Techwards Optimized Content Relevance and Reduced Costs through Intelligent Automation
A leading publisher managing a vast content library faced significant challenges in interlinking related content at scale. The lack of regular updates and the complexity of identifying relevant links prompted the need for an AI-driven interlinking solution. The solution, developed by Techwards, enhanced content relevance, streamlined interlinking processes, and significantly improved SEO performance.
Problem Statement
Managing multiple domains with over 150,000 articles each posed significant challenges in efficiently identifying relevant internal links. Manual cross-referencing was labor-intensive, while relying solely on LLM models proved unsustainable due to high costs. Ensuring contextual relevance, optimizing SEO, and keeping up with seasonal updates added complexity. Outdated links in seasonal articles further harmed user experience and SEO rankings, highlighting the need for a scalable solution.
Solution
Techwards implemented an AI-driven interlinking solution using vector indexing to identify contextual relationships across the content library. This enabled precise shortlisting of articles for interlinking while significantly reducing the dependency on LLM processing. The system leveraged small-batch processing to update links simultaneously and introduced automated updates for seasonal content, ensuring outdated or irrelevant links were refreshed regularly. This simplified content management, improved SEO performance, and elevated user experience by minimizing manual effort.
Outcome
The AI-powered interlinking solution achieved remarkable results:
- The interlinking solution was deployed on one major domain, significantly boosting SEO rankings across the content library.
- Automated updates ensured seasonal content remained relevant, improving user engagement and organic traffic.
- Operational costs for interlinking were reduced, enabling the team to focus on strategic initiatives rather than manual processes.
