What is AI search?

What is an AI search engine?

AI search uses artificial intelligence, large language models (LLMs) and semantic understanding to interpret natural-language questions and return synthesized answers with cited sources. Instead of matching keywords to indexed pages, an AI search engine analyzes context, retrieves relevant source material and generates a response grounded in that information.

AI search can refer to consumer-facing answer engines, such as ChatGPT Search, Perplexity, Google AI Mode and Microsoft Copilot, as well as enterprise AI search tools that help employees search private, governed company data. For example, Databricks AI Search brings natural-language search to internal products and organizational data, helping teams find trusted information at scale. Both use cases are changing how people find answers, whether they’re searching the open web or querying data inside an organization. In this article, we’ll cover how AI search works, where it’s used and why it matters.

Why AI search matters

On the consumer side, the availability of AI-enhanced or AI-driven search has already changed people’s expectations, with direct answers rather than lists of links now considered to be a standard feature. Voice queries, mobile habits and increasing availability of conversational AI tools have trained users to ask questions the way they would ask a colleague, and to expect a coherent response, not ten URLs to sort through.

On the business-side, companies face two related pressures. Externally, they need their content and products to be visible inside AI-generated answers, not just highly ranked in traditional search results. Internally, they need to make their own data, documentation and knowledge discoverable through natural language.

Both challenges require search systems that understand meaning, not just keywords. According to generative AI adoption trends, organizations are moving quickly, with enterprise spending totalling $37 billion in 2025, more than triple total spending from the prior year.

How is AI search different from traditional search?

Traditional search engines match the words in your query to words in an index. If you type "best plumber NYC,” the results pages will contain those words or various combinations of them, ranked by relevance signals. The search engine doesn't know (or care) what you mean or what you are trying to do, it just knows what you typed.

AI search tries to interpret the meaning and intent behind your query, but also supports follow-up questions and generates a natural language answer rather than linking you to web pages it thinks are most likely to contain what you are looking for. That shift, from keyword matching to interpreting meaning and intent, is the biggest difference between search engine results and working with AI search. The table below highlights some of the other ways the two technologies differ.

How do these differences show up in the real world? One of the most obvious and significant ways is the rise in zero-click searches, where users find their answer directly on the results page without clicking through to a website. For instance, since launching AI Overviews, Google searches that resulted in zero clicks rose from 56% to 69% between May 2024 and May 2025.

How AI search works

Unlike legacy search technologies, AI search isn't based on a single linear, algorithmic process (e.g., tokenize-match-rank). It is several technologies chained together that work in sequence. Understanding the sequence will help clarify both what makes AI search powerful as well as where it can go wrong.

1. Query understanding. AI search first interprets your question and figures out what you actually mean and what your objective might be.
    
2. Embeddings. Your question and any source content are converted into numerical representations. You might think of these as coordinates that place similar ideas near each other to create a "meaning map." That means content about Apache Spark and content about distributed computing will sit close together in that space even if they use different words.
    
3. Vector search. The system searches the meaning map to find source content closest in meaning to your question, even when exact keywords don't match. This is why if you type "blue shoes good for a Friday night" you will likely get results appropriate for a night on the town.
    
4. Retrieval. The most relevant passages, documents or web pages are pulled in to ground the answer in verified source material rather than relying solely on what the model was trained on. For enterprise AI search, a vector database is able to store embeddings to make the retrieval step fast and accurate at scale.
    
5. LLM synthesis. An LLM reads those retrieved sources and writes a single, coherent answer in natural language.
    
6. Citations and ranking. The system includes attribution to sources and presents them so you can verify where the information came from.
    

This pattern of retrieve first then generate is commonly called retrieval-augmented generation (RAG). RAG is the architecture that connects AI search to real source material.

Examples of AI search engines

The following list of AI search solutions covers a wide range of consumer and enterprise tools designed for different types of jobs.

• ChatGPT Search: Web-connected search built into ChatGPT. Strong for reasoning and multi-step questions.
   
• Perplexity: An answer engine built around cited web research and direct links to sources widely used for fact-finding.
   
• Google AI Mode and AI Overviews: AI Overviews generates responses that appear above traditional results. AI Mode is a separate conversational tab for deeper research.
   
• Microsoft Copilot: Includes AI search deeply integrated across the Microsoft 365 ecosystem, including Outlook, Teams and Edge.
   
• Felo: AI search built for users searching in or across multiple languages.
   
• Azure AI Search: Integrates with Azure OpenAI and the Microsoft 365 ecosystem so organizations can build AI-powered search inside applications. 
   
• Databricks AI Search: Enterprise platform for building AI search and RAG applications grounded in a company's proprietary data.

Picking the right AI search engine

There's no single best AI search engine for every situation. The right choice depends on your goals. The table below maps some of the most common use cases to the tools best suited for them.

Note that the enterprise use case is unique. Consumer AI search solutions search the internet. They aren't built to handle proprietary documents, internal knowledge bases or data where access is restricted or limited. Organizations that need AI search for their own data should use a platform built for that purpose, with governance, security and retrieval quality built in.

