SEO Signal Architecture — When IA Meets Infra

Engineered an information architecture that made Google spend crawl budget on the pages that convert.

SEOArchitectureNext.jsDevOpsAnalytics

Context

A multilingual education platform had thousands of indexed pages but almost zero impressions. The issue wasn’t copy — it was the chaotic way signals reached Google.

Threats

  • Googlebot wasted crawl budget on duplicate parameterized pages.
  • Dynamic routes lacked canonical logic, confusing search engines.
  • Critical assets were blocked by misconfigured caching + robots rules.
  • Internal link equity died because pages were orphaned inside components.

Approach

  1. Redesigned IA using semantic clusters and component-level JSON-LD so every lesson, event, and course declared its relationship to parent topics.
  2. Moved rendering server-side (Next.js + edge caching) to expose real metadata instantly and support hreflang pairs.
  3. Built a link-flow visualizer (GraphQL + D3) that shows dead ends so writers fix content architecture, not just copy.
  4. Automated SEO checks in CI (Lighthouse CI + Screaming Frog headless) and blocked deploys when canonical/hreflang drifted.
  5. Used Search Console API + BigQuery to monitor crawl stats; anomalies pinged Slack.

Outcome

Crawl efficiency improved 54% in a month, time-to-index dropped from 18 to 6 days, and impressions grew 112% without publishing new blogs. The business finally treated SEO as a systems problem instead of a content chore.

Lessons Learned

When IA, rendering, and ops operate in sync, ranking becomes a byproduct. Search wants clarity, not cleverness.

    SEO Signal Architecture — When IA Meets Infra — Case Study