How to Use AI for SEO Research: A Practical, Implementation Guide

👋 Hey there! Andrey here.

Look, I've been working in marketing for over a decade now. After working at ClickFunnels, a couple top tier media buying agencies for international e-commerce brands, doing marketing consulting for 5 star hotels across the world, and running my own agencies, I've been privileged to help hundreds of clients optimize their funnels and ad campaigns.

I've worked on platforms like Facebook, Instagram, and Google Ads. I've always liked SEO in theory because it theoretically can reduce marketing budgets and leverages buyer intent, as opposed to interrupting people with ads.

But honestly? I never had the time to learn it as deeply as I wanted to until LLMs came along and made it easier to research and implement. Given that I'm also a self taught software developer, it felt like the perfect time to jump in.

Then something clicked for me. I realized that AI could revolutionize marketing research when the Deep Research feature for Perplexity came out and other LLM companies jumped on board as well.

It definitely was a step up from Googling or using ChatGPT or Claude alone. Market research is incredibly important and often the reason that campaigns succeed or fail.

Yet, it's also the most tedious and annoying part. Well prompted Deep Research with citations and links to sources can save hours of time and help you find insights you might have missed otherwise.

💡 Key Insight: Being an AI enthusiast and heavily involved in the AI ecosystem, I noticed that a lot of people were putting out AI slop and expecting it to rank on Google. What they didn't realize is that they were doing more harm than good in the long run in the form of loss of good will with potential customers and being punished by the Google algorithm.

Now, let me share what actually works. I'm currently investing around $500 to $1,000 per month in AI and SEO tools, APIs, and education. This strategy though can be executed with an investment that is significantly less, you'll just need to spend more time doing things manually.

The expected ROI timeline for the following strategy is also around 3 to 6 months, depending on the competitiveness of the niche and the quality of the content we produce. For a deeper analysis of SEO's overall value proposition and ROI in the AI era, read my comprehensive analysis on whether SEO is still worth it in 2025. There are also ways to expedite these results by incorporating an ad strategy, but we'll save that for another post!

• ✅ First movers are going to be the big winners due to lack of competition
• ✅ High quality content on your website trains LLMs on your expertise
• ✅ SEO helps establish authority in your niche and industry

Here's my actual AI SEO research stack that I use daily. No fluff, just what actually gets results.

Claude Code has been a game changer. I discovered it because I stay up to date with the latest AI tools and news through various newsletters, blogs, and social media channels.

• 💻 AI agent that runs through the terminal
• 📁 Access to tools that can read and write files
• 🌐 Fetch webpages and run Python code
• 🔗 Integration with different MCP servers

💎 Pro Tip: The ability to build something once and then reuse it forever has been a game-changer. It feels like having an AI companion that helps with different tasks while keeping track of everything with its internal to-do list feature. You can create subagents and custom commands as well so you don't have to keep repeating yourself.

For keyword intelligence, I chose DataForSEO MCP server because of the affordability and quality of the API.

• 🔍 SERP API: Track keyword rankings and analyze competitors
• 📈 Keyword Data API: Find new opportunities and analyze search volume
• 🔧 On-Page API: Analyze SEO factors and identify improvements
• 🔗 Claude Code Integration: Makes it incredibly powerful

Research primary keyword: [your target keyword]

• 📊 Analyze keyword difficulty and search volume
• 🎯 Identify long-tail opportunities
• 🗺️ Map semantic keyword clusters
• 🔍 Check SERP features and competition

• 📝 Content structure patterns and depth
• 👥 Expert citations and authority signals
• ⚙️ Technical implementation details
• 🎯 Content gaps and missed opportunities

• 🏆 Personal experiences that establish credibility
• 📖 Specific stories demonstrating expertise
• 💡 Authentic details competitors are missing
• 🤝 Moments that build genuine audience rapport

Upload 3-5 best-performing content pieces:

• 📈 Highest-engagement blog posts
• 📧 Email sequences that converted well
• 📱 Social media posts that resonated
• 💬 Content where people said "This sounds like you!"

Here's where most content creators make a critical mistake: they publish without testing.

Even though Google claims it doesn't penalize AI content directly, they're definitely scrutinizing it more carefully.

I use the Winston AI MCP server to test content authenticity:

⚠️ Critical Point: Google absolutely hates duplicate content. With so much AI content being generated, accidental similarities are inevitable. I run every piece through plagiarism detection APIs to ensure uniqueness.