What AI search can’t do

AI search has changed how people find information, but it has clear limitations worth understanding.

Confidence does not mean accuracy

AI search can respond with an authoritative tone but still be wrong. A 2025 audit of multiple AI systems with web access found that between 30% and 90% of responses were not fully supported (and sometimes contradicted) by the cited sources, depending on the system.

The best AI search engines reduce risk of errors by grounding answers in verified sources, which is one of the benefits of working with a RAG-based system. However, hallucinations can’t be eliminated entirely. Never assume the response of an AI search is entirely accurate. Look for claims, conclusions, statistics or references to research or other expertise in the response and verify that all of them are supported by documentation, data or both.

Good answers require good sources

AI search results are only as reliable as the content it can access. If the available source material includes low-quality, outdated or biased information, responses will reflect that. This is especially an issue for consumer tools that search the internet without transparent source filtering.

Source transparency varies significantly across tools. Perplexity provides numbered citations with every response. Other tools are less explicit about where information comes from, making it harder to evaluate reliability. When accuracy matters, you will likely save time by using tools that show their work.

Blind spots

Most AI models are trained on data up to a specific cutoff date. That means without live web retrieval, they can't answer questions about recent events, updated policies or anything that occurred or may have changed after that cutoff.

Newer tools address this by integrating live retrieval so they are able to access current information. However, not all tools do this consistently, and even those with web access may miss the most recent developments. For queries that need the most up-to-date information, you may want to combine AI search with your own targeted internet research.

The implications of zero clicks

When AI search delivers a complete answer at the top of the search results, users often don't need to click any of the links in the results. For users, that means faster answers. For publishers, it means less referral traffic. Many publishers have reported referral traffic losses of 20% to 30% in 2025, and even up to 90% in limited cases, as AI-generated answer experiences expanded. How AI search engines attribute and compensate content sources remains unsettled, with active legal disputes and licensing negotiations underway across the industry.

Privacy risks for consumers

Consumer AI search tools log and store query data, so providing sensitive information such as internal business data, customer details or confidential documents to a company’s chatbot or search interface means it will be retained by the provider's logging and training systems.

Enterprise teams should review privacy policies before using consumer tools for work queries, and consider whether a purpose-built enterprise search platform with explicit data governance controls is the right fit for sensitive use cases. Tools like Databricks AI Search are built specifically to keep enterprise data within governed, access-controlled environments and separate from any public model training.

Using AI search within an enterprise

Consumer tools that rely on the internet are generally dealing with public information. Enterprise AI search on the other hand needs to access private data, such as documents, tickets, product catalogs, code or transcripts without compromising security. That means respecting access permissions so users only see what they're authorized to see, staying current as data is updated and returning answers grounded in trusted internal sources rather than the internet at large.

Meeting those requirements takes more than an LLM. You need vector search to retrieve the right content, RAG to ground answers in real source material and a data platform that can unify retrieval and governance in one place. Databricks AI Search is one example of this type of technical foundation and features a vector database integrated into the Databrick Platform and Agent Bricks for building AI agents trained on a company's own governed data.

Frequently asked questions

Can AI search read images?

Some AI search engines can process images alongside text, also known as multimodal search. Most enterprise AI search platforms focus primarily on text and structured data, though multimodal support is an active area of development.

How accurate is AI search?

It depends on the system and the query. AI search engines that ground answers in retrieved sources are generally more accurate than those relying purely on model training. However, you should never rely on AI search as a definitive resource or assume its responses are correct.

What's the difference between AI search and a chatbot?

A chatbot is designed to answer questions and assist with tasks, but is not necessarily connected to live information sources. An AI search engine is specifically built to retrieve and synthesize information, typically with citations of source material. For the user, there can be some similarities in the experience, and some enterprise AI search platforms have conversational interfaces.

Is AI search safe for business or sensitive data?

Consumer AI search tools log query data and may use it to improve their models, meaning anything provided to a public AI search engine could be retained by the provider. For business use, this creates real data exposure risk, particularly for proprietary documents, customer data or anything subject to regulatory requirements. Enterprise AI search platforms built on governed infrastructure are the appropriate choice for those use cases.

What is the difference between AI search and semantic search?

Semantic search is a component of AI search. It drives retrieval of content based on meaning rather than exact keyword matches. AI search is the broader system that combines semantic search (retrieval) with an LLM to produce answers and source citations rather than a list of results. You can have semantic search without AI search, but AI search always depends on semantic search as part of its retrieval pipeline.

Build AI search for your data with Databricks

AI search is no longer a consumer-only experience. Enterprises are building it into their own products, internal tools, and AI agents — and the foundation is governed data, vector search, and retrieval-augmented generation. Databricks AI Search provides the vector search and RAG infrastructure that enterprise AI search requires, while Agent Bricks lets teams build and deploy AI agents grounded in their own governed data — all within the Databricks Data Intelligence Platform.

See how Databricks AI Search and Agent Bricks help teams build accurate, governed AI search on their own data.