Deep Research for SEO: How to Use AI for Advanced SEO Insights

New AI tools and modes are dropping almost every week, with each new release quickly fading into the digital background. But every now and then, something cuts through the static. Something that doesn’t just tweak the way we work, but transforms it.

ChatGPT’s new Deep Research mode is one of those rare standouts. It’s built for automating complex research, finding insights at scale, and simplifying the kind of analysis that usually eats up your day. Sure, it’s not a full replacement for your go-to SEO tools or hands-on analysis, but it works great alongside them (helping you move quicker without sacrificing depth).

This article covers the areas in SEO where Deep Research can make a real difference. We’ll also explain how to start putting it to work.

What is ChatGPT’s Deep Research feature?

Deep Research is an advanced agentic capability introduced by OpenAI in early 2025. It functions as an AI research assistant that can autonomously conduct multi-step research on the web and synthesize findings into detailed reports.

In practical terms, Deep Research goes beyond the ChatGPT-4o experience. Instead of answering with just what it “knows” or a quick web result, it performs an in-depth investigation across numerous sources, then delivers a comprehensive, well-structured response (often with references). 

For professionals using AI for SEO, this means they can delegate complex research tasks (like competitor audits and topical research) to ChatGPT, then receive detailed, analyst-style insights.

How does Deep Research differ from other AI tools?

We’ll jump into how Deep Research helps with SEO tasks soon, but first, let’s look at how Deep Research compares with other AI research tools, specifically DeepSeek and xAI’s Grok.

Each tool has its strengths, but Deep Research stands out with its structured outputs, real-time data depth, and the ability to tackle complex SEO tasks like competitor analysis and topical research.

Now, let’s shift focus to analyzing ChatGPT’s specific modes in detail.

Since ChatGPT now comes in various “modes” or agents, it’s important to clarify what makes Deep Research unique. The key differences between Deep Research and other ChatGPT models (like standard ChatGPT, the ChatGPT Operator, or SearchGPT) boil down to autonomy, depth, and use-case focus. 

Below is an SEO use-case comparison between Deep Research, OpenAI’s standard ChatGPT, and Operator (an agent that can search the web to perform trivial tasks):

See the articles below for a closer look at ChatGPT’s modes:

Overall, we found that Deep Research is far more self-sufficient and thorough in SEO research tasks than ChatGPT’s other modes. 

How to use ChatGPT’s Deep Research for SEO

Let’s now explore some Deep Research use cases and how to get the most out of this tool in each.

Competitor analysis

Traditionally, you would use multiple tools for competitor analysis – one for traffic estimates, one for backlinks, one for content analysis. Then you would manually compile the findings. 

Deep Research is useful in that you can feed it a detailed prompt, list the competitors and all other aspects you care about, and then let it do the heavy lifting.

Here’s how:

1. Select the competitors you want to analyze, plus your website (preferably, 2-3 at a time to achieve the most comprehensive insights on each).

2. Define the SEO factors to compare: This could be domain authority/backlinks, top keywords/traffic, content marketing strategy, on-page SEO, and technical factors like site speed or mobile friendliness.

3. Craft a detailed prompt. It should be clear, specific, and structured to help guide ChatGPT’s Deep Research functionality. As an example, this is the prompt we used for our testing:

We made sure we were crystal clear about what we wanted in the prompt (competitor analysis) and why (to improve our site). The prompt was also very specific, detailing exact metrics (e.g., DA, PA, top keywords) and distinct competitors.

We also organized the prompt into distinct sections (like SEO performance, keyword strategy, backlink profile, and so on), each with specific sub-tasks.

Is it long? For sure. But keep in mind, longer and more specific prompts may lead to higher-quality responses.

4. Expect 5–30 minutes for Deep Research processes to finish processing the request and finalizing its output. It will do so in the background.

5. Check the report for insights and takeaways. Some of the insights you can expect from such a Deep Research output include:

But even though Deep Research does a good job delivering practical insights, you’ll still need to validate them with reliable tools like SE Ranking’s Competitor Research tool. This is the best way to avoid skewed results and make decisions based on accurate, up-to-date information.

Topical research

Identifying relevant, high-performing topics and keyword opportunities is another area where ChatGPT Deep Research shines. 

