Language combinations
Cost reduction per unit
Challenge
Training data at model scale, with no room for corrupted input.
The company needed its LLMs to accurately understand, respond to, and adapt for users across 100+ languages and 300+ language combinations. Getting there required translation, post-editing, and quality evaluation at enterprise volume, all feeding directly into model training pipelines. A single corrupted or inconsistent batch of training data could degrade model performance, so QA controls had to catch problems before data ever reached the pipeline. The company needed a partner who could stand up the platform, the governed process, and the linguist network all at once, not assemble them piece by piece.
Approach
Centific was chosen as the trusted partner to run the pipeline end to end. Flow, Centific’s multilingual AI platform, governed and automated the workflow, while OneForma's linguist network supplied the human translation and post-editing at the volume the company's model training required.
AI Data Pipeline - Flow and OneForma ran the end-to-end workflow for creating and validating LLM training data, from ingestion through quality-cleared output, across all 300+ language combinations.
AI Governance Framework - Content was tiered by risk level, with ML-based anomaly detection and automated QC gates screening out low-quality or inconsistent data before it could contaminate a training set.
Model-Ready Quality Controls - Automated screening, linguist post-editing, and AI-assisted QA worked as layered checks, so training data met the accuracy and consistency bar the model needed.
Scalable Talent Infrastructure - Geo-targeted recruitment, automated training modules, and AI-assisted validation let the linguist network grow fast. Language pairs expanded from 13 to 336 without loosening quality or governance.
Outcomes
The program scaled to hundreds of languages while holding the line on cost, speed, and governance.
Lower cost per unit - A 45%+ reduction in cost per translated unit held steady across 600+ projects a year, driven by the AI-human hybrid delivery model.
On-time, at scale - 99% on-time delivery held year-over-year as the program grew to cover hundreds of languages.
Faster global launches - Simultaneous international market launches across priority regions became possible, speeding up the company’s global product rollout.
No compromise on governance - Enterprise-grade LLM training data at scale, with governance controls and model quality standards held constant throughout.
Language combinations
Cost reduction per unit
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