AI-Powered Content Tagging & Metadata Automation
Where it Started
A growing content library became its own problem. As a global manufacturer's blogs, product pages, resources, and sustainability content expanded, manual tagging could not keep up.
Classifying everything by hand took real effort, introduced inconsistencies, and made it harder for users to find content through search, navigation, and filtering. Metadata quality varied from author to author and unit to unit, and taxonomy drift crept in.
The client needed a scalable way to automate metadata, improve discoverability, and hold consistent taxonomy governance across the whole digital ecosystem.
How we work
Discover
We analyzed the high-volume content types, the existing taxonomy, and the metadata patterns, to understand exactly where inconsistency was hurting discoverability and governance.
Develop
We designed AI-powered tagging models and automated classification rules, aligned to the business taxonomy: industry categories, technologies, and application areas.
Deliver
We integrated intelligent tagging workflows directly into the CMS, so metadata is generated automatically and content is classified consistently at scale.
What we achived
- Automated classification deployed across four major content categories, holding consistent metadata standards across the ecosystem.
- 70% reduction in manual tagging effort, freeing authors from repetitive classification work.
- 85% metadata accuracy rate, with fewer common tagging errors across large content volumes.
- Faster content publishing, as automated metadata removed a preparation bottleneck.
- Improved discoverability, with standardized taxonomy lifting search relevance, filtering, and navigation.
- Stronger taxonomy governance, with consistent classification across industries, technologies, applications, and resources.
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Related Cases
Answers to common Questions
Still have more questions? Talk to us
It reads content and assigns metadata (industry, technology, application, content type) automatically, using classification models aligned to your taxonomy, instead of relying on each author to tag by hand.
In this engagement it reached an 85% metadata accuracy rate, higher and more consistent than manual tagging at volume. Governance standards and review keep quality high where it matters most.
No. It removes the repetitive classification work so the team spends its time on content, not admin. People still own the taxonomy and the judgment; automation handles the volume.
Metadata is what powers search, filtering, and related-content navigation. Consistent, accurate tags mean people actually find the right content, which is the whole point of a large library.
Yes. That is the reason to automate. The framework applies the same classification rules to new content automatically, so discoverability and governance hold as volume increases.