OpenAI “Deep Research” (DeepSearch)

OpenAI’s Deep Research – sometimes informally called “DeepSearch” – is a new ChatGPT agent designed for in-depth web research. Built on a specialized “o3” model optimized for browsing and analysis, it can autonomously perform multi-step searches and synthesize information from the internet.

In practice, a user with ChatGPT Plus or Enterprise can switch to Deep Research mode, enter a complex query, and the agent will scour online sources for about 10 minutes to compile an answer. The results come with clear citations and reasoning, making it easy to verify facts.

According to OpenAI’s notes, this agent can accomplish in minutes research tasks that might take humans many hours, by searching, reading, and analyzing massive amounts of text, images, and even PDFs across the web.

From a user perspective, Deep Research extends ChatGPT’s capabilities into real-time search. It not only fetches facts but also explains its thought process step by step and provides source links for transparency. For example, if asked to investigate a current event, the agent will pull data from multiple high-quality sources (news sites, official reports, etc.) in real time and then generate a detailed report with context and analysis.

This makes it especially useful for analysts, researchers, and students who need trustworthy, up-to-date information. There are usage limits to ensure quality – Plus users may get around 10 Deep Research queries per month, while higher-tier “Pro” users have a larger quota.

Unlike the standard ChatGPT, which has a fixed knowledge cutoff, Deep Research always works with live data and thus is less likely to hallucinate outdated facts. (Notably, OpenAI has not made this feature available via API yet, keeping it in the ChatGPT interface for safety reasons.) Overall, OpenAI’s Deep Research is bridging the gap between conversational AI and traditional search engines by acting as an autonomous research assistant with citations for every answer.

Perplexity AI

Perplexity AI is an AI-powered search engine and chatbot that responds to user queries with concise answers and citations in real time. Often described as a hybrid of Google Search and ChatGPT, Perplexity was founded in 2022 by former OpenAI and Google engineers with the aim of “democratizing access to knowledge” through conversational search. Users can ask Perplexity natural-language questions, and the system will search the web and return a summarized answer with reference links. In contrast to vanilla ChatGPT, Perplexity is always up-to-date and provides sources for every statement it makes.

This focus on sourcing makes it popular for academic and professional queries where the provenance of information is important. The interface feels like chatting with a knowledgeable assistant, but you’ll see footnotes or link buttons that trace facts back to websites (news articles, Wikipedia, academic papers, etc.), reducing the need to manually sift through search results.

Technically, Perplexity uses a combination of large language models (including GPT-4, Anthropic’s Claude, the open-source Mistral model, and its own models) for natural language processing. It “searches the web in real-time” by likely leveraging search engine APIs to find relevant pages, then reads the content with the help of an LLM to formulate an answer. The system is designed to deliver succinct, relevant answers rather than a list of links, thus saving users time.

Perplexity’s answers often include images when relevant and will sometimes proactively suggest a follow-up question or a deeper dive via a feature called Copilot (which breaks down complex queries into sub-questions).

The service is available for free with some usage limits, and they also offer a paid plan for power users that unlocks more daily searches and advanced features like GPT-4 responses, file uploads for analysis, and even text-to-image generation via DALL·E and other tools.

In summary, Perplexity aims to be a one-stop AI search companion: as easy to talk to as a chatbot, but as informative and current as a search engine, complete with citations for trustworthiness.

Anthropic Claude 2

Anthropic’s Claude 2 is another AI assistant that, while not a search engine in the traditional sense, offers powerful information retrieval capabilities through its large context window and up-to-date knowledge in certain deployments. Claude 2 is a large language model launched in 2023 as a competitor to ChatGPT. Its standout feature is the ability to accept extremely long prompts – up to about 100,000 tokens (roughly 75,000 words) – allowing it to ingest and analyze very large documents or multiple sources in one go.

For users, this means Claude can be given a lengthy report, book, or dataset and then be asked questions about it, effectively functioning as a research assistant that can “search” within a custom data corpus you provide. This capability is valuable for anyone who needs to synthesize information from large texts (e.g. legal briefs, scientific papers) without manually reading everything.

In scenarios where Claude is integrated with external data, such as a company’s knowledge base or via a plugin, it can retrieve relevant information and answer questions in a conversational manner. For example, Claude is used in some applications to power customer support bots and can draw on provided documents or knowledge repositories to answer user queries.

