Kevin Urrutia & Magic Rinku's Internal Linking SaaS

15/09/2025 47 min

                    Kevin Urrutia & Magic Rinku's Internal Linking SaaS

Listen " Kevin Urrutia & Magic Rinku's Internal Linking SaaS "

Episode Synopsis


Guest Bio
Kevin Urrutia is a software engineer turned serial entrepreneur with 20+ years of experience building tech products. He's worked at Silicon Valley companies including Mint.com and Zaarly, and founded Voy Media, a digital marketing agency that has generated over $50 million in revenue. His latest venture, Magic Rinku, tackles one of SEO's most persistent challenges: automating internal link building at scale.
Connect with Kevin:

Email: [email protected]
Twitter: @danest
Website: Magic Rinku


Episode Summary
In this unscripted conversation, Kevin shares the journey of building Magic Rinku from personal frustration to SaaS solution. We dive deep into the technical challenges of WordPress integration, the strategic use of AI versus traditional algorithms, and unconventional marketing approaches that are actually working in 2025.
Kevin reveals how he discovered 350 out of 400 articles on his own site had zero internal links, the technical nightmare of supporting multiple WordPress page builders, and why cold email and Reddit still outperform traditional SaaS marketing for developer tools.

Key Topics Covered
The Genesis Story

How Kevin's personal pain with internal linking across 8-10 affiliate sites led to Magic Rinku
The validation process through Reddit and direct customer conversations
Why most SEO professionals are missing huge linking opportunities due to broken manual processes

Technical Deep Dive

WordPress ecosystem challenges: Supporting Elementor, Beaver Builder, Gutenberg, and Classic Editor
The 30-second delay problem and why bulk operations are complex in WordPress
Database structure differences across page builders and how they affect plugin development
Error handling and retry systems for WordPress API integration

AI Implementation Philosophy

Strategic use of AI vs. traditional algorithms (RAKE, ENG tagger)
Two specific AI use cases: contextual understanding and smart sentence generation
Why 30% of AI suggestions are perfect, 50% need tweaking, and 20% are unusable
The explainability problem and showing confidence scores to use...