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VAIKAGROUP
AI Data ServicesNLP & LLMs

Text Annotation & NLP Data Labeling

Structured NER, sentiment, intent classification, and LLM training datasets.

VAIKA GROUP provides structured text annotation and Natural Language Processing (NLP) data labeling services for organizations developing language models, conversational AI systems, search engines and automated text classification solutions.

Our text annotation workflows transform unstructured text into machine-readable datasets through Named Entity Recognition (NER), sentiment analysis, intent classification, document categorization and conversational dialogue evaluation.

We use domain-specific taxonomies and consistent labeling guidelines to support specialized NLP applications across industries such as finance, healthcare, legal services and customer support.

REQUEST PROJECT ESTIMATEVIEW WORKFLOW (8 STEPS)ALL DATA & AI SERVICES
// OPERATIONAL CHALLENGE SOLVED

Why Organizations Outsource Text Annotation & NLP Data Labeling

Replaces noisy automated tags with consistent human linguistic judgment for specialized LLMs, search relevance, and document intelligence systems.

// EXECUTION PIPELINE

Our 8-Step Production Process

Structured guidelines, trained human operators, and multi-stage verification ensure predictable throughput at any dataset scale.

STEP 01

Text Data Assessment

Review source documents, conversations, text corpora and labeling objectives to establish the required annotation taxonomy.

STEP 02

Named Entity Recognition

Identify and label entities such as organizations, people, locations, dates, financial terms and domain-specific terminology.

STEP 03

Sentiment & Emotion Annotation

Classify text based on positive, negative and neutral sentiment, emotional tone and contextual meaning.

STEP 04

Intent & Text Classification

Categorize sentences, documents and customer conversations according to predefined intent classes and business taxonomies.

STEP 05

Conversational Data Evaluation

Review dialogue turns, response relevance, contextual consistency and potential hallucinations in AI-generated conversations.

STEP 06

Domain-Specific Labeling

Apply specialized annotation guidelines for legal, healthcare, finance and other industry-specific datasets.

STEP 07

Quality Validation

Conduct consistency checks, taxonomy alignment reviews and multi-annotator quality assessments.

STEP 08

Dataset Delivery

Export validated annotations in structured JSONL, CSV or other requested formats.

// TECHNICAL CAPABILITIES

Key Capabilities Included

Named Entity Recognition (NER)
Sentiment and emotion analysis
Intent and topic classification
Document and sentence classification
Conversational dialogue annotation
LLM response evaluation
Domain-specific NLP data labeling
Custom taxonomy development
// PRODUCTION DELIVERABLES

Deliverables & Formats

  • Annotated JSONL and CSV datasets
  • Entity tagging dictionaries
  • Text classification datasets
  • Annotation guidelines and taxonomy documentation
  • Quality validation reports
// APPLICATIONS & INDUSTRIES

Primary Use Cases & Sectors

LLM fine-tuningConversational AICustomer sentiment monitoringIntelligent searchDocument intelligenceNLP model development
SPECIALIZED WORKFLOW COVERAGE

Named Entity Recognition Services • NER Annotation • Sentiment Analysis Annotation • Text Classification • Intent Classification • LLM Training Data • NLP Dataset Preparation

// MULTI-STAGE QUALITY ASSURANCE

Verification & Consistency Controls

Multi-annotator consensus scoring, taxonomy drift audits, and domain specialist spot-checks ensure label consistency across complex linguistic contexts.

GUIDELINE CALIBRATIONHUMAN AUDIT REVIEWSSCHEMA VALIDATION
// SERVICE FAQ

Frequently Asked Questions

Yes. We align each project with domain-specific labeling taxonomies to accurately capture regulated terminology, clauses, clinical terms, and financial entities.
// PROJECT SCOPING & PILOT REQUEST

Scope Your Text Annotation & NLP Data Labeling Project

Share your dataset format, volume, or back-office requirements. Our Data & AI Core engineering lead responds within one business day with a pilot plan.