How TechCorp Cut Content Costs by 40% with AI-Driven CMS

TechCorp managed over 50,000 assets and 12,000 content nodes across 8 regional sites. Manual translation, tagging, and publishing workflows consumed 60% of the editorial team's time, leaving little room for strategic work.

We implemented an AI content layer on top of their existing CMS, adding automatic taxonomy suggestion, translation pre-filling using a fine-tuned language model, and smart duplicate detection across their DAM. Workflows were redesigned to treat AI suggestions as a starting point rather than a final output.

Within six months, editorial throughput doubled. Translation costs dropped by 55%. Content tagging accuracy improved from 62% to 94%. The team redirected 40% of their previously manual effort toward high-value content strategy work.