rely heavily on high-quality retrieval to provide accurate and contextual responses. While large language models generate answers, it is the underlying search infrastructure that retrieves relevant information and grounds AI outputs in real data.
In this session, we explore how modern AI applications use search infrastructure as the backbone of their intelligence layer. We will examine the architectural patterns that combine OpenSearch with embeddings, vector search, and hybrid retrieval to power knowledge assistants, enterprise search systems, and AI copilots.
The talk will walk through the evolution of search infrastructurfrom traditional keyword-based search to semantic retrieval and retrieval-augmented generation (RAG). We will discuss key design considerations such as document chunking strategies, embedding pipelines, hybrid ranking approaches, and relevance tuning required to build reliable AI systems.

Attendees will also learn how it enables scalable retrieval pipelines that support real-time indexing, vector similarity search, and metadata filtering—capabilities that are essential for powering AI-driven knowledge systems at scale.