AI & Machine Learning for Search
Machine learning is fundamentally changing how we approach search engine optimization. From RAG-powered internal linking engines to knowledge graphs that classify millions of queries, AI/ML enables SEO strategies that were impossible with manual execution.
Retrieval-Augmented Generation (RAG)
Built RAG-based internal linking recommendation engines using vector embeddings, cosine similarity, and entity recognition. The system queries organic performance data in natural language, enabling teams to make data-driven decisions without SQL knowledge.
Knowledge Graphs at Scale
Structured 5.3M raw queries into a 5-level taxonomy (Categories → Subcategories → Intents → Topics → Keywords). 913 L1 categories with <1ms classification speed. What would take 2 hours for 1,000 keywords manually, this system processes 5.3M keywords instantly.
Topic Clustering with NLP
Created topic clustering systems using vector embeddings to map content relationships for programmatic SEO recommendations. The NLP pipeline processes millions of pages, identifying content gaps and topical authority opportunities.
LLM-Optimized Content Strategy
AI-powered metadata automation with knowledge graphs, topical analysis, and content guardrails (legal, brand voice). Multi-model LLM testing across GPT-4, Claude, and open-source models for optimal content generation at scale.
Answer Engine Optimization (AEO)
Building for what's next: Answer Engine Optimization, Generative Engine Optimization, AI Overviews. Led AI-powered internal search chatbot development using RAG architecture for AEO — optimizing content for AI-generated answers, not just traditional search results.
Related Articles