The future of search is semantic, conversational, and grounded in real-time enterprise data: Ravindra Ramnani, Elastic

In a recent interaction with Express Computer, Ravindra Ramnani, Senior Manager, Solutions Architecture at Elastic, offers a deep dive into how AI is fundamentally reshaping enterprise search experiences. Moving beyond simple keyword matching, Elastic is leveraging semantic search, Retrieval-Augmented Generation (RAG), and Large Language Models (LLMs) to create more intuitive, accurate, and multimodal search systems.

How is AI transforming the search experience, and what sets AI-driven search apart from traditional search engines?

Every search begins with a question, but how do search engines ensure you get the right answer or information? Search engines have commonly relied on lexical or keyword search, a system built on matching exact keywords or phrases in documents to those in a user’s query. While this approach generally works well on broad types of data, this method can encounter challenges when faced with misspellings, synonyms, or ambiguous phrasing.

The integration of AI into search engines has evolved search beyond traditional keyword-based searches through the integration of AI. Semantic search taps into natural language processing (NLP), a branch of AI, to analyse and store a representation of the meaning of the document or paragraph, rather than storing the individual words. This search technique can better understand the meaning and intent of queries.

The efficacy of search engines has been further augmented by the integration of Retrieval-Augmented Generation (RAG) architectures. Combining the language capabilities of large language models (LLMs) with access to up-to-date and domain-specific knowledge, AI-fused search can respond with more accurate, context-aware results, even in the absence of exact keyword matches.

Unlike traditional engines, which treat each query in isolation, AI-driven search can also maintain context across a conversation. They support multimodal search, interpreting images, videos, and even audio alongside text. These search systems are capable of delivering meaningful, accurate, and human-like responses.

What are the biggest challenges in integrating Large Language Models into enterprise search, especially across hybrid cloud environments?

Integrating large language models (LLMs) into hybrid or multi-cloud environments holds tremendous promise, especially for advancing enterprise search capabilities.

Yet, this shift comes with its own set of complexities. Organisations often face operational hurdles as each cloud provider typically has distinct management tools and proprietary APIs, making uniform deployment of AI solutions at scale a challenge.

Moreover, the effectiveness of AI-driven search hinges on the quality and accessibility of data. Poor quality and obsolete data will have a direct impact on LLM results, resulting in inaccurate outputs. Insufficient access to timely or high quality data can also lead to models with significant knowledge gaps and a reduced ability to provide accurate and current responses. Ensuring seamless data availability across diverse cloud environments is critical for optimising LLM performance.

Beyond technical considerations, regulatory and compliance concerns also come into play. Data sovereignty, in particular, becomes increasingly important in hybrid and multi-cloud setups. In India, for example, organisations must align with frameworks such as the Digital Personal Data Protection (DPDP) Act, 2023 (DPDPA), which came into effect on August 11, 2023. The Act mandates strict consent requirements and places restrictions on cross-border data transfers, with organisations having to ensure that sensitive data used for LLM inference is processed within compliant environments.

How does Elastic ensure that AI-powered search results remain accurate, relevant, and free from bias?

Elastic adopts a multipronged approach that blends advanced technology, ethical frameworks, and robust security measures to reduce inaccuracies and bias. We leverage the Elastic Search AI Platform, which combines semantic search and hybrid ranking methodologies, to deliver precise, contextually relevant results while reducing the risk of bias inherent in large language models (LLMs).

As part of our commitment to reliable AI, Elastic enhances accuracy by using Retrieval-Augmented Generation (RAG), anchoring generative AI outputs in real-time, context-rich data from its indexes, helping prevent the hallucinations and misinformation often associated with ungrounded LLMs.

Additionally, Elastic aligns its AI practices with trusted frameworks such as the NIST AI Risk Management Framework, emphasising governance, measurement, and responsible deployment to ensure fairness and transparency. Complementing this, Elastic’s AI Assistant for security includes built-in safeguards, such as protection against prompt injection, data leakage, and unauthorised access, reinforced by custom rule sets and real-time alerts.

Bringing it all together, Elastic reinforces its commitment to ethical AI through strong data quality controls, with the Data Quality Dashboard ensuring outputs that are accurate, trustworthy, and traceable.

What innovations can we expect in this space over the next couple years?

The rapid growth of data across the Internet and within enterprise systems has made it increasingly challenging for organisations to quickly identify the information that matters most.

Progress in AI and search have led and will lead to even more innovative, powerful and accurate search experiences. For example, searches have evolved from being purely text-based to a hybrid approach, which includes semantic search into the mix. Combining this approach with AI has led to more intuitive searches that can interpret context.

Supporting this modern approach to search are vector databases, with Elastic is being an excellent vector database for production use cases. These vector databases are optimised for AI applications and excel at identifying similar items quickly even if there are no exact matches. They have become essential infrastructure for AI-driven search, especially as more organisations integrate RAG and semantic search capabilities into their search systems.

Building on this, features like faceted search in the Elastic Search AI platform can help users refine their queries with precision, narrowing down options to improve both search scope and accuracy.

Industries like BFSI, healthcare, and the public sector stand to benefit significantly, with GenAI enabling semantic document search, summarisation, and conversational access to complex data sets.

Such advancements reflect how the convergence of AI and search is creating smarter, more intuitive search experiences. As AI consumes cleaner inputs, its outputs improve, creating a feedback loop that strengthens both technologies. However, we must not forget that effective AI-driven search is highly dependent on access to accurate, relevant and timely data. Search enhances the relevance and quality of data AI systems consume, thereby improving outcomes and enabling faster, more reliable results and insights.