How to Use ChatGPT Plugins for SEO Research in 2025

Key takeaways

Plugins were deprecated in 2024; the equivalent functionality now lives in GPTs (custom assistants with Actions) and ChatGPT's built-in browsing.
ChatGPT is good at synthesizing and clustering research you feed it, but it's unreliable at pulling live ranking or volume data on its own — pair it with an actual data source.
The highest-leverage use isn't keyword lists, it's using ChatGPT to reverse-engineer what an AI answer engine would need to see before citing a page on a topic.

What people actually mean by "ChatGPT plugins for SEO"

In early 2023, OpenAI shipped a plugin ecosystem that let ChatGPT call external tools — things like WebPilot for browsing, or SEO-specific plugins that pulled keyword volume and SERP data mid-conversation. That store is dead. OpenAI's own documentation now routes everything through GPTs, which use "Actions" (an OpenAPI-based connector) to hit external APIs, plus native web browsing that's on by default in ChatGPT Plus and Team.

Functionally, the difference for an SEO workflow is small. You still connect ChatGPT to outside data and ask it to reason over that data. What changed is you're now either using a pre-built GPT from the GPT store, building your own with Actions wired to an SEO API (Ahrefs, DataForSEO, SerpApi), or leaning on ChatGPT's native browsing to pull current search results into the conversation. If a guide tells you to "install the SEMrush plugin," that guide is stale — the same capability exists, just relabeled.

The four research tasks ChatGPT is genuinely good at

Not everything labeled "SEO research" benefits from an LLM. ChatGPT adds real value in four specific tasks:

Keyword clustering by intent — feed it 200 raw keywords from a real tool and it will group them into topic clusters and label search intent (informational, commercial, navigational) faster than doing it by hand.
Content gap synthesis — paste in 3-5 competitor URLs' headings and it will tell you what subtopics they all cover and where the gap is, which is useful before you write anything.
SERP pattern reading — with browsing enabled, it can describe what format currently ranks (listicle vs. comparison table vs. long-form guide) for a query, which tells you what Google currently rewards for that intent.
Question mining — it's excellent at generating the real long-tail questions a buyer would ask around a keyword, which matters more for GEO than classic SEO because AI answer engines cite content that directly answers a specific question.

What it's bad at: giving you an actual search volume or keyword difficulty number. It will confidently produce a plausible-looking figure with no data behind it. If you've ever gotten a suspiciously round "1,900 monthly searches" out of ChatGPT with no tool attached, that number was hallucinated, not retrieved. Always cross-check volume claims against a real keyword tool before using them in a deck or a content calendar — this is one of the most common ways indie hackers get burned, and it's avoidable if you know which of the four tasks above ChatGPT is actually reliable for versus just fluent-sounding.

A step-by-step research workflow that actually holds up

Pull raw keyword data from a real source first. Use Google Search Console, Ahrefs, or a free tool like Google's Keyword Planner to get an actual list with volume and difficulty attached. Never start with ChatGPT as the data source.
Paste the raw list into ChatGPT and ask for intent-based clustering. Prompt it to group by buyer stage, not just by topic similarity — commercial intent and informational intent keywords need different page types.
Ask ChatGPT to identify low-competition, high-intent clusters within that list based on the difficulty scores you provided. This is where a lot of founders find pockets nobody's targeting yet; if you want a deeper method for this specific step, see how to find low-competition commercial keywords for SaaS.
Turn on browsing and have it read the top 5 ranking pages for your target keyword. Ask specifically: what does each page answer in its first 100 words, what's missing, and what format is it using. This tells you the bar you need to clear, not just the topic you need to cover.
Draft an outline, not a full article, in this session. Feeding ChatGPT a full "write the article" prompt at this stage skips the research value entirely and produces generic output. Save full drafting for after you've locked structure.
Slot the outline into your existing content plan rather than treating it as a one-off. If you don't already have a system for sequencing these, a content calendar built for indie teams keeps this from becoming a pile of orphaned research docs nobody writes from.

