Yesterday I sent out a survey and about 70% of you said you’re interested in the course. That's amazing! Thank you!
While I’m still figuring out the best way to deliver it, I wanted to share something that I know will be genuinely useful for anyone in SEO who’s thinking about building their own tools.
If you’ve ever tried to automate a tedious SEO task, analyze data at scale, or just wished you had a custom solution for your workflow, you know how overwhelming it can feel to get started.
Before you even touch a line of code, there are a handful of high-level concepts that make all the difference between a tool that’s just “meh” and one that’s actually valuable for your day-to-day work.
Here’s a breakdown of the core ideas I think every SEO pro should know if they want to build something truly useful. I hope this helps you get a head start, whether you’re planning to join the course or just want to experiment on your own.
🕵️♂️ Problem Solving & Tool-Building Mindset
Spotting real SEO problems worth solving—think: repetitive reporting, data wrangling, or scaling audits
Breaking big challenges into manageable, actionable steps
Designing solutions that actually fit your workflow and make your day easier
Checking if your idea is actually feasible to build—can you get the data, is there an API, do you have the right resources?
🧩 Product Thinking
Getting clear on what your team or clients really need (not just what’s trendy)
Defining your Minimum Viable Product (MVP) so you can launch fast and iterate
Using feedback from real users to improve your tool and make it indispensable
🎨 User Experience (UX) & Usability
Designing intuitive interfaces and workflows so your tools are easy and enjoyable to use
Gathering user feedback to improve usability and adoption
🔒 Security & Privacy (Basics)
Understanding the importance of protecting site and user data
Making sure your tools handle sensitive information responsibly and comply with privacy standards
🔗 APIs & Data Integration
Knowing how to pull data from sources like Google Search Console, DataForSEO, or custom APIs
Making API requests (REST API) to automate data collection and reporting
Parsing and using API responses to power your dashboards, audits, or keyword research
🤖 AI & Language Models
Basics of GPT and Large Language Models (LLMs) for content generation, content analysis, clustering, or automating repetitive writing tasks
Integrating AI into your tools to generate insights, summaries, or even content drafts
🧠 Vector Search & Embeddings
Understanding what is vector embeddings, embedding models, and how to use embeddings for smarter keyword grouping, content clustering, or semantic search
Using vector databases to organize and retrieve large sets of crawl data or other data sets efficiently
⚙️ Automation & Scripting
Automating repetitive SEO tasks (like crawling, reporting, or link analysis)
Writing scripts in Python, JavaScript, etc., to save hours on manual work
🖥️ Frontend Basics
Building user interfaces for your tools—think: custom dashboards, data explorers, or quick audit checkers
Using HTML, CSS, JavaScript, or frameworks like React to make your tools accessible and easy to use
🗄️ Backend Basics
Setting up servers (Node.js, Python, etc.) to handle data processing, API calls, or scheduled jobs
Managing requests, responses, and connecting to your data sources
🧰 Frameworks
Using frameworks like Flask, FastAPI (Python), or Express (Node.js) to speed up backend development
Frameworks provide ready-to-use tools and structure, making it easier to build, scale, and maintain your SEO tools
💾 Database
Storing, retrieving, and managing SEO data (rankings, keywords, crawl results, etc.)
Introduction to SQL and NoSQL databases for flexible data storage
Using cloud databases (MongoDB, Supabase, etc.) to keep your tools scalable and reliable
🚀 Deployment & Operations
Deploying your SEO tools and dashboards online (Vercel, Heroku, etc.) so your team or clients can use them
Monitoring and maintaining your tools to ensure they keep running smoothly
🗂️ Version Control
Using Git (and platforms like GitHub or GitLab) to manage code changes, collaborate, and track progress
📝 Documentation
Writing clear documentation for your tools so others (and your future self) can use and maintain them
Including usage instructions, setup guides, and API docs
📊 Analytics & Monitoring
Adding analytics to your tools to track usage and performance
Setting up error logging and monitoring for live tools
Why does this matter?
If you want to build tools that actually solve problems—and maybe even launch your own AI-powered product—these are the foundational concepts that will set you up for success.
I’m working on a course that will guide you through all of this, step by step, with hands-on projects and real-world examples.
What concept are you most excited to learn about? Hit reply and let me know!
Mihir
“P.S. If you have a tool idea you’re stuck on, reply and tell me about it—I might feature it in the course!”
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