Twelve million localized words a year. Six siloed teams. One workflow to replace them all — and the results are hard to argue with.
Awin Global, one of the world’s largest affiliate marketing networks, has cut its content localization turnaround time by 57 percent — reducing a four-week process to under 12 days — by consolidating a fragmented internal operation into a single, AI-powered workflow built on technology from Acclaro and Lokalise.
The results mark a significant operational shift for a company operating across 17 countries on four continents, producing 1.5 million source words and approximately 12 million localized words annually across eight languages.
The Problem: Six Teams, No Single Process
Awin’s localization operation had grown organically across product, user experience, marketing, and engineering teams — each running its own processes, tools, and review cycles. The fragmentation introduced inconsistencies in brand voice and terminology, slowed product and marketing releases, and made it difficult to track where a given piece of content was in the localization pipeline at any given time.
The cost was measurable. Turnaround times stretched to four weeks. Backlogs accumulated. And the internal review burden consumed the equivalent of three to five full-time employees who could otherwise have been directed toward higher-value work.
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The Solution: One Workflow, Shared Infrastructure
Working with Acclaro, a technology-driven language services provider, and Lokalise, an AI language platform, Awin replaced its six disparate localization streams with a single, centralized workflow that combines AI-powered translation, automated orchestration, and expert human post-editing.
Shared linguistic assets — glossaries, translation memories, and style guides — now ensure consistent terminology and brand voice across all eight languages, reducing redundant work and eliminating the variance that had crept into content produced by isolated teams. Smart automation integrates localization tasks directly into existing development processes, reducing internal review requirements by up to 80 percent.
The human element has been preserved rather than eliminated. Trained linguists provide domain expertise and post-editing at critical points in the workflow, maintaining the quality and contextual accuracy that pure machine translation cannot reliably deliver at scale.
“By consolidating tools and workflows into a standardized, technology-enabled process, we have been able to reduce localization turnaround times by more than half while clearing our existing localization backlog,” said Rosario Messina, Senior Product Operations Manager at Awin Global. “We are now enabling faster market releases without any budget increase.”
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The Broader Argument
The Awin case illustrates a pattern increasingly visible across global businesses: localization infrastructure that develops organically tends to fragment at scale, and the cost of that fragmentation — in time, consistency, and internal resource allocation — compounds quietly until it becomes a strategic constraint.
“Disparate localization processes become bottlenecks at scale,” said Devin Lynch, Chief Growth Officer at Acclaro. “By unifying workflows, leveraging automation, and embedding a central language platform, we helped Awin unlock faster releases with greater consistency — without increasing spend. Awin now has scalable localization infrastructure to accelerate expansion, product iteration, and competitive differentiation with high-quality multilingual content that resonates locally and performs globally.”
For a network of one million partners spanning influencers, technology companies, and global brands, the ability to move faster in local markets — without sacrificing the consistency that holds a global brand together — is not an operational nicety. It is a competitive requirement.