Independent product
SEOcluster.ai
Search data, turned into a working product.
I founded and built SEOcluster.ai, a subscription SEO platform that turns Search Console queries into page decisions: what to create, improve or combine, and what content each page should cover.
At a glance
- Queries and ranking URLs
- Target Pages and content briefs
- Implementation and review
The work
What I built
From queries to page decisions
The Target Pages view connects groups of related queries with existing ranking URLs. Users can review which page should cover an intent, investigate overlapping pages and move into a content brief. The public demo shows that workflow using sample data from a dog-grooming business.
Keeping decisions connected to evidence
Clusters support the decision about a page. They do not automatically mean another URL is needed. The interface brings the queries, current ranking page and suggested action together so users can review whether to improve existing content or create something new.
Following the work after implementation
The product also supports content briefs, technical audits and migration mapping. Its outcome-tracking workflow compares Search Console data before and after a change. Those comparisons show directional movement; they do not establish that one edit caused a ranking change.
Building the whole product
My role spans the application, data workflows and interface. The backend uses Python and FastAPI, with a Next.js frontend and deployment on Google Cloud Run. This is a product I build and operate, as well as an example of my search and software work.
Testing beyond the code
I also built an independent AI review workflow for SEOcluster. It tests real user journeys on staging before a release is considered for production. That work is described separately in the AI build and review project.
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Related expertise
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