Content tagging solution for CMS powered by AI

The optimized tagging process reduced content editor’s time dedicated to manual tagging tasks.

Client

Global online news and media platform headquartered in Virginia.

Objective

Our client aimed to enhance their custom CMS capabilities by automating tag suggestions within the content workflow and providing guided recommendations for content authors. They sought an AI-based solution capable of analyzing submitted content and offering relevant, accurate, and optimal tag suggestions. Additionally, the solution should allow for the implementation of guardrails to minimize the use of misleading or irrelevant tags.

Solution

Our approach to the project began with a structured series of planning and discovery sessions aimed at comprehensive understanding of the intricacies of content tagging requirements. The team focused on key aspects such as accepting and rejecting proposed tags, introducing new tags, and managing the taxonomy tree.

Once discovery phase has been completed, AI architect designed a solution that includes the following features:

AI-driven tag suggestions: utilizes advanced LLMs to automatically generate relevant and accurate tag suggestions based on the content.
Optimized tagging process: significantly reduces the time content authors spend on manual tagging, allowing them to focus on content creation.
Custom tag management: enables authors to accept or reject proposed tags and introduce new tags, ensuring flexibility and control over content categorization.
Taxonomy tree management: provides a structured approach to managing tags, enhancing the organization and retrieval of content.
Guardrails for tagging: implements measures to prevent the use of misleading or irrelevant tags, maintaining the integrity of the tagging system.

Result

The AI-powered tagging solution not only enhanced the efficiency and accuracy of the client’s CMS but also laid the groundwork for further innovations in content management and metadata utilization.

Improved Tagging Accuracy: The AI-driven solution provided more accurate and relevant tags, improving searchability and content categorization.
Operational Efficiency: By automating the tagging process, the client reduced the time and effort required for content management.
Cost Savings: The project generates a reduction in operational costs with the streamlined tagging process.