nextura.ai
// AI DATA CAPABILITIES / DATA ANNOTATION

Precision annotation for production AI.

From pixels to intent — multimodal annotation engineered by domain experts, governed by ISO-aligned quality controls, delivered at scale.

Data annotation is not a commodity. It is the craft that determines whether your AI model succeeds in the real world. Nextura.ai's annotation practice combines trained human expertise, advanced tooling, and rigorous QA to deliver production-grade labeled data for every modality, every domain, and every scale requirement.

// IMAGE.ANNOTATION

Computer vision & image labeling

  • Bounding boxes and polygonal segmentation for object detection models
  • Semantic and instance segmentation for autonomous systems and medical imaging
  • Keypoint annotation for pose estimation and facial analysis
  • Facial landmark detection and tagging for biometric and AR applications
  • OCR labeling and document layout annotation for intelligent document processing
  • LiDAR and 3D point cloud annotation for ADAS and robotics
// VIDEO.ANNOTATION

Video & temporal labeling

  • Frame-by-frame object detection and tracking across video sequences
  • Action recognition and event detection for surveillance and sports analytics
  • Scene segmentation and contextual labeling for autonomous vehicles
  • Temporal activity annotation for behavior analysis and gesture recognition
// AUDIO.SPEECH.ANNOTATION

Speech, audio & voice intelligence

  • Speech transcription across 30+ foreign languages, dialects, and accents
  • Speaker diarization — identifying and separating multiple speakers in audio
  • Emotion, tone, and sentiment tagging for voice AI and CX models
  • Noise classification and acoustic event detection
  • Intent and entity annotation for ASR model training
  • TTS (Text-to-Speech) dataset preparation and phonetic labeling
// TEXT.NLP.ANNOTATION

Natural language & LLM training data

  • Named Entity Recognition (NER): people, places, organizations, financial entities, medical terms
  • Sentiment, intent, and topic classification at sentence and document level
  • Question-answer pair generation and verification for RAG and fine-tuning
  • RLHF (Reinforcement Learning from Human Feedback) annotation and preference ranking
  • LLM alignment tasks: safety scoring, toxicity classification, factual grounding
  • Content moderation annotation for Trust & Safety platforms
  • Cross-lingual annotation and multilingual NLU datasets
// QUALITY.GOVERNANCE

Built-in quality at every layer.

Our QA architecture is not an afterthought — it is the backbone of every annotation project.

  • Multi-level QA workflows: annotator → reviewer → QA lead → client acceptance
  • Gold standard benchmarking with controlled test sets for continuous accuracy measurement
  • Inter-Annotator Agreement (IAA) scoring to ensure label consistency
  • Human-in-the-loop validation for edge cases, ambiguous data, and model-generated labels
  • ISO-aligned process controls with full audit trails and error categorization
Quality & Governance Framework — Nextura.ai annotation pipeline from data intake through client acceptance with ISO-aligned controls
Quality & Governance Framework — Nextura.ai
// ANNOTATION.TOOL.STACK

Tooling built for every use case.

We operate across all major annotation platforms and maintain custom internal tooling for specialized requirements.

CVATLabel StudioLabelboxSuperviselyScale AI-compatible workflowsAmazon SageMaker Ground TruthPraat (audio)Prodigy (NLP)Custom internal tooling