Instead of spending hours combing through forums, blogs, and keyword tools to figure out “what’s trending” or “what should I write about next,” you can ask Deep Research to do a deep dive on industry trends or emerging topics in your field. Here’s how:

1. Start with a clear, detailed prompt. To get a comprehensive overview, ask for a structured breakdown and explicitly request insights from recent industry analyses, expert commentary, authoritative studies, and credible forecasts:

2. If you want the response to dive into specific areas, like technology, trends, or practical applications, break down the prompt into sub-questions. For instance:

3. To make the response actionable (like identifying content ideas or best practices), include a request for examples or suggestions. For instance:

“Based on your analysis of GEO trends in 2025, suggest 3-5 blog topic ideas that would likely perform well in AI-driven search, and explain why each is a good fit.”

We used this approach to get Deep Research to respond with insights on key technological developments, emerging trends, business implications, expert opinions, and forecasts.

After analyzing Deep Research’s response, you can extract whatever is relevant to your content strategy.

For instance, if the report indicates that “how-to” content with structured steps performs well with generative search, you might prioritize creating more how-to guides and marking them up with HowTo schema. Or if it suggests certain new keyword opportunities (perhaps people asking very specific, long-tail keywords for AI searches), you can target those.

To see how those opportunities play out in Google search results, use SE Ranking’s AI Overviews Tracker. It lets you monitor the keywords appearing in AI Overviews, how their presence evolves, and the sources featured in AI snippets. Deep Research and SE Ranking are a great tool combo if you’re trying to get the complete picture, from research to real-world performance.

Combined internal linking and backlink insights

ChatGPT’s Deep Research can also assist by analyzing your internal link structure and backlink profile together, uncovering link-building opportunities you might overlook when reviewing them apart.

1. Collect & organize data. Use a crawler (e.g., Screaming Frog) to export a list of pages with their internal inlink counts. Separately, run a backlink check using tools like SE Ranking’s Backlink Checker to collect a list of pages from your site with external backlink data.

2. Clean and anonymize the data. Strip out unnecessary columns (e.g., crawl depth, domain trust score) to keep the input lean—focus only on URLs and link counts. Replace sensitive URLs with generic labels (e.g., “Homepage” or “Blog Post X”) if privacy is a concern. This also keeps the prompt size manageable.

3. Craft a clear, specific prompt and attach files with data collected on internal and external links. Here’s how our prompt looked:

Here’s what Deep Research was able to do with our data:

Although its results are general and obvious, with broad recommendations and little specificity, OpenAI is still heading in the right direction. 

The biggest issue with these kinds of analyses is that ChatGPT often refuses to generate result files for complex analyses (even after several requests). When it does, it usually includes only the first 10–15 rows (at least, in our case). 

Backlink status monitoring

Keeping track of backlink health (i.e., whether backlinks are still live, have become no-follow, or got removed) is an ongoing chore for SEO pros and agencies. 

Typically, one might use a backlink monitoring tool for this, but there are times you want a quick custom check (for example, after a big link-building campaign, or when inheriting a new client’s backlink list).

ChatGPT’s Deep Research can expedite the backlink verification process by automatically checking each URL to confirm your link’s presence. We tested this by asking Deep Research to go through a list of URLs (a CSV of backlinks) and mark each as “Exists” or “Not Exists” depending on whether the page still contains a link to our site.

Here is the process we followed and the outcome:

1. Prepare the backlink list: Collect and export the CSV file with backlink data using dedicated tools like SE Ranking’s Backlink Monitoring Tool.

2. Ensure the file has a column for Referring page URL and Status (exist, not exist) – this is the column the agent will fill for you.

3. Clearly prompt deep research. This is how we structured our prompt:

4. Wait for a deep research execution. The agent will then visit each URL one by one and scan the content of each page for mentions of our website or any hyperlink pointing to it. This is what an SEO would do manually: open the link, Ctrl+F for the domain, and mark yes/no.

During our trial, we started by submitting nearly 1,000 URLs in one go—but that overloaded the tool, and the process didn’t complete. We resolved this by splitting the list into smaller chunks of about 100 URLs each. Deep Research managed these batches much more efficiently, returning the status for every URL in the group.

The speed was impressive: what could have taken a human a couple of hours (opening 100 links and checking) took the AI a fraction of that. Moreover, we didn’t have to babysit it. Deep Research can work through the list autonomously once prompted.

For agencies, this use of Deep Research can be a huge time-saver in reporting. You can integrate a backlink check into your monthly SEO reports: just run the client’s backlink list through ChatGPT’s Deep Research and quickly flag the ones that disappeared. 

Even if you use an automated tool, the AI can provide a nice plain-English summary of “X% of backlinks are active, Y were found removed this month, mostly from Site A and Site B.” You can then focus on how to handle those lost links.

