US-DATAData Annotation
Cabinet
Text classification assigns categories to textual data and powers search, chatbots, customer communication analytics and document processing.
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Text classification identifies semantic category from content, context and linguistic signals. Labels can be created for phrases, messages, dialogues, requests, emails, documents and publications.
We classify documents, messages and fragments; identify topic and context; label intents; support multi-class, multi-label and hierarchical workflows, large multilingual corpora and strict validation.
Our annotation and NLP teams follow agreed guidelines to preserve class accuracy and annotation consistency even across large data volumes.
Inconsistent labels reduce accuracy, introduce bias and can make production behavior unreliable.
We create task-specific guidelines and validate every stage so the dataset matches the model architecture and business objective.
Examples of data annotation for machine learning
Full data preparation cycle from raw data to model-ready output
How does US-DATA deliver the results business needs?
We pay close attention to quality. Even the most accurate model will not perform well if the data is labeled with errors.
Our team works under a unified annotation rule system based on multi-level review and consistency control. Every process is adapted to client needs and the specifics of the ML model. The result is a clean dataset ready for training without additional rework.
We understand how data quality affects model training.
Annotation tailored to architecture and project goals.
From pilots to large-scale data volumes.
Control at every stage and transparent metrics.
We handle non-standard and challenging scenarios.
Data exported in the format you need.
Faster model training
Higher accuracy and robustness
Lower retraining costs
Production-ready datasets
Data security and compliance
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* This estimate is not a public offer. Final cost is determined after technical analysis and data review.
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Text classification identifies topic, category, intent and other semantic attributes in messages and documents. US-DATA provides binary, multi-class, multi-label and hierarchical classification, including intent classification.
We develop annotation guidelines, run pilot labeling and multi-level quality control, then deliver a consistent dataset in the format required by your ML pipeline.
The result is data that is ready to train, validate and deploy machine learning models in real operating conditions.
