The new SEO stack: What replaces your old toolset

Generative AI and automation are reshaping SEO, with 87% of Americans reading AI summaries, pushing professionals to adopt agile, AI-driven tools like LLMs, APIs, and scripts over traditional rank trackers, keyword tools, and site audits to stay competitive.

SEO https://searchengineland.com/library/seo » The new SEO stack: What replaces your old toolset See how LLMs, APIs and scripts cut busywork, uncover AI search signals and help teams move faster without ditching core SEO basics. Generative AI and automation are bringing excitement to some SEO professionals and anxiety to others. With 87% https://searchengineland.com/ai-search-survey-summaries-shopping-461928 of Americans reading AI summaries, you’re falling behind if you’re not adapting your toolset to this trend. Moving from rigid enterprise tools to agile, AI-driven ones positions you as a forward-thinking authority with clients or your employer. This how-to will help you guide clients, employers, or your team through that shift. Here’s what an old SEO stack looks like SEO practices remain relevant because the company’s generative AI features are rooted in: - Core search ranking systems. - Quality systems. Here’s a traditional “SEO stack”: Rank trackers Tracking keywords used to be every campaign’s heartbeat. Add target keywords, monitor SERP positions, and higher rankings would drive more search traffic. But rankings have fragmented over the last few years. SEOs are now tracking: - AI Overviews - Local packs - Shopping carousels - And so much more. A third-place local pack ranking might drive two or three times more traffic than a number one AI Overview ranking. Keyword tools What are people searching for? With a crystal ball, you could optimize for specific queries and target certain groups. Keyword research lets you write content that matches those queries and user intent. You’ll choose keywords based on: - Difficulty - Search volume - Intent - Other factors Dozens of options help you find keywords for campaigns, and some competitors had more access to keyword data than others. Lagging search volume data may have hurt your campaign, but it still showed past performance. For example, you might target a keyword with 10,000 monthly visits. But just because it reached that volume last month doesn’t mean it will perform the same this month. Volume could double or fall to a tenth of last month’s level. The problem in today’s search environment is that a keyword with tens of thousands of clicks in 2022 may now appear in an AI Overview. Zero-click searches may steal your traffic, making some once high-click queries irrelevant or not worth the same investment. Even if search volume hasn’t dropped, the opportunity has. Site audit tools Crawlers still crawl your site and interpret its content. Getting a complete picture of how these crawlers see your website has always been crucial to SEO. Audit tools help you identify: - Broken links - Redirect issues - Missing metadata - Slow pages - Thin content - Other issues on your site But don’t put these audit tools on the shelf just yet. You’ll still need them to know whether your site is technically healthy. Crawl audits don’t guarantee that your content will surface. Factors such as brand mentions are crucial signals for inclusion in LLMs like ChatGPT, Claude, and Gemini. Unfortunately, many site audit tools in your old stack lack mention-tracking functionality. So while you may still rely on your old stack, it’s time to add new tools that cover these signals and change how you operate as an SEO professional. Here’s what a new SEO stack looks like IIf you’re still optimizing only for Google, it’s time to shift gears. Between the first and second half of 2025, LLM referral traffic grew by 80%. Conversion rates reached 18%, but LLM referrals still accounted for 2% or less of total traffic, according to the dataset. Now is the time to shift to a new stack that helps you leverage growing LLM referrals. Add the following to your SEO tech stack to stay ahead of the competition: LLMs You want your site to show up in LLMs, but these same tools can help power your SEO strategy. For example, you might use: ChatGPT : Connect ChatGPT with Google Search Console to automate your SEO analysis, as I show you how to do in a. recent article here Claude : Use Claude to write your copy, refine metadata and conduct a full content audit. Gemini : Hop on Gemini to help generate schema markup, compare competitor sites with your own, or find issues with your site. LLMs can help with everything from data analysis to competitor research. Use the LLM you’re most comfortable with for these tasks, but keep human oversight in place. Use these tools to improve performance, not replace the human element. Large datasets that once took hours, days, or weeks to review now take minutes with these tools. Keep learning LLMs and how to integrate them into your workflow. APIs Old dashboards with CSV exports into Excel were once standard. You logged into