Out-of-the-box, Claude 2 does not browse the live web on its own (it has a fixed training cutoff, and as of its release it was knowledgeable up to early 2023). However, it excels at analysis and synthesis of information and can be paired with retrieval tools. Some third-party platforms (like the search engine or DuckDuckGo’s assistants) have experimented with using Claude for answering queries with real-time data.

Generally, to get current information, Claude must be given relevant text (either by a developer hooking it up to a search API or by the user copy-pasting content). Once it has the information, it provides very clear, structured answers and is known for a polite, concise style. Anthropic also prioritized safety and honesty with Claude – it tries to avoid hallucinating and will admit when it doesn’t know something.

In the landscape of AI search, Claude represents a more user-provisioned approach: instead of automatically crawling the web, it relies on users (or integrated systems) to feed it data, which it then analyzes with great depth. Its ability to handle huge inputs means a researcher could literally give it an entire academic journal issue and ask for a summary of relevant points – a task beyond the scope of most other chatbots.

While Claude may not replace a web search engine on its own, it’s increasingly used behind the scenes in tools that do (for instance, as an option in Perplexity’s model settings, or other AI search assistants). In essence, Claude 2 is a strong general-purpose AI with unique strengths in digesting large amounts of information – a complement to the web-focused tools like Deep Research and Perplexity.

Other Notable AI Search Tools

Beyond the above, there are several other AI systems offering information search capabilities that have emerged in the last year:

Bing Chat (Microsoft)

Microsoft’s Bing search engine now has an integrated chatbot powered by GPT-4, introduced in early 2023. Bing Chat can answer questions directly in the search interface, citing sources for its statements. It effectively combines the full Bing web index with OpenAI’s model. Users can even choose the tone of responses (“Precise” for factual, “Creative” for more elaborate answers).

In practice, Bing Chat will perform a live web search for your query, then formulate an answer with footnotes linking to the websites it drew from. It tends to present a concise summary and then suggest some follow-up questions or show related search results. Because it’s part of a search engine, it excels at factual and up-to-date queries. For example, asking Bing Chat “What are today’s top tech news headlines?” will yield a quick summary with references to the news sites.

This tool is free to use (with a Microsoft login) and has become an excellent alternative to traditional search for many users, essentially bringing the chatbot experience directly into web search results.

Google Bard and SGE (Google)

Google’s answer to ChatGPT is Bard, a conversational AI that can draw information from the live web. Launched in 2023 and now based on Google’s PaLM 2 model, Bard is able to “extract information straight from the internet,” giving it an advantage in answering recent questions. For example, Bard handled questions about events in 2022 that ChatGPT (with older training data) could not. Bard’s interface typically provides a few draft answers for each query and may include images or diagrams in its responses.

It doesn’t always cite sources directly in the text, but it has a “Google It” button and often underlines key facts that, when clicked, reveal the source webpage. In parallel, Google has been testing the Search Generative Experience (SGE) inside Google Search, which uses AI to generate summarized answers at the top of search results. SGE will take a query, gather relevant results, and display an AI-written summary with links to the sources it used.

This is Google’s way of blending traditional search with AI – users get the convenience of a quick answer plus the option to click through to authoritative sources. Content creators have noticed their pages being referenced in these AI snapshots, similar to featured snippets but drawn from multiple sites. Both Bard and SGE show Google’s cautious but steady move toward AI-assisted search, ensuring that the answers are backed by the vast index Google has built while giving users a conversational feel.

Others (YouChat, ChatSonic, etc.)

A number of smaller platforms also merge search with AI. YouChat is a search engine that outputs answers in a chat format. It uses its own indexing and an AI model to generate responses, along with footnotes linking to websites. ChatSonic (by WriteSonic) is a chatbot that can toggle a “Google Search” option on – when enabled, it will fetch the latest information from the web and incorporate it into its answer, useful for questions about current events or trending topics.

DuckDuckGo introduced DuckAssist, an experimental feature that uses Wikipedia (and later, other sources) along with OpenAI’s tech to answer questions directly on its results page. And recently, xAI’s Grok (backed by Elon Musk) has launched as a new AI chatbot with an ability to pull in real-time info – Grok is reported to have internet access and a bit of a sarcastic personality, aiming to differentiate itself while still providing sourced answers.

Each of these tools has its own twist, but all share the common goal: reducing the time it takes to go from a question to a trustworthy answer by leveraging AI to read and condense information from the web.

