Case StudyGPU cloud platform
How a GPU Cloud Platform Got 10x More AI Traffic
In under five months, we published 29 pieces. One article got cited by all three major LLMs.
- More AI traffic than older pages
- 10x
- Major LLMs cited one article
- 3 of 3
- Pieces published in under 5 months
- 29
- Client
- GPU cloud infrastructure platform (AI/ML)
- Industry
- AI infrastructure / GPU cloud
- Timeline
- February 2026 – present (ongoing)
The Story in Three Parts
01
The challenge
- Buyers compare four or five providers
- Strong older pages had gone stale
- Rivals owned the comparison searches
02
What we did
- A clear plan for every buyer stage
- Guides and comparisons reviewed by engineers
- Content shaped so LLMs can quote it
03
The results
- The content library grew by 40% in a single month
- One comparison article brought in over a third of all organic traffic
- New pages outperformed older ones that had more authority
How We Did It
Two or three workstreams each month, from audit to syndication.
- 01
Audit and roadmap
Found stale pages and content gaps, then built a dated roadmap.
- 02
Flagship guides
An inference optimization guide, an industry report and 3 refreshes.
- 03
Comparison pages
Head-to-head comparisons against 3 competing GPU clouds.
- 04
Category guides
Deep guides on inference engines, GPU servers and clusters.
- 05
Agent integrations
6 integration guides in 2 months for fast-growing agent frameworks.
- 06
Syndication and tracking
Republished on developer communities, with monthly AI citation tracking.
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