Topic Clustering with AI at Scale

Topic clustering at enterprise scale requires more than keyword grouping — it needs semantic understanding of content relationships.

Beyond Keyword Grouping

Traditional topic clustering groups keywords by similarity. Our approach uses vector embeddings to understand semantic relationships between pages, queries, and user intents.

The NLP Pipeline

1. 1.Content vectorization — Every page converted to embeddings using transformer models
2. 2.Similarity computation — Cosine similarity between all content pairs
3. 3.Cluster formation — Hierarchical clustering with dynamic threshold optimization
4. 4.Gap detection — Identifying missing content within each cluster

Scale Challenges

Processing millions of pages creates computational challenges. We use BigQuery for distributed processing and optimized embedding generation to handle 50M+ page inventories.

Content Strategy Output

The clustering system generates actionable outputs: pillar page recommendations, supporting content briefs, internal linking maps, and content consolidation candidates. Each cluster represents a topical authority opportunity.