Scale Your Content with AI. Trust Human Experts to Protect Your Brand.

AI localisation with human review is not one workflow. It is a decision framework. Locaria’s Tiered Quality Governance model assigns the right methodology to each content type based on risk level, complexity, and volume.

High-volume, low-risk content (product descriptions, FAQs, structured data, etc.) gets the speed and cost efficiency of AI with a light human pass. Brand content gets closer human oversight. Campaigns built around cultural nuance or emotional storytelling bypass automation entirely.

Your budget goes where quality matters most. Not spread evenly across content that doesn’t need it.

AI translation has improved significantly. LLMs in particular produce fluent, grammatically confident output. That confidence is also the problem. Subtle brand inconsistencies and factual errors are far harder to spot when the surrounding text reads well.

Locaria’s post-editors train specifically for this. They correct tone, restore cultural alignment, and surface the errors that surface-level fluency conceals. The result is output that performs in-market, not just output that reads correctly.

Locaria continuously assesses and tracks the performance of both Neural Machine Translation engines and Large Language Models across language pairs, content verticals, and output quality. No single engine leads across every combination.

Engine selection is determined by language pair and content type, not by default. For major European languages and general marketing content, DeepL performs consistently well. For Asian language pairs, regionally specialised engines such as Tencent, Baidu, and Jais capture syntax and grammatical nuance that general-purpose models miss.

That selection process is ongoing. Engine performance shifts as models update. Locaria tracks that movement so your content is always processed by the best-performing option for its specific context, not the most convenient one.

No. Locaria applies AI and Machine Translation to simple, high-volume content, such as e-commerce product descriptions. For complex, emotional, or creative marketing campaigns, human-led localisation and transcreation remain the approach.

Modern Neural Machine Translation can produce grammatically correct output, but it lacks fine-tuning. AI struggles to capture your brand tone of voice, specialised vocabulary, and audience nuances. A human expert reviews and corrects the machine output through Human Post-Editing (HPE), ensuring the result is tailored for your audience.

Post-editing content from Large Language Models (like ChatGPT) is more challenging than post-editing standard NMT tools (like Google Translate or DeepL). LLMs can read exceptionally well, but they’re prone to factual inaccuracies and inconsistencies that are harder for a human editor to catch than typical linguistic or grammatical errors.

Not as much as you might expect. Machine translation still requires human intervention across the process, including:

• File preparation and setup
• Continuous human review and quality assurance
• Project management and planning

Because this human oversight is necessary to guarantee final quality, Locaria is transparent with procurement teams that cost savings may be more modest than initially anticipated if high standards are maintained.

Locaria’s methodology is “human-in-the-loop” (HILP): combining the speed and scalability of AI with the cultural nuance and emotional intelligence of human experts. This hybrid approach is designed to achieve near 100% content accuracy while protecting the richness of language.