Localization demand outstripping your budget? Shindan provides the answer.
*Actual gains vary from project to project, and will depend on base translation quality.
Localize moreShindan, our AI-powered language quality evaluation tool, optimizes your workflows, enabling you to get more translation from your budget than ever before.
Shindan uses an agentic AI network alongside a parametric approach to quality evaluation in order to identify which content needs human review, and which is already error-free and ready for release. The result? Human expertise is focused on the areas that need it most.
As more businesses look to leverage the power of automation in their localization workflows, the question of quality control is becoming increasingly prevalent. Our platform enables businesses to analyze their machine output quality over time, gaining deeper insights into their automated processes.
Shindan offers users a holistic view of their localization output quality, with a suite of use cases to help you optimize your budgets and, crucially, your return on investment.
Our parametric quality evaluation automatically identifies which content requires human review and which already meets your quality standards. This streamlined approach reduces review time and optimizes your budget, ensuring you can localize more of your content for global markets.
Shindan analyzes both text and screenshots to identify in-context issues like text truncation. It integrates seamlessly into existing LQA workflows by reporting directly to tools like JIRA via API or flagging items for human review.
Shindan provides objective, metrics-based measurement of translation output over time. It monitors project quality and identifies significant shifts in your production pipeline, delivering unprecedented insight into your localization process.
We believe that every business has unique quality needs. That’s why we’ve built Shindan to be fully customizable and responsive, ensuring all your assessments are relevant to you.
Not sure how to define quality? No need to worry – Shindan’s standard model uses the ISO 5060 quality framework as a base, using a tweaked set of categories and weights to ensure optimal AI assessment. During the onboarding process, we will work together to understand what quality means to you, and how best to further configure Shindan for your quality needs.
Shindan can be used across your workflows to perform a range of tasks, from filtering strings out of the human post-editing process, to providing a holistic, objective view of quality on languages that you can’t assess on an ongoing basis yourselves.
Thanks to Shindan’s intelligent assessment, you know your localization spend is focused on the content that needs it most. Using your assessment criteria, Shindan filters high-quality content from the traditional post-editing workflow and sends it straight to delivery. Our clients have found this has enabled them to localize much more content without having to increase their budget.
Our teams will work alongside you to identify the best ways to integrate Shindan into your workflows. We will help you analyze your content types, the visibility, and the risk level in order to establish recommended quality weights and applications for your program. We understand that each use case is different, which is why we’ve designed Shindan to be as flexible as possible.
Since 1987, Alpha CRC has provided clients around the world with expert localization services. From humble beginnings in translation to full content creation and multilingual voiceover production, we’ve spent the last four decades honing our craft, working tirelessly to polish translation quality and help customers engage with their global audiences. Now, almost forty years after […]
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At Alpha CRC, our four decades of localization expertise informed every decision in developing Shindan, our proprietary language quality management platform. But the path to creating a production-ready AI system that could meet the exacting standards of professional localization wasn’t straightforward. The technical evolution of Shindan, from a promising but unreliable single-AI system to today’s […]
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