AI Search Platforms

Anthropic builds Claude as a conversational AI system that generates answers from a large language model trained on text, with an optional web search tool that fetches and cites live pages. When a user or an application enables that tool, Claude runs searches, reads the returned pages, and includes inline citations so people can verify each claim.

Two modes shape how Claude answers. Without web access, it responds from parametric knowledge captured at its training cutoff, so its view of your brand reflects what existed in that data. With web search on, it retrieves current pages through a search provider and grounds its answer in what it reads, which is where recent, well-structured content can earn a mention.

This places Claude alongside ChatGPT, Google Gemini, and Perplexity as an answer engine that marketers now track for brand visibility. AirOps monitors how often Claude and other engines cite and mention your brand across a tracked prompt set, so you can see where you stand and act on it.

Resources: Query your AI search visibility and citation data inside Claude with the AirOps connector

Every MCP (Model Context Protocol) connection runs on a client-server model that lets an AI application discover and use outside capabilities at runtime. The standard is model-agnostic, so the same server works with Claude, ChatGPT, or a coding tool without custom code for each one.

Three parts make it work. A host such as Claude or Cursor launches a client, and that client opens a connection to an MCP server. The server exposes three things the model can use: tools it can call to take actions, resources it can read for context, and prompts it can reuse as templated workflows. Messages travel over JSON-RPC 2.0, either locally or across a remote connection secured with OAuth 2.0.

MCP sits next to concepts like function calling and retrieval, but it standardizes the connection instead of tying it to one vendor. Anthropic open-sourced it in November 2024, and it now runs under the Linux Foundation. AirOps runs its own MCP server, so assistants can query AI-search visibility directly.

Measured in your analytics, AI referral traffic is the sessions whose referrer points to an AI assistant domain such as chatgpt.com, perplexity.ai, or gemini.google.com. Each visit began inside a conversation where the model recommended a source and the reader clicked through. That origin makes the traffic behave differently from a keyword-driven search click, because the model has framed your brand as the answer.

For a visit to count, the AI system has to cite your page, the reader has to click through, and the click has to carry a readable referrer. That last condition is the weak point: many AI-influenced visits arrive with no referrer and get filed as direct traffic in GA4, so the channel is undercounted.

In your channel report, AI referral traffic sits beside organic search and direct traffic, but it answers a sharper question: which AI answers sent people to your site. It is the clicked, measurable half of AI visibility, while citations and brand mentions are the upstream half that often build influence without a click. AirOps ties that upstream citation and mention data to the downstream sessions so both halves read together.

Resources: See which metrics actually track AI search performance and referral quality

Meta AI functions as a conversational answer engine that responds to questions, generates text and images, holds voice conversations, and increasingly returns synthesized answers inside Meta's search bars. It reaches users on Facebook, Instagram, WhatsApp, Messenger, the meta.ai website, a dedicated mobile app, and Ray-Ban Meta smart glasses.

Under the surface, Meta AI runs on Meta's Llama models, the open-weight family developers download and self-host. For questions that need current facts, it grounds answers in real-time web results and in public signals from across Meta's own apps, then writes a response in natural language. That grounding step is why the same prompt can produce a different answer on Meta AI than on a raw Llama model.

This puts Meta AI in the same category as ChatGPT, Google Gemini, and Perplexity: an AI system that answers directly instead of returning ten blue links. Its edge is distribution, since it lives where billions of people already message and scroll. AirOps tracks whether your brand gets mentioned and cited across these answer engines so you can see where Meta AI is surfacing you.

Resources: See how AirOps monitors your brand across AI answer engines like Meta AI

As an answer engine, Grok sits between your audience and the live pulse of X, pulling public posts and web results into responses generated by xAI's models. When someone asks it about products, tools, or brands in your category, it draws on what is being said on X in the moment and updates as that conversation changes.

Under the hood, Grok combines real-time X retrieval with web search through its DeepSearch mode, reasoning modes like Think and a multi-agent Heavy tier, image generation via Aurora, native tool use, and a developer API that is compatible with the OpenAI SDK. You reach it inside X as a compose and reply button, through standalone apps, and at grok.com.

Perplexity leans on live web citations, Gemini ties into Google's index, and ChatGPT blends training data with browsing, so each engine names brands for its own reasons. Grok's tilt toward X means your off-platform chatter there shapes whether it cites you. AirOps helps you see and close those gaps across engines.

For a deeper look, see how to monitor your brand across AI engines including Grok.

As an AI answer engine, DeepSeek takes a natural-language question and returns a synthesized written answer, frequently pulling in and citing live web sources instead of listing ranked links. Its consumer app and API run on the same underlying models.

The technology rests on a Mixture-of-Experts design. DeepSeek-V3 uses 671 billion total parameters but activates only 37 billion per token, and DeepSeek trained it on 14.8 trillion tokens, according to DeepSeek's published model details. Reasoning variants such as DeepSeek-R1 add a chain-of-thought step, so the model works through a problem before it writes the final answer. When web search is on, it retrieves pages, reads them, and grounds parts of its answer in what it found.

