Case study / 2025 / Architect and engineer
Content intelligence engine
GSC and Ahrefs data, surfaced through MCP, driving content prioritization and decisioning instead of guesswork.
Problem
Editorial decisions were made on intuition. There was no live, consolidated read on search demand, competitor movement, or content decay, so effort went to pages that did not need it and missed the ones that did.
Constraints
The signal had to come from authoritative sources (Search Console plus Ahrefs), not a model's guess. It had to be consumable by non-engineers, and it had to be cheap enough to run continuously.
System
A first production version (v1) aggregates Search Console and Ahrefs data and exposes it to the workflow through the Model Context Protocol, so prioritization is driven by real performance data rather than opinion. A re-architected v2 with a new stack and broader feature set is in progress.
Outcome
Version 1 shipped to production and moved editorial prioritization from opinion to data-backed decisions, putting authoritative performance data directly into the content workflow: competitive research and content briefs that took days now take under an hour. Version 2 is being rebuilt on a new stack with broader functionality.
Stack
Next.js for the interface, the Model Context Protocol for tool access, and the Search Console and Ahrefs APIs as the data sources.
Table B - Outcome
- shipped to production, v2 rebuild in progress v1
- research and briefs that took days before <1 hour
- prioritization moved from opinion to evidence data-led
Stack: Next.js / MCP / Google Search Console / Ahrefs API