Build AI that speaks every language your users do. Learning Spiral provides multilingual data collection, annotation, and validation across text, speech, and conversational formats, covering global languages and dialects. Our diverse, native-speaking experts ensure linguistic accuracy and cultural context, helping you train NLP, speech recognition, and generative AI models for truly global performance.
Multilingual Data
Text Annotation
It involves labeling and tagging written content — such as entities, sentiment, intent, and parts of speech — across multiple languages. This process helps AI models understand context, grammar, and meaning accurately, forming the foundation for natural language processing, chatbots, and text classification systems.
Language validation
It ensures that data used for AI training is linguistically accurate, culturally appropriate, and free from grammatical or contextual errors. Native-language experts review content to confirm correctness in tone, dialect, and usage, helping models understand regional nuances and produce more natural, human-like outputs.
Translation review
It focuses on evaluating translated content for accuracy, fluency, and contextual consistency between the source and target languages. This quality check ensures that meaning, tone, and intent are preserved, which is essential for training reliable multilingual AI systems and reducing translation errors in real-world applications.
Dataset creation
It involves collecting, structuring, and curating multilingual text, speech, or conversational data to train and evaluate AI models. Well-organized, diverse datasets across languages help improve model accuracy, reduce bias, and support the development of AI applications that perform consistently across global markets.