How AI Search Engines Retrieve, Rank, and Present Information

Despite differences in interface, most AI-driven search systems follow a similar pipeline behind the scenes to retrieve, rank, and present information to the user:

Query Understanding and Retrieval

When a user asks a question, the system first interprets the query (often using an AI model to parse intent and keywords). It then performs a web search to gather candidate information. Many tools rely on existing search engines for this step – for instance, OpenAI’s agents and ChatSonic use the Bing or Google search APIs under the hood, and Perplexity has its own method to search the web in real time.

The search component returns a list of relevant pages or snippets. Some advanced systems may run multiple searches iteratively: the AI can take an initial result, refine the query, and search again (a bit like a human researcher would). OpenAI’s Deep Research is explicitly “agentic,” meaning it can pivot and issue new searches based on what it finds. This ensures that if the first pass doesn’t answer the question, the AI will dig deeper or broaden the search automatically.

Ranking and Filtering Sources

Once potential sources are retrieved, the AI system decides which information to trust and use. Often, the initial ranking comes from the search engine (e.g. top Google results are assumed to be relevant). However, the AI can re-rank or filter these using its own criteria. Typically, credible sites (major news outlets, academic or government sites, well-known reference works) are favored for factual queries.

The AI might skim the content of each candidate page – effectively using the language model to judge if a page actually contains an answer to the query. Some implementations use vector embeddings to measure semantic relevance, but more commonly the model just reads the text. If a page is paywalled or full of irrelevant material, the agent will skip it (for example, OpenAI’s crawler automatically skips pages that are behind paywalls or contain disallowed content).

Many AI search agents also cross-check facts between sources

For instance, OpenAI’s Deep Research was designed to verify claims by finding multiple sources and comparing them. If two reputable sources agree on a fact, the AI gains confidence in it; if there’s discrepancy, it might either look for more evidence or at least flag that uncertainty in its answer. This ranking and filtering stage is crucial to avoid blindly trusting a single source (mitigating the risk of getting a wrong answer from one misleading webpage). The end result of this stage is a set of the most relevant, reliable snippets of information that will go into the answer.

Answer Synthesis and Presentation

In the final step, the AI generates a coherent answer to the user’s question using the selected information. The large language model takes the gathered facts and assembles them into a natural-language response. Importantly, the systems aim to preserve provenance: citations are attached so the user can see where each piece of information came from.

Different tools handle this in varying ways. Perplexity and Bing Chat use numeric footnote markers in the answer text – clicking those opens the source article or page. OpenAI’s Deep Research and Google’s SGE provide inline links or a list of sources alongside the answer. The answer synthesis isn’t just copy-pasting sentences; the AI paraphrases and merges insights, ideally adding context or explanations as needed.

For example, if asked a complex question, the answer might be structured in paragraphs or bullet points addressing each aspect, with multiple sources cited throughout. Some systems also show their reasoning: Deep Research will explain why it concludes something, referencing the evidence it found.

The presentation can include images or diagrams if they help (Bard and Perplexity have shown the ability to include images when relevant). Additionally, many AI search tools suggest follow-up questions or related topics after giving the answer, to invite the user to continue exploring.

Ultimately, the goal of the presentation stage is to give the user a clear, correct answer with the convenience of not having to click through multiple links – but still empowering the user to verify details by providing references. This balances the depth of information with transparency.

It’s worth noting that these AI systems continuously learn from user feedback to refine retrieval and ranking. If users frequently click a particular source or indicate an answer was useful, the system can weight those sources more highly in the future. Conversely, if an answer is found to be incorrect, developers adjust the retrieval algorithms or add that case to training data to avoid repeating the mistake.

Bottom line, AI search works through a synergy of classic search engine techniques and advanced AI: using search engines to find and rank content, and using LLMs to read, reason, and write a summarized answer, complete with citations.

Optimizing Websites for AI-Driven Search Visibility

As AI search tools become prevalent, website owners and content creators are keen to have their content indexed and featured in AI-generated answers. In many ways this is an extension of traditional SEO (Search Engine Optimization) with some new considerations. Here are several strategies to optimize your website for AI systems:

Allow AI Crawlers to Index Your Site

Ensure your site’s robots.txt and meta tags do not block AI-focused crawlers. For example, OpenAI’s GPTBot is a crawler that collects web data for training models like ChatGPT. If you want your content to inform these AI, you should allow GPTBot to access your pages. OpenAI has stated that “allowing GPTBot to access your site can help AI models become more accurate and improve their general capabilities and safety”.

