![\"\"](\"https:\/\/alphacrc.com\/wp-content\/uploads\/2025\/09\/Proposed-workflow-diagram_v3-1024x468.png\")
<\/figure>\n\nNew localization workflows for a new era<\/h2>\n\n
Let\u2019s take a closer look at the potential applications of Shindan on your projects. In the above example, you\u2019ll see how a task moves straight from launch to translation. There are no restrictions on the type of translation performed here \u2013 ranging from fully human to neural machine translation (NMT) or large-language model (LLM). If you decide to opt for automated approaches to translation, we recommend using a service such as Braid, which will take your linguistic assets into account during the production process.<\/p>\n\n
Once translation has been completed, the bilingual files are automatically reviewed by Shindan. Shindan does have an \u2018off-the-shelf\u2019 solution, in which it measures quality according to a generic understanding of expected output, but it can be customized using your localization assets to provide an even more accurate view of brand-alignment and translation quality through your specific lens.<\/p>\n\n
Shindan assesses each piece of content across your chosen criteria, deciding which parts need to be sent for human review and which already meet your defined quality standard. Alpha\u2019s localization experts are ready to help you define what quality means to your brand, which assets to use in customizing the tool, and to ensure all evaluation works for your specific needs.<\/p>\n
![\"\"](\"https:\/\/alphacrc.com\/wp-content\/uploads\/2025\/03\/Launch-1024x576.png\")
<\/figure><\/div>\nGoing agentic with Ken Chang<\/h2>\n\n
During the evaluation process, Shindan uses an agentic AI network to ensure the highest possible quality output. Ken Chang, our Head of AI Development, explains why this agentic approach is key to Shindan\u2019s success.<\/p>\n\n
\u201cShindan\u2019s agentic network has been designed to guardrail AI. Our design was inspired by the idea of a team of coherent members working together: while everyone has the same goal, they each have a different task to work on. The members share information and knowledge, but they ultimately each specialize in their own field, focusing on their own tasks. Shindan follows this model by incorporating multiple AI agents, each one focusing on a specific part of the language quality problem. Together, they create a full solution that can take into account style guides, terminology, and error categories to deliver a truly comprehensive language quality management tool.<\/p>\n\n
This approach is vital because of the boost to consistency. When each agent is working on a small and focused task, the resulting output is significantly more consistent (and therefore reliable) than an AI that needs to deal with multiple tasks.<\/p>\n\n
An agentic approach also helps to reduce AI hallucinations. When the assigned task is small, prompts can have a much clearer and more specific direction, and agents have much less info to process. This is what I\u2019m referring to when I say that we\u2019ve designed Shindan\u2019s network to guardrail AI. We\u2019ve also implemented a shared memory layer, which enables each agent to know what the others are working on. This enables the formation of an effective feedback loop, further enhancing the quality assessment.\u201d<\/p>\n
![\"\"](\"https:\/\/alphacrc.com\/wp-content\/uploads\/2025\/08\/Data-planning-1024x576.png\")
<\/figure><\/div>\nContent-agnostic language quality management<\/h2>\n\n
Where desired or necessary, Shindan can be configured to work with humans in a workflow. Content Shindan sends for further editing prior to delivery can be sent through human post-editing, or even AI post-editing, with potential for further review afterward. This can then create an editing loop in which content is continually tweaked until it meets your quality expectations for delivery. Here, we begin to see a more cyclical approach to the typical localization workflow.<\/p>\n\n
While the above is an example of how we\u2019d recommend using Shindan in your workflows, it\u2019s important to remember that Shindan\u2019s various capabilities can be \u2018plugged in\u2019 to various points of your production line.<\/p>\n\n
Taking a look at the bigger picture<\/h2>\n\n
When we were developing Shindan, one of our goals was to move beyond a task-based approach to quality, and instead enable businesses to garner deep insight into their long-term output quality across projects. As Shindan assesses each string or segment during the localization process, it stores this information in a centralized database, making it available through the language quality control dashboard.<\/p>\n\n
As an increasing number of businesses switch to machine-translation-first workflows, accurate, ongoing quality control is only going to become more important.\u00a0 Here, you\u2019ll be able to track output quality across language corridors over time, gaining insight into the typical errors that arise in each language pair, and begin troubleshooting as soon as you notice overall quality begin to drift.<\/p>\n\n
Shindan\u2019s powerful quality management tools can be deployed in various ways across your localization pipeline. Talk to us today to find out more about how it can benefit you.<\/p>\n","protected":false},"excerpt":{"rendered":"
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\u2019ve 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 […]<\/p>\n","protected":false},"author":14,"featured_media":11174,"template":"","service":[],"sector":[],"insight_type":[],"class_list":["post-11328","insight","type-insight","status-publish","has-post-thumbnail","hentry"],"acf":[],"yoast_head":"\n