The challenge
Developers use AI assistants to explore deployment options, troubleshoot applications, and compare platforms. This sample uses Railway to propose a 30-day campaign for measuring visibility and prioritizing useful content.
What I did
Defined the evaluation set
I grouped queries by developer intent: migration, deployment, infrastructure, troubleshooting, and emerging workflows. Each AI platform would be evaluated separately.
Separated the measurement dimensions
I distinguished mentions, citations, recommendations, cited URLs, capability accuracy, and competitive presence, with segmentation by audience, platform, language, and intent.
Scoped content and technical improvements
I planned a crawler-access and rendering audit alongside checks for canonicals, sitemaps, structured data, and editorial quality.
Mapped delivery and evaluation
I organized the work into phased deliverables, ending with an impact report and a 30–60 day roadmap. Structured data and llms.txt were hypotheses to test, rather than assumed routes to citations.
The outcome
An eight-page proposal with a phased campaign plan, an evaluation framework, and defined deliverables.
Independent strategy sample. The campaign described here is a proposal.