What is Search AI?

Search AI is a new technology that combines the defining features of advanced search technology with the defining features of artificial intelligence (AI), creating a transformative approach to enterprise information retrieval and analysis. Much like how lakehouses combine the strengths of data lakes and data warehouses, Search AI integrates the best of search and AI to produce the most relevant and accurate answers and actionable insights.

Search AI enables people to quickly find answers to their questions from any data — from text and log files to code, security events, and more. Questions that might be as simple as "What should I wear to my best friend's wedding in the spring?" to questions that are a lot more complex, such as "I received an alert on my firewall followed by an alert on one of my endpoint devices. What does this mean and what should I do?" With Search AI, all of these questions can be answered swiftly.

Search AI is important because, as the volume of data in organizations grows, it becomes harder and harder for customers and employees to find answers to their questions from that data using conventional methods.

Limitations of search technology alone

Traditional search technology revolutionized information retrieval, enabling instant access to the right pieces of information from vast quantities of data. However, while conventional search excels at returning relevant results from unstructured datasets, it falls short when the user is looking for a specific answer versus manually digging through a list of results and digesting the results themselves.

Examples of search use cases:

• A shopper searching for a shirt on a clothing store's website
• A user searching FAQs for answers
• An employee parsing through internal resources
• A site reliability engineer (SRE) searching through logs to identify the root cause of an incident
• A security operations center (SOC) analyst searching through security events to determine whether or not there was a security breach
• A knowledge worker searching through internal knowledge bases to perform day-to-day tasks

But with so much data to parse through, search loses effectiveness. At the end of the day, search technology outputs a list of responses.

Limitations of artificial intelligence alone

AI is exceptionally adept at identifying patterns, generating insights, and automating complex analyses. However, AI often faces challenges when pinpointing specific data within expansive, unstructured datasets. Its power lies in analysis and processing rather than direct data retrieval.

For instance, traditional AI, like machine learning, might help with things like pattern recognition and anomaly detection to help users quickly narrow down the data. But even with traditional AI, the number of alerts that are generated to give you insights into your data is overwhelming. It's impossible to manually deal with the number of alerts your data generates.

With the advent of generative AI technologies, there's a different way to get answers. Generative AI produces answers instead of lists of results, as traditional search and machine learning technologies do. Unfortunately, unlike search and machine learning technologies, generative AI doesn't have the real-time context outside of the data it's been trained on.

Search AI solves this problem

Search AI merges the complementary strengths of search technology and artificial intelligence. It uniquely enables organizations to capitalize on the exponential growth of underutilized, unstructured data. Search AI ensures that the most relevant data is found through search technology and then passed into the large language model (LLM) through retrieval augmented generation (RAG) to generate answers based on a user's original question.

This combination of technologies finally enables users to easily get relevant answers to their questions from any data in real time. At an enterprise level, Search AI is an important productivity support tool. Search AI empowers businesses to make better decisions, enhance operational efficiency, drive innovation, elevate customer experiences, ensure resilience, and mitigate security risks.

Search AI can optimize content discovery and data access, improve customer support, enhance decision-making, democratize knowledge, and increase operational efficiency.

Search AI is critical in fields like cybersecurity and observability, where it improves issue resolution speed by swiftly identifying threats and anomalies from all the logs, security events data, and more, and provides actionable steps for remediation or response. Improved mean time to resolution (MTTR) translates into increased operational resilience for your systems, improved security posture, and enhanced productivity for your teams.

Search AI works by combining sophisticated search technologies with AI technologies, such as natural language processing (NLP), machine learning (ML), and generative AI models. Here's a simplified breakdown of its core functions:

Intelligent data retrieval: Search AI first takes an input, understands the semantic and contextual meaning behind the input, and then retrieves the right pieces of data that are most relevant to the input. The retrieval mechanism involves several technologies working seamlessly together, including NLP for interpreting the context behind an input, Best Match 25 (BM25) — a ranking algorithm used in information retrieval to determine the relevance of documents to a given search query — and vector search for finding similar items in large datasets by representing them as vectors (numerical representations) and comparing their similarity in a vector space.

