Text classification: documents organized into categories for NLP training data
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Text & NLP Annotation

NER, sentiment & intent, and classification for production NLP.

Entity, tone, intent, and taxonomy labels your LLMs and classifiers can train on.

Capabilities

Each capability pairs illustrative imagery with how we deliver it at production quality.

Named entity recognition: text annotated with people, places, organizations, and technical references

Named Entity Recognition (NER)

Entities for graphs, RAG, and core NLP—people, places, orgs, dates, codes, products. High-fidelity spans including nested/overlapping mentions, technical CODE/PROJECT refs, and cross-document coreference for consistent knowledge modeling.

Sentiment and intent analysis: customer messages labeled with emotion and user goals

Sentiment & Intent Analysis

Nuanced sentiment plus concrete intents from tickets, social, and feedback. Joy vs. frustration, cancellations, product questions, support—with sarcasm, urgency, and joint sentiment+intent validation for chatbots and research.

Text classification: documents sorted into hierarchical categories and industry-specific taxonomies

Text Classification

Hierarchical and multi-label taxonomies for legal, clinical, finance, and research. Manual validation into your schema powers retrieval, routing, and moderation—high volume, domain playbooks, multiple tags per document.

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