SEO Blog Article Generator with Quality Loop
Automation & AI
An intelligent n8n automation workflow that generates SEO-optimized blog articles using AI, competitive analysis, and iterative quality refinement. It combines real-time SERP analysis, AI-powered entity extraction, vector-based knowledge retrieval, and a self-improving quality loop to produce articles that are semantically aligned with top-ranking content.
From Keyword to Published Article
A 35-node workflow that handles the entire content creation pipeline with self-improving quality checks.
How It Works
1. Trigger & Input
The workflow is triggered from a Notion database. Simply add a keyword and configure your quality parameters - the system handles the rest.
2. Competitive Intelligence
The system analyzes the current Google search landscape for your target keyword, fetching top-ranking pages and extracting clean content while filtering noise.
3. Entity Extraction & Semantic Analysis
An AI model analyzes the competitive content to identify core entities, supporting concepts, semantic variations, and co-occurrence patterns.
4. Knowledge Base Integration (RAG)
The workflow performs vector similarity search against a proprietary knowledge base, enriching the article with domain-specific context from retrieved knowledge chunks.
5. AI Content Generation
GPT-4o generates an SEO-optimized title, meta description, and a structured article with 5-7 sections and an FAQ, using the competitive insights and RAG context.
6. Quality Scoring & Iterative Improvement
The generated article is converted to a vector embedding and compared to target SERP entities. A gap analysis drives targeted revisions until the quality threshold is met.
7. Publishing
Once quality standards are met, the final score and metadata are updated in Notion, markdown is converted to Notion blocks, and the content is published directly.
Technical Highlights
- 35 interconnected nodes handling data flow, transformations, and API calls
- Multiple AI models for different tasks (entity extraction, content generation, gap analysis)
- Vector embeddings for semantic similarity scoring
- Supabase vector store for RAG-based knowledge retrieval
- Intelligent content cleaning with multi-pass noise filtering
- Error handling & retry logic throughout the pipeline
- Configurable parameters: target score, max iterations, RAG chunk count
Integration Stack
- Notion: Trigger, status tracking, content publishing
- SerpAPI: Real-time Google search analysis
- Jina AI: Web page content extraction
- OpenAI: Entity extraction, content generation, embeddings
- Supabase: Vector similarity search for RAG
Quality Metrics
The workflow tracks and reports key metrics to ensure high performance:
- Semantic Alignment Score
- Iteration Count
- Score History
- Pages Analyzed
- Knowledge Chunks