Will AI translation replace human translators?
No. AI significantly assists translation and handles well-defined, high-volume content in appropriate contexts, but it still faces challenges with cultural nuance, creative transcreation, regulated domains, and low-resource languages. LocaTran's position is that AI and human linguists are complementary: AI produces the first draft and handles the initial workload; expert reviewers help ensure accuracy, tone, and compliance. The role of the linguist evolves over time.
What is the difference between NMT and LLM-based translation?
Neural machine translation engines (Google, DeepL, Microsoft, Amazon, domain-trained systems) are specialized for translation at industrial scale and cost. Large language models (the GPT and Claude families, among others) produce more fluent, context-aware text and are effective at tasks such as post-editing, transcreation, and multilingual content generation. In most production workflows, a hybrid of NMT plus LLM-driven refinement provides a balanced combination of quality and efficiency.
How do you manage quality when AI is part of the workflow?
AIQE scores segments against agreed quality criteria, routes lower-confidence content for human review, and is supported by senior linguists, qualified reviewers, and ISO-certified quality workflows. Evaluation metrics, issue tracking, and severity-based error categories make the process transparent from review to delivery.
Is it safe to send confidential content through AI translation?
Yes, when the right controls are in place. LocaTran uses private, enterprise-grade deployments with no training on customer data, signed NDAs, role-based access, and zero-retention options for sensitive workloads. Confidential client content is not sent to free or public LLM endpoints. For clients in regulated markets including mainland China, in-region processing and data residency options are available.
Can you train a custom MT engine on our content?
Yes. We train domain-adapted engines on your translation memories, glossaries, and approved corpora. Custom engines are effective for organizations with large volumes of in-domain content, strict terminology requirements, or style conventions that off-the-shelf engines cannot capture. Engines are retrained regularly to improve performance as content evolves.
Which CAT tools and TMS platforms do you integrate with?
We support major CAT tools and TMS platforms including Trados, memoQ, XTM, Phrase, Smartcat, Lokalise, Crowdin, and Wordbee, and can integrate with systems such as WordPress, Drupal, Contentful, Sitecore, Salesforce Commerce, GitHub, and GitLab for continuous localization workflows.
Which languages do you support?
150+ languages, with particular coverage in Chinese (Simplified and Traditional), Japanese, Korean, Vietnamese, Thai, Malay, Indonesian, Hindi, and other Asian languages, plus major European languages including English, Spanish, French, German, Italian, Portuguese, and Russian.
How much does AI-powered translation actually save?
It depends on the language pair, content type, and the quality tier you need. Cost savings are greater on high-volume, structurally predictable content, and smaller (or absent) on brand-facing or regulated content where human work remains essential. Rather than quoting an industry average, we run a benchmark on a sample of your real content, using BLEU, COMET, edit-distance, and human evaluation, to show specific savings, turnaround, and quality profile before you commit.
Share a sample of your content with us. Within two business days, we return an engine benchmark, a recommended workflow tier (light post-editing, full post-editing, publishable quality, or transcreation), and a transparent cost and timeline estimate. No obligation.
