WordPress-to-Vector-Database Content Scoring: Build AI-Citable Authority

A vector-database system that extracts WordPress content via REST API, converts each page and section into semantic embeddings (numerical representations of meaning), and stores them in Supabase pgvector. The system then scores all your pages against topic clusters, identifying which pages are clear authorities, which cannibalize each other, and which leave coverage gaps.

The core benefit: You build, update, and interlink hub-and-spoke architectures that rank across traditional search, AI platforms, and answer engines—eliminating the costly gaps and duplication that leave your firm invisible to both Google and generative engines.

Unlike keyword-matching tools that treat "automobile collision legal representation" and "car crash attorney services" as unrelated, vector databases recognize semantic equivalence. This makes your site architecture decisions data-driven rather than guesswork.

Traditional WordPress searches use keyword matching (LIKE queries in MySQL). A vector database stores content as numerical embeddings that capture semantic meaning—the "concept" behind the words, not just the words themselves.

Why this matters for AI visibility: Generative engines (ChatGPT, Claude, Perplexity) use semantic search to retrieve citable content. If two of your pages cover nearly identical topics (>0.92 cosine similarity), the engines see them as redundant and cite only the stronger one—wasting your authority.

Conversely, if you're missing pages for high-value subtopics (best existing page scores <0.60 against that topic), the engines simply cite a competitor instead.

The ingestion pipeline has three stages:

• Extract: Pull all posts and pages via WordPress REST API (/wp-json/wp/v2/posts, /wp-json/wp/v2/pages), preserving metadata (URL, title, type, practice area, target city, publish/update dates).
• Clean & Chunk: Remove HTML clutter, preserve H2 and H3 headings as semantic boundaries. Split at H2 rather than fixed token counts so each chunk is a natural topic section.
• Embed & Store: Generate vector embeddings (OpenAI text-embedding-3-small is recommended: 1,536 dimensions, cost-effective for a full site). Store in Supabase with HNSW indexing for sub-millisecond queries.

For legal content, use OpenAI's text-embedding-3-small or open-source BAAI BGE-M3. Never mix embedding models; consistency matters for semantic scoring accuracy.

After embedding your entire site, score each page against your topic clusters. Two key patterns emerge:

Cannibalization: Two pages scoring >0.92 cosine similarity cover nearly identical ground. Resolution: merge into one authoritative spoke, or differentiate one to target a distinct sub-question.

Coverage gaps: Topics where your best existing page scores <0.75 against that cluster represent missing content opportunities. If search volume is high (via GSC or Ahrefs), this gap is a high-priority spoke to create.

Monthly re-ingestion reveals drift: a page that scored 0.68 on topic X six months ago may now score 0.78 after editorial updates, or fall to 0.52 after drift—signaling when to revisit linking or content strategy.

Vector similarity scores reveal your true hub structure. A page scoring 0.65–0.80 against many subtopic spokes is a better hub than one scoring 0.90+ against just a few—it bridges multiple concepts.

Implementation:

• Identify the topic cluster (e.g., "Personal Injury Law in California") and score all pages within it.
• Promote the highest-authority bridging page (/practice-areas/personal-injury) as the hub.
• Assign all pages scoring 0.70–0.90 as spokes (chapters answering sub-questions: "How long do I have to file?", "What damages can I recover?", "Comparative fault explained").
• Compute pairwise similarity between spokes; link any pair scoring >0.80 without existing connections.
• Hub links down to every spoke; each spoke links up to the hub and sideways to siblings.

This architecture signals to AI engines that your site has comprehensive, non-redundant authority on that topic.

Before optimizing, establish a baseline:

• Content inventory: Matrix of all pages vs. topic clusters with similarity scores.
• AI citation baseline: Test a range of relevant queries across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews. Record: Does your firm appear? Is it cited or just mentioned? What passage is quoted?
• SEO baseline: Organic traffic, rankings, and internal link density from Google Search Console.

After implementing hub-spoke changes, re-score monthly. Track: average hub-cluster coverage score (higher = fewer gaps), orphaned page count (should drop), cannibalization pairs (should approach zero).

Correlation: Consistent improvements in cluster coverage correlate with organic traffic gains and—critically—increased AI citations 4–8 weeks post-fix as engines re-crawl and re-index your content.

Supabase (PostgreSQL + pgvector extension) unifies relational and vector storage. Unlike separate embedding services:

• No data sync overhead: Your pages, metadata, and embeddings live in one database.
• Hybrid queries: Search by vector similarity AND metadata filters (e.g., "find all content about Massachusetts probate, scoring >0.75 on the Probate hub").
• SOC 2 Type 2 compliance: Meets HIPAA and state bar requirements for sensitive operational data.
• RAG-ready: Direct integration with LLM APIs for retrieval-augmented generation pipelines.

Cost & scale: Infrastructure and embedding costs scale efficiently, making the approach viable for firms of all sizes. Primary expense is developer time: 20–40 hours initial setup, 2–5 hours monthly maintenance.

"Won't this require leaving WordPress?" No. The ingestion layer is CMS-agnostic; any system with a REST API can feed Supabase. WordPress stays your editor and CMS; Supabase runs the semantic analysis in the background.

"How often do we re-score?" Monthly re-ingestion is standard for firms publishing 4–12 new pages per month. Trigger immediate re-scoring after major content updates. Quarterly full re-embedding recommended when upgrading embedding models.

"What if we have multiple practice areas or locations?" Supabase stores practice-area and target-city metadata alongside embeddings. Score pages within each cluster independently; multi-location firms benefit most because cannibalization patterns emerge across geographies—revealing reusable templates versus genuinely local content.

"Can I verify the scores are accurate?" Yes. Spot-check high-cannibalization pairs (>0.92 similarity) by reading them side-by-side. Check gap analysis by confirming that topic-area pages scoring <0.60 are indeed thin or missing. Build confidence before automation.