Language Data Collection and Curation
From raw collection to structured delivery, we prepare multilingual data that is clean, consistent, and ready for training, tuning, and evaluation.
Parallel Corpora Creation and Alignment
Our bilingual and multilingual corpora are built for model training and benchmarking, with careful attention to terminology control and cross-language consistency.
Prompt, Instruction, and Dialogue Dataset Development
We develop multilingual prompts, instructions, dialogue datasets, and response pairs for LLM training and fine-tuning.
Linguistic Annotation and Labeling
Language-focused annotation covers intent, named entities, sentiment, taxonomies, error types, and other linguistic categories.
Preference Ranking and RLHF Support
Our linguists conduct preference ranking, response comparison, and structured error analysis to help teams improve model alignment and reduce unwanted behavior.
AI Output Evaluation and Validation
Our reviewers evaluate AI-generated output across a full range of quality dimensions—fluency, faithfulness, terminology accuracy, style compliance, omissions, additions, mixed-language issues, and hallucination risk—so issues are identified before they reach end users.
MT Evaluation and Benchmarking
For machine translation programs, we assess output through acceptability scoring, source-target alignment checks, omission and addition detection, quality tagging, and error analysis.
MTPE and Human Reference Translation
Post-editing, human reference translation, and comparative linguistic review help strengthen multilingual output quality.
Speech Transcription and Metadata Annotation
For speech and voice AI workflows, we support verbatim transcription, timestamp alignment, speaker labeling, and non-speech event annotation.
Corpus Cleaning and Normalization
Language data is cleaned, normalized, filtered, and standardized to improve training quality and reduce noise across multilingual pipelines.
