AI-Driven SEO Workflow — Fully Automated Content Generation and Publishing System

Technology Stack

Component	Technology	Purpose
AI Integration	OpenAI GPT-4 + Claude	Content generation and optimization
Workflow Automation	n8n	Visual workflow orchestration and automation
Web Scraping	Apify	Automated data collection and web crawling
Data Management	Airtable	Structured data organization and collaboration
SEO Analysis	Ahrefs API	Keyword research and competitor analysis
Content CMS	Firebase Firestore Database	Content storage and management
Development IDE	Firebase Studio	Database management and debugging
Protocol	Model Context Protocol (MCP) + JSON-RPC	AI agent tool communication
Runtime	Node.js	JavaScript server runtime environment
Server Framework	Express	Web application framework and API routing
Frontend Framework	Next.js 15 + TypeScript	Frontend application framework
UI Components	shadcn/ui + Tailwind CSS	Design system and styling
Testing Framework	Vitest	Unit testing and test automation
Authentication	Firebase Auth	User management and security
Backend Services	Google Cloud Functions	Serverless function execution
File Storage	Firebase Storage	Asset and media management
Hosting	Firebase App Hosting	Application deployment and CDN
Image Generation	DALL-E 3	Automated visual content creation
Data Sources	Custom APIs	Sportsbook data and market research
Build Pipeline	GitHub Actions	Automated deployment and testing

Table of Contents

1. Framing the Problem
2. Designing the Architecture
3. The Complete Workflow
4. How Each Component Worked
5. Design Principles
6. Implementation & Outcome
7. Key Technical Innovations

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As a company operating in the sports-betting SEO space, our challenge changed over time. Search intent shifted from broad keywords like "best sportsbook" to very specific long-tail queries like "best crypto sportsbooks in New York." That meant we suddenly needed hundreds of highly optimized, state-specific, and category-specific pages — sports, casino, poker, crypto — all following the same structure but tailored to different contexts.

At the same time, our biggest competitors were using black-hat SEO tactics: spinning up disposable sites that ranked fast and died fast. We wanted the speed and scale of that approach, but built on precision, automation, and factual accuracy. The question I had to solve was:

Framing the Problem

Traditional SEO workflows were too slow and too human-heavy. Each page required manual writing, research, visual design, and data insertion (tables, charts, sportsbook bonuses, etc.). It could take hours for one person to build one page — totally unscalable.

What I needed was an end-to-end automated workflow that could:

1. Generate factually correct, SEO-optimized content at scale
2. Insert real data (like sportsbook bonuses) without hallucination
3. Include visual and interactive components — tables, callouts, infographics — so the pages didn't look like AI-generated walls of text
4. Publish directly to WordPress with zero manual intervention

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Designing the Architecture

I built the entire system from scratch using n8n, Firebase, TypeScript, and WordPress — all connected through a custom MCP (Model Context Protocol) Server I developed myself.

There were 4 main pieces of the system:

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The Complete Workflow

The process started when the SEO team created an outline in the Publisher Admin. Let's say "Best Sportsbook Apps" with parameters like state=Texas, year=2025. This kicked off the entire n8n workflow:

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How Each Component Worked

1. n8n Workflows – The Automation Engine

The core orchestration layer that managed the entire content creation pipeline:

• Executes Google searches for competitor analysis and keyword extraction
• Converts Markdown outlines to structured JSON with shortcode requirements
• Generates targeted research questions for each content section
• Coordinates AI writing with MCP server data resolution
• Handles the final upload and publishing to WordPress

2. Advision Publisher – The Admin Interface

Where SEO teams created and managed content production:

• Outline Creation: SEO specialists wrote content outlines in Markdown
• Parameter Configuration: Variables like state, year, or specific requirements
• Tone & Voice Settings: Defined writing style and do’s/don’ts
• Verification Controls: Choose manual or automatic research verification

3. Research & Writing Process

• Google API Integration: Pulls top 10 results for keyword extraction
• Perplexity Research: Gathers factual insights per section
• Section-by-Section Writing: Keeps article length balanced
• Human Verification: Optional manual review before writing

4. MCP Server Integration

During writing, the AI connects to the MCP server to resolve:

• Dataset Tokens: e.g. sportsbook bonuses, launch dates, legal data
• Shortcodes: Dynamic elements like [offer_table]
• Structured Data: Ensures factual accuracy without hallucination

5. WordPress Plugin & Rendering

The final layer that transforms tokens into visuals:

• Local Data Sync: Shortcodes and tokens cached locally
• Dynamic Rendering: Converts tokens into tables, callouts, visuals
• Global Updates: Any data change propagates instantly

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Design Principles

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Implementation & Outcome

I built everything — from the n8n workflow logic to the MCP server, WordPress plugin, and Firebase admin — myself, end to end, in just two months. It was a complete zero-to-one build with no prior framework to rely on.

Today, it runs in production as the backbone of our content automation system. The system doesn't just write — it thinks, verifies, and structures every page for both readers and search engines. It's the most advanced automation project I've ever built, and the first one where AI became a true production-grade collaborator.

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Key Technical Innovations

1. Model Context Protocol Integration — AI queries verified datasets, not hallucinations
2. Human-in-the-Loop Verification — Balances accuracy with automation
3. Shortcode Architecture — Globally updatable dynamic components
4. Local Data Resolution — Zero remote API calls at render time

This project proved that AI-driven automation doesn’t have to trade accuracy for speed.
With the right architecture, you can achieve both.