Insight and answer generation: Once the relevant data is retrieved, Search AI uses advanced AI-powered analytical methods to extract meaningful insights and generate accurate, context-aware answers. For example:

• Pattern or anomaly recognition: Machine learning algorithms scan through the retrieved data to identify recurring patterns or flag anomalies from the retrieved data.
• Correlations: By analyzing relationships between different data points, Search AI can identify significant correlations that might otherwise go unnoticed.
• Context-aware, natural language answers: Deliver highly contextualized answers in natural, human-like language. This significantly enhances usability, allowing users of all levels of technical acumen to interact intuitively with complex data without needing specialized analytical expertise.

Search AI provides organizations with a comprehensive, real-time view of all their data. By unifying disparate data sources, it enables instantaneous access to critical insights, supporting rapid, informed decision-making. This real-time intelligence fosters confidence across decisions, irrespective of data volume, type, or complexity.

For instance, a global retail chain can use Search AI to unify sales data, customer feedback, inventory levels, and competitor pricing from multiple systems — CRM, ERP, ecommerce platforms, and public web sources. Search technology retrieves all relevant signals in real time, like a sudden spike in returns for a specific product and social sentiment trends from reviews. AI then analyzes this retrieved data to identify a pattern, such as a design flaw causing dissatisfaction in a high-revenue item. The insight generated? Halt future orders, issue a fix, and reallocate marketing spend, preventing revenue loss and preserving brand trust.

Maximize efficiency

By rapidly delivering precise, relevant data, Search AI eliminates operational inefficiencies. It streamlines workflows and automates tasks across a unified, open platform. Organizations can significantly reduce costs, improve productivity, and strategically allocate resources.

A financial services firm might use Search AI to unify expense reports, procurement data, software license usage, and employee access logs from tools like SAP, Coupa, and Okta. In this example, search technology retrieves patterns showing underused software licenses across departments. AI then analyzes this data to flag redundant tools, calculate potential savings, and recommend consolidating vendors. The result: The company eliminates overlapping subscriptions, renegotiates contracts, and cuts software spend by 30% — all without manual audits or disruption to teams.

Drive innovation

Search AI empowers organizations to leverage the full potential of their data assets, both structured and unstructured. By seamlessly integrating data with generative AI capabilities, organizations transform creative ideas into tangible innovations, expanding possibilities and enabling groundbreaking applications.

In a real-world scenario, a legal tech company can use Search AI to transform its archive of millions of underutilized client contracts, stored as PDFs, into a new customer-facing product. Search technology extracts and indexes key clauses, terms, and metadata from these contracts, regardless of structure or format. When clients ask questions like “What’s my earliest termination right?” or “Which contracts have auto-renewal clauses?,” AI analyzes the retrieved clauses in context and provides clear, contract-specific answers. The result is a self-service legal intelligence tool that reduces dependency on legal teams and creates a new, revenue-generating product line from dormant data.

Elevate customer experiences

With Search AI’s open, high-performance architecture, organizations can develop sophisticated, intelligent digital experiences. Developers gain powerful tools to build scalable, personalized, and seamless applications and services, significantly enhancing customer interactions and satisfaction.

For instance, a travel booking platform can use Search AI to combine real-time flight data, past booking history, customer support chats, and user behavior across web and mobile apps. When a traveler searches for a last-minute trip, search technology retrieves relevant itineraries, loyalty perks, and similar traveler preferences. AI analyzes this data to personalize results, offering optimized routes, upgrade suggestions, and dynamic pricing tailored to the user’s profile and intent. The outcome is a fast, intuitive, and deeply personalized experience that increases conversions and customer satisfaction.

Improve operational resilience

Search AI aggregates logs, metrics, traces, and other operational data into an accessible, flexible format. This unified data view enables SREs to rapidly identify root causes of incidents, resolve issues, and maintain peak operational performance.

For example, a large telecom provider can use Search AI to combine network logs, support tickets, outage reports, and infrastructure telemetry from siloed systems like ServiceNow, Splunk, and OpenTelemetry. When a service disruption occurs, search technology instantly retrieves correlated signals — like error spikes, affected regions, and prior maintenance history. AI analyzes these results to identify the root cause — in this case, a misconfigured firmware update on a specific router model. It then recommends a targeted rollback and alerts the ops team, reducing mean time to resolution from hours to minutes and freeing engineers from manual triage.