DeepSeek sits alongside ChatGPT, Gemini, Perplexity, and Copilot as one of the answer engines where your brand can be mentioned or cited, and its citations rarely overlap with theirs. That makes it a distinct visibility surface with its own citation behavior. AirOps tracks how brands appear across AI answer engines so teams can see where DeepSeek surfaces them and where it leaves them out.

Google Gemini powers both a standalone assistant app and the AI Overviews and AI Mode summaries that appear inside Google Search results, and it answers questions across text, images, audio, and video.

The system pairs large language models (LLMs) trained on text, code, images, audio, and video with a grounding step that can query Google Search in real time. When grounding runs, Gemini retrieves current web pages, synthesizes them into one answer, and attaches citations to the sources it used.

Gemini sits alongside ChatGPT and Perplexity as one of the answer engines shaping AI search, though its distribution through Google Search and Android gives it unusually broad reach. Gemini-powered AI Overviews had more than 2.5 billion monthly active users as of Google I/O in May 2026, according to Google, a scale few answer engines match. AirOps tracks how often Gemini cites and mentions your brand across the prompts your buyers use.

Resources: See how AI search engines pick and cite brands across platforms

Powered by a custom Gemini model, AI Overviews compress an answer from several web sources into a short block with inline citation links. This block occupies the space above the first organic result, so it is the first thing many searchers read. The summary aims to resolve informational queries without a click.

The mechanism is retrieval plus synthesis. Google pulls candidate pages from its search index, and the Gemini model summarizes them into a few sentences with links back to the cited pages. Google reported that AI Overviews reach more than 2.5 billion monthly active users as of its May 2026 Google I/O keynote, which puts your category's answer in front of an enormous audience.

AI Overviews are separate from AI Mode, a full conversational search experience, and from the standalone Gemini app you open on its own. They also sit apart from the traditional ten-link results page, even though they draw from the same index. AirOps helps brands track which pages earn citations inside these summaries and build the evidence that agents trust.

Resources: see the AirOps research on how brands stay visible in AI search

Microsoft Copilot refers to the family of AI assistants Microsoft ships across its consumer and enterprise products, all built on large language models hosted on Azure and grounded in Bing's search index. The consumer version lives in Windows, Edge, and Bing, while Microsoft 365 Copilot adds access to your organization's emails and documents through the Microsoft Graph.

Every Copilot answer draws on three components. The language model handles reasoning and phrasing. Bing supplies the live web data and the candidate sources. A Microsoft orchestration model called Prometheus decides which grounding queries to run and how tightly to anchor the response to what Bing returns.

This makes Copilot closer to Perplexity and Google AI Overviews than to a raw chatbot, because visibility depends on being indexed and cited by a specific search engine. For marketers tracking that visibility, AirOps monitors how often Copilot mentions and cites your brand alongside ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews.

Resources: see how your brand shows up in Microsoft Copilot and other AI search engines.

Perplexity works as a retrieval-augmented answer engine that runs a live web search for each query, then uses a language model to synthesize a sourced, cited response from the pages it retrieves.

Every standard query triggers a real-time retrieval against Perplexity's own search index, built by its crawler, PerplexityBot. The system ranks candidate passages by relevance, freshness, structure, and authority, then assembles the strongest ones into the model's context with citation markers already attached. The model writes an answer constrained to those retrieved passages, so each claim maps back to a numbered source.

This puts Perplexity closer to Google than to a pure chatbot: it does not answer from memory alone, and its answers depend on what your pages say and how well they rank. AirOps helps teams see which of their pages Perplexity retrieves and cites, and track how that changes over time.

Resources: how to structure content so AI answer engines retrieve and cite it

ChatGPT is a large language model (LLM) application that generates human-like text from a prompt, and its newer search feature grounds those answers in live web results.

Under the hood, ChatGPT runs on OpenAI's GPT series of models, trained on large text datasets to predict the next token in a sequence. When you ask a current-events question, ChatGPT search draws on third-party search providers and content from OpenAI's publishing partners, then writes an answer that links back to some of those sources.

ChatGPT sits alongside Perplexity, Google Gemini, and Google AI Overviews in the category marketers call AI search, where each engine answers directly instead of sending clicks to ranked pages. Earning a mention inside those answers is the discipline of answer engine optimization (AEO). AirOps helps brands measure and improve how often ChatGPT cites and recommends them across a tracked set of prompts.

Resources: A practical guide to earning brand citations across AI answer engines

Powered by a custom version of Google's Gemini model, Google AI Overviews sit above the organic results and answer a query directly with a synthesized paragraph and a set of linked citations.

Each Overview draws on Google's search index, so a page has to be crawlable, indexed, and eligible for standard snippets before it can be cited. Google retrieves candidate passages, ranks them for relevance and clarity, then composes an answer that names and links the sources it used. The set of links shown is the citation surface your brand competes for.

Overviews sit alongside AI Mode, Google's fully conversational search experience, and the two cite overlapping but different sources. Winning a citation depends on content structure and authority signals, and Google does not sell the slot. AirOps tracks which prompts trigger Overviews and which pages Google cites, so you can see where you stand before you act.

Resources: See the data-backed framework for earning citations in Google AI Overviews