In August 2023, many major news sites chose to disallow GPTBot (over copyright concerns), but if your goal is to be included in AI training data, make sure you’re not among those blocking it. The same goes for other crawlers: don’t forbid Common Crawl’s bot (used by many researchers) and allow search engine bots (Googlebot, Bingbot), since most AI search tools rely on those indexes. In short, keeping your content accessible to crawlers is step one for AI visibility.

Maintain Strong Traditional SEO

AI search results still heavily depend on the underlying index of traditional search engines. Bing Chat and Perplexity pull from Bing/Google results, and Google’s Bard/SGE obviously pull from Google’s index. So the old advice stands: use descriptive page titles, relevant keywords, and clear headings so that your content ranks well for its topic.

If your site ranks on the first page of Google for a given query, it’s far more likely to be used by an AI summarizer answering that query. Likewise, a high Bing ranking increases chances of being cited by Bing Chat. Continue to invest in quality content and standard SEO best practices (fast load times, mobile friendliness, proper meta descriptions) to ensure search engines recognize your pages. The AI cannot summarize or cite what it doesn’t find in the first place.

Provide Clear Answers and Structured Data

Many AI tools look for concise passages that directly answer questions. Structuring your content to address common questions can make it more “AI-friendly.” For instance, consider adding an FAQ section or using headings that are phrased as questions (H2/H3 tags like “How does XYZ work?”). If a user asks a similar question, the AI is more likely to spot that your page contains a direct answer.

Using structured data (schema markup) for Q&A, how-to instructions, definitions, etc., can also help your content stand out to search engines and any AI that parses the HTML. While current AI bots primarily read raw text, structured data aids the search engine in understanding your content, which can indirectly influence what the AI presents. In essence, write with clarity and organize information in a logical, question-and-answer format where appropriate – this makes it easier for an AI to extract key points from your site to use in its response.

Demonstrate Authority and Trustworthiness

AI systems aim to deliver accurate information and often favor reputable sources. Content creators should strive for E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) – a term from Google’s guidelines that is becoming relevant to AI as well. If your site is known for high-quality, well-sourced content, AI models (which have read a lot of the internet during training) may inherently treat your content as more reliable.

Moreover, some AI search implementations explicitly boost signals of authority – for example, OpenAI’s agent was described as pulling from “high-quality sources” like mainstream news and official publications. To benefit from this, ensure your content is factual, up-to-date, and preferably backed by references or data. If you have credentials (say you’re a medical professional writing about health), mention that on the page.

Over time, as AI models learn from user interactions, content from sites that users trust (or that are frequently cited by other sources) will likely be favored. It’s also wise to monitor your content for accuracy – if an AI cites your page and it contains an error, that mistake can spread quickly. By being a dependable source, you increase the likelihood that an AI search tool will pick up and recommend your material.

Keep Content Accessible and Up-to-Date

Make sure that important content on your site isn’t locked behind logins or heavy paywalls (unless your business model requires it), because AI crawlers and even search engine bots might not access it. Content that is freely accessible can be indexed and used by AI. Update your content regularly as well – AI tools love “fresh” information for topics that change over time.

If, for example, you have a page about a technology or a law that’s updated annually, keep those updates coming. An AI like Bard or Bing Chat might favor a more recent source if the question implies timeliness (“as of 2025, what is the status of…”). Indicating the date of last update on your pages can also help establish recency. In AI summaries via SGE or others, we’ve seen that newer info often gets highlighted. By staying current, you increase your chances of being included when an AI searches for the latest on a subject.

Monitor AI Traffic and Mentions

Just as you would track SEO rankings and referral traffic, start watching for traffic from AI sources. For instance, if Bing Chat cites your site, you might see hits from the Bing domain or from an “edge agent.” Similarly, if an AI tool provides a link to your page, some analytics might record that. This feedback can tell you which content of yours is resonating with AI-driven search. Additionally, pay attention to any summaries of your content that AI provides (when you encounter them). If the AI is misinterpreting or truncating your content in a weird way, you might need to adjust how you present that info (maybe the first sentence is unclear, etc.). We are in a new era of “AI SEO,” and staying alert to how these tools use your content will help you adapt.

Therefore, optimizing for AI search is about being visible, relevant, and trustworthy. By allowing your site to be indexed, following good SEO practices, structuring your information well, and providing reliable content, you maximize the likelihood that AI search assistants will include and recommend your website in their answers.

The reward is not just direct traffic (when users click your cited link) but also the intangible benefit of your information influencing countless AI-driven interactions.

As one tech journalist put it, in the age of AI search “the best answer wins” – and by preparing your site for these tools, you put yourself in the best position to be that winning answer.