Mitigate security risk

Search AI strengthens organizational security by consolidating security signals across cloud infrastructure, endpoints, networks, and applications. Enhanced with AI-driven threat detection and response, security operations center (SOC) teams can proactively identify and neutralize threats, maintaining robust, continuous defense against evolving cyber risks.

A global enterprise can use Search AI to unify data from cloud access logs, endpoint alerts, firewall events, identity providers, and threat intel feeds across tools like AWS CloudTrail, CrowdStrike, Okta, and VirusTotal. When unusual login behavior is detected, search instantly retrieves related signals, like geolocation anomalies, privilege escalation attempts, and lateral movement patterns. AI correlates these events, identifies an active credential compromise, and generates an attack timeline with recommended containment steps. The SOC team is alerted in real time, enabling rapid response and preventing a full-scale breach.

AI-powered search: AI-powered search refers to traditional search solutions enhanced by AI technologies to improve relevance and efficiency, but it is typically limited to better search result accuracy (for example, an ecommerce website that uses semantic search, rather than traditional keyword search). The AI-powered search engine uses vector embeddings to understand similar terms (e.g., "rainproof" and "waterproof") and retrieve conceptually relevant products, even if the product description doesn’t contain the exact query words.

AI search: AI search broadly describes any search functionality that uses AI technologies. It is often limited in scope to enhance search relevance without integrated insights or actions. For example, American web search engine Perplexity AI (or simply Perplexity) uses a large language model to process queries and synthesize responses based on web search results.

Search AI: Search AI refers to the holistic fusion of advanced search and AI technologies designed to deliver insights, context, and real-time actions beyond traditional information retrieval. For example, Elastic's Attack Discovery feature (a cybersecurity application of Search AI) uses search to find relevant evidence across your security data estate. AI brings disparate pieces of data together into a coherent story so you can understand the full picture and respond fast.

Search AI encompasses the functionalities of AI-powered search and AI search but surpasses them through deeper integration, contextual understanding, and actionable outcomes.

Elastic's Search AI Platform gives organizations the freedom to build any application tailored to their specific needs. It provides an open, powerful, and scalable infrastructure, supporting diverse use cases from personalized customer experiences to advanced analytics and automation.

Open by design

Elastic champions openness as a catalyst for innovation and adaptability. The Search AI Platform is built on open standards, promoting interoperability and transparency. This openness enables organizations to maintain flexibility, adapt quickly to changing needs, and ensure long-term viability across diverse use cases. EY integrated the Search AI Platform into its existing generative AI stack to streamline regulatory and compliance reporting. The platform offered seamless integration with other components in EY's stack including LamaIndex, a data framework for building LLM applications, LangChain, and open-source vector databases such as LanceDB.

Built for performance

Elastic's Search AI Platform prioritizes speed, scalability, and accuracy. Whether executing complex queries, deriving actionable insights, or automating real-time actions, Elastic ensures optimal performance. Organizations can trust that every result is returned swiftly, accurately, and reliably at any scale. Docusign found that the Search AI Platform was the only solution that could handle the daily influx of billions of new documents to transform how businesses create, manage, and analyze contracts.

Wired for innovation

Recognizing the dynamic nature of data landscapes, Elastic continuously pushes technological boundaries. The Search AI Platform equips organizations with cutting-edge tools and capabilities to effectively explore, correlate, and act on data from every source, type, and scale. Elastic enables organizations to remain at the forefront of data-driven innovation, continually fueling business value and growth. Using the Search AI Platform, Cogstack partnered with the NHS to transform patient care during COVID-19 by allowing healthcare staff to quickly access relevant data without the need for specialized terminology or coding.

Elastic's solutions for observability and security are built on and included in the Search AI Platform:

Elastic Observability uses Search AI to provide real-time insights into system health and performance. It integrates logs, metrics, and tracing data, enabling teams to identify and resolve operational issues swiftly and maintain superior system reliability.

Elastic Security uses Search AI to deliver real-time threat detection and response capabilities. With unified security signals and advanced analytics, Elastic Security empowers security teams to rapidly identify and mitigate threats, ensuring comprehensive security posture management.

Search AI represents a transformative approach to using the combined strengths of search technology and artificial intelligence. Through Elastic's innovative Search AI Platform, organizations can unlock unprecedented value from their data, achieving enhanced decision-making, improved operational efficiency and resilience, enhanced customer experiences, and strengthened security posture.

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