Content moderation
Review and monitor user-generated content to ensure that it meets your standards and guidelines.
Text annotation
Vietnamese text labeled with meaning and context.
Turn raw language data into structured signals for moderation, search, classification, conversational AI and language model training.
What we annotate
Language signals your AI can learn from.
Annotation schemes are aligned with your taxonomy, domain, edge cases and quality targets, then applied by trained Vietnamese reviewers.
Localization
Adapt text and language content to fit the target culture, market, audience, and local communication style.
Toxic language identification
Detect and label offensive, abusive, hateful, or unsafe language to support content safety and moderation workflows.
Keyword annotation
Identify and label important keywords, phrases, and search terms to improve information retrieval and text classification.
Intent classification
Classify text into predefined action or request categories to help AI systems understand what users want to do.
Grammatical markup
Annotate grammatical features such as parts of speech, sentence structure, tense, dependency, and linguistic patterns.
Sentiment annotation
Label text based on expressed emotion, opinion, attitude, or sentiment polarity such as positive, negative, or neutral.
Language analysis
Analyze text for meaning, context, tone, structure, and language patterns to support better AI understanding.
Content evaluation
Review and assess written content for quality, relevance, clarity, accuracy, and overall usefulness.
Domain annotation
Label text according to industry-specific categories, terminology, and subject areas such as finance, legal, healthcare, or technology.
Dialog analysis
Analyze conversations and classify utterances based on intent, function, context, and interaction flow.
Linguistic annotation
Label text with linguistic information such as syntax, grammar, meaning, and structure for language model training.
Intent annotation
Identify the purpose behind a user message, query, or utterance to support chatbots and conversational AI systems.
Named entity tagging / NER
Identify and classify named entities such as people, locations, organizations, products, dates, and other key terms.
Built for your use case
Annotation programs shaped around the language task.
We translate your ontology and decision rules into clear workflows for native reviewers, with ambiguity surfaced early.
01Chatbots & conversational AI
02Safety & content moderation
03Search & classification
04NLP & language models
How we deliver
From taxonomy to trusted labels.
A managed workflow keeps decisions consistent, disagreements visible and quality measurable as volume grows.
Define the schema
Align labels, examples, edge cases, domains, volume and acceptance criteria.
Train & calibrate
Qualify native reviewers through practice batches and guideline feedback.
Annotate & review
Run controlled production with consensus, audits and issue resolution.
Deliver & report
Provide structured labels with quality findings and decision documentation.
Start focused
Plan a Vietnamese text annotation pilot.
Share your taxonomy, sample data and quality targets. We’ll propose a focused annotation plan with clear review criteria.
Discuss your text annotation project ↗