We know poorly written content when we don’t want to read it, and we certainly know it will be worse once translated, leading to user queries, delayed time to market, and huge unnecessary costs. How can we stop this oncoming avalanche using objective data science measures and get to an actionable outcome: remediation and standardization? The core of the Smart Linguistics methodology is combining client performance KPIs such as time spent on web page, return on ad spend, cost per click, employee retention, employee satisfaction, and content consumption, to name but a few with linguistic features such as linguistic complexity, linguistic quality, and brand relevance to build AI-based predictive models. The Welocalize team will present real-life client case studies: ROI, exploratory analysis of client data, ML-based curation and cleaning methodology, linguistic feature ranking, AI-modeling approaches and lastly, driving optimal content performance for maximum business impact using best AI model.