Where this connects to GEO, not just classic SEO

The reason keyword research changed in the last two years isn't just that Google added AI Overviews — it's that a meaningful chunk of research now needs to answer "would an AI answer engine quote this," which is a different question than "would this rank on page one." Traditional SEO research optimizes for what a crawler indexes and a ranking algorithm scores. GEO research optimizes for what a retrieval system pulls into context when a model generates an answer.

Practically, this means your ChatGPT research session should include a step classic SEO workflows skip: asking the model directly, "if someone asked you this question right now, what would you look for in a source before citing it?" The answers are usually blunt — a clear definition near the top, a specific number or step, and content that isn't buried behind three paragraphs of preamble. That list maps almost exactly to what actually gets cited in practice. We test this constantly at Seolyn because it's the whole premise of the product, and the pattern holds: pages that front-load a direct, quotable answer get pulled into AI-generated responses far more often than pages that build up to the point. If you want to verify whether any of your own pages are being picked up this way, there's a concrete method in how to test if ChatGPT cites your website.

What actually breaks when founders automate this end-to-end

The failure mode we see most often isn't bad prompts — it's skipping step 1 above and letting ChatGPT invent the keyword list from scratch with no real data behind it. The model has strong priors about what people search for, and those priors are frequently three years stale or shaped by whatever training data was most common, not what your specific niche is actually searching now. A SaaS founder targeting a narrow B2B tool category will get keyword suggestions optimized for the general case, not their case, unless real data anchors the session.

The second failure mode is treating the research output as the final content. A clustered keyword list and a SERP gap analysis are inputs to an outline, not a finished brief. Founders without a content team often collapse these steps to save time, and what comes out reads exactly like what it is — competent, structurally sound, and indistinguishable from every other AI-assisted article covering the same keyword. If your structure is thin, no amount of research fixes that; pairing solid research with a deliberate page architecture matters more than either alone, which is covered in more depth in how pillar pages should be structured for AI search engines.

Tools worth knowing beyond ChatGPT itself

Native browsing (ChatGPT Plus/Team): sufficient for SERP pattern reading and competitor content review; no setup required.
Custom GPTs with Actions: worth building only if you're doing this weekly and want a repeatable connector to a specific data API rather than copy-pasting exports each time.
A real keyword data source: Google Search Console (free, first-party data on what already brings you traffic), Google Keyword Planner (free, volume estimates), or a paid tool if budget allows. Google's Search Central documentation is the most reliable free reference for how Google itself defines and measures these signals.

None of these replace the judgment step — deciding which cluster is worth writing for, given your actual product and audience, is still a human call.

Frequently Asked Questions

Q: Do ChatGPT plugins for SEO still exist in 2025?

No. OpenAI shut down the original plugin store in 2024. The same functionality now runs through GPTs with Actions, or through ChatGPT's native web browsing feature.

Q: Can ChatGPT give me accurate search volume numbers?

Not reliably. ChatGPT will produce plausible-sounding volume figures without real data behind them unless it's connected to an actual keyword API through Actions or you supply the numbers yourself from a tool like Search Console or Ahrefs.

Q: What's the single best use of ChatGPT in an SEO research workflow?

Clustering a real keyword list by search intent and identifying content gaps against competitor pages — both are synthesis tasks the model handles well when you give it real source data to work from.

Q: Is ChatGPT-based research different from optimizing for AI answer engines (GEO)?

Yes, though they overlap. Classic SEO research targets what ranks in Google's index; GEO research targets what gets pulled into an AI-generated answer, which favors direct, quotable answers over content built to satisfy crawlers.

Q: Do I need a paid ChatGPT plan to do this kind of research?

You need a plan that includes web browsing, which is available on ChatGPT Plus and Team tiers, to get current SERP and competitor data rather than relying on the model's training knowledge alone.