Note: The AI’s output was typically pretty accurate, but we did find a few discrepancies. For instance, one URL was marked “Exists” but when we visited, the link had been removed (maybe the AI found a mention of our website in text but not an actual hyperlink). Another was marked “Not Exists” but the link was there (perhaps the page had a lot of dynamic content or required a login that the AI couldn’t fully see). 

This highlights an important point: while Deep Research is great for automating checks, it’s not infallible. Web pages with complex scripts, or partial loads, might fool it. So, for critical backlinks (your top 50, for example), you may still want to verify them manually or use a specialized tool to double-check. But it works well for broad sweeps to catch obvious dropped links.

Ongoing tasks: keyword research, technical SEO checks, content strategy

Beyond tackling big SEO tasks, ChatGPT’s Deep Research simplifies routine SEO research tasks. It quickly handles the routine questions and lookups SEOs face daily.

Below are some examples of tasks Deep Research can assist with:

1. Keyword Research & Trends: We already touched on how Deep Research works for trending topics, but it’s also invaluable for direct keyword research strategies.

For example, you can ask it to list the “top trending finance-related keywords globally for January 2025, including their monthly search volumes, keyword difficulty, and key industries or topics associated with each.” It will attempt to fetch data from news sites, Google Trends results, and SEO blogs. 

It returned a keyword list in one run, with options like “Bitcoin price”, “mortgage rates”, “dow futures,” each with an estimated search volume and keyword difficulty. 

This kind of output, if verified, can save time compared to manually gathering info. It’s like getting a mini-report of “what’s popular right now” with context. 

Of course, for absolute accuracy on monthly searches and keyword difficulty scores, you’d still need to use dedicated keyword research tools like Google Keyword Planner or SE Ranking’s Keyword Suggestion Tool (since ChatGPT’s data sources are secondary). But it works fantastically as a quick brainstorming tool.

2. Technical SEO Checks: While Deep Research can’t run a full technical audit (it’s not going to crawl your site), it can analyze outputs from dedicated tools. For instance, if you have a CSV export from a crawl, you can paste it and ask Deep Research to focus on key issues. 

In one test, we gave it a summary of PageSpeed metrics for multiple pages. We asked for recommendations in the following way:

“I have analyzed the performance of my website pages using the PageSpeed API and Screaming Frog. The final export is attached. Please review the data and provide the results in the ‘Verdict’ column in CSV format, similar to the one I provided, but with your recommendations included.”

Deep Research went through and added a “Verdict” for each page, like “Compress/Lazy-load images, optimize LCP; Minify/Defer JS, reduce long tasks for FID; Reduce CLS: Set image/video dimensions” depending on the data.

It’s a great way to turn raw data into an interpretable to-do list.

3. Content Briefs & Outlines: Start by clearly stating what you want from your content brief or outline. Don’t forget that the quality of the output depends on your input. Be specific and structured. Here’s an example:

“Create a high-quality content brief for a blog post titled ‘How AI Tools Boost Customer Service Efficiency.’ Include a summary of current trends, key benefits backed by data, real-world examples, and an outline with H1, H2, and H3 headings. Search the web for recent articles, statistics, and case studies from 2024-2025.”

Deep Research will likely ask follow-up questions (e.g., “What industries should I focus on?” or “Do you want case studies included?”). Answer these to refine the scope.

The report it returns will be detailed, sometimes 10+ pages. Extract the essentials for your brief:

AI can occasionally misinterpret data or “hallucinate” details. Verify stats and examples with source material. Add your voice or brand-specific insights to make it unique.

The bottom line?

Deep Research gives you the foundation, not the final draft.

Check out this comprehensive guide for a direct comparison of ChatGPT’s content generation capabilities with Jasper AI and SE Ranking’s AI Writer.

Top tips for making the most of Deep Research 

Here is how to effectively use ChatGPT’s Deep Research for SEO. Follow the best practices below to get accurate, useful outputs:

Conclusion

ChatGPT’s Deep Research feature marks a major step forward for SEO professionals using AI. It turns time-consuming tasks, like competitor audits, content research, trend analysis, and technical checks, into a smooth, automated process, delivering deep insights in far less time. 

By blending AI’s power with your own SEO skills, you can drive smarter, data-backed results.

Written by

Yulia Deda

Yulia is an SEO and Content Marketing Expert at SE Ranking passionate about all things SEO, with a particular focus on analytics & reporting, technical SEO, and keyword research. She is fond of expressing complicated and sometimes confusing concepts succinctly and clearly. Whenever Yulia has free time, she is likely to draw, watch documentaries, immers herself in a captivating book, or plan a new trip.