Retrieval-Augmented Generation (RAG) is transforming how enterprise teams interact with SEO data. Instead of writing SQL queries or navigating dashboards, teams can ask questions in plain English.
The Problem
Enterprise SEO generates massive amounts of data: crawl logs, ranking data, content performance, technical audits. But most teams can't access this data without technical skills.
Our RAG Architecture
We built a RAG engine using vector embeddings and cosine similarity to index our entire SEO knowledge base. The system uses entity recognition to understand queries like "What pages lost traffic last month?" and retrieves relevant data from BigQuery, Botify, and internal dashboards.
Internal Linking at Scale
The same RAG architecture powers our internal linking recommendation engine. By vectorizing page content and computing similarity scores, the system suggests contextually relevant internal links — replacing manual linking audits that took weeks.
Results
Teams now query organic performance data in natural language. What previously required a data analyst and 2-day turnaround now gets answered in seconds with higher accuracy.