Google Search Console GSC and exported data. While it may sound too technical, LLMs can now help you connect to APIs for: - Google Search Console - Google Analytics LLMs can help you authenticate requests and parse JSON. With this skill, you can open up a workflow Lightweight scripts Python scripts are now available to any SEO with some skill and Claude Code, or similar options in ChatGPT or Gemini. You can easily create scripts that: - Pull your top pages from GSC - Compare titles to character limits - Flag 30-day changes - Create a CSV output for you Rather than waiting for vendor tools to add a feature that removes a performance bottleneck, create a script that does the same thing. A hundred-line script can handle much of the work you used to do by hand, without a new license or SaaS upsell. If you hand the script to someone else, they can see the exact logic behind it. Notebooks / local workflows Your SEO team has data in many places: - Shared folders - Google Sheets - Notion docs You might have a three-year content audit tracker in Google Sheets. A spreadsheet with monthly CSV dumps from your favorite tools leaves you with files you must manually open and decipher. Notebooks and local workflows change how data fragmentation slows your team down. Instead, Notebooks interpret these files and turn them into action. For example, a script may pull data, an API surfaces the signal, and LLMs make sense of the data and put the output into your Notebook. Notebooks also offer the benefit of: - Consistent data formats - Shared access to data - Documented logic SEO teams need to be agile and scalable to grow with the new era of search optimization and generative AI. Rather than starting over every time they need to pull data, teams can use local workflows for data consistency. Creating hybrid workflows to mix old and new SEO stacks Is your old SEO stack obsolete? No. Are these new tools the only ones you need? No. Hybrid workflows and search engine optimization stacks offer the best of both worlds. Tool + custom script + AI layer You’ll need to experiment to create a hybrid workflow that works best for your clients, projects, and teams. One hypothetical workflow that combines the old and new stack for well-rounded SEO includes: - Crawling the site with an audit tool, such as Screaming Frog - Running a Python script that dissects the file and joins it with GSC data - Scripts that flag pages where you have a lot of impressions but low clicks - Sending flagged pages to an LLM to evaluate titles against search intent - Putting LLM output into a Notebook or spreadsheet for editors to review - Turning approvals into change logs Tasks like these used to take weeks, so teams put them on the back burner. At the enterprise level, teams quickly felt overwhelmed by this much data. But when you combine old and new SEO stacks, you can complete larger projects in a fraction of the time. Replacing your current SEO stack with one that’s more agile and built for today’s massive datasets will make you an invaluable asset to any SEO team. Topics on this page Artificial intelligence https://searchengineland.com/topic/artificial-intelligence/ Large language model https://searchengineland.com/topic/large-language-model/ Search engine optimization https://searchengineland.com/topic/search-engine-optimization/ AI Overviews https://searchengineland.com/topic/ai-overviews/ Anthropic https://searchengineland.com/topic/anthropic/ Application programming interface https://searchengineland.com/topic/application-programming-interface/ Automation https://searchengineland.com/topic/automation/ C https://searchengineland.com/topic/c/ Chatbot https://searchengineland.com/topic/chatbot/ ChatGPT https://searchengineland.com/topic/chatgpt/ Claude https://searchengineland.com/topic/claude/ Comma-separated values https://searchengineland.com/topic/comma-separated-values/ Computer programming https://searchengineland.com/topic/computer-programming/ Data analysis https://searchengineland.com/topic/data-analysis/ Gemini https://searchengineland.com/topic/bard/ Generative AI https://searchengineland.com/topic/generative-artificial-intelligence/ Google Analytics https://searchengineland.com/topic/google-analytics/ Google Search Console https://searchengineland.com/topic/google-search-console/ Google Sheets https://searchengineland.com/topic/google-sheets/ JSON https://searchengineland.com/topic/json/ Microsoft Excel https://searchengineland.com/topic/microsoft-excel/ OpenAI https://searchengineland.com/topic/openai/ Python https://searchengineland.com/topic/python/ Screaming Frog https://searchengineland.com/topic/screaming-frog/ Screaming Frog SEO Spider https://searchengineland.com/topic/screaming-frog-seo-spider/ Search engine results page https://searchengineland.com/topic/search-engine-results-page/ Contributing authors are invited to create content for Search Engine Land and are chosen for their expertise and contribution to the search community. 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