AI Data Solutions
Data Annotation
High-Quality Training Data to Scale AI Model Development
Our Data Annotation Process.
Our data annotation process is designed to deliver accurate, high-quality datasets tailored to your AI model training needs.
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Taxonomy CreationWe define a clear and precise structure to organize and categorize your data effectively.
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Guidline DevelopmentDetailed guidelines are crafted to ensure consistency and accuracy across annotations.
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Pilot Execution + DeliveryA potential pilot run validates the approach and aligns outputs with your project goals.
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Project KickoffThe project officially launches with dedicated team members and defined milestones.
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Single/Multi-Pass AnnotationData is annotated with one or multiple review passes to meet quality standards.
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Quality Testing + AnalysisTesting and analysis can be performed to guarantee the reliability and accuracy of the final dataset(s).
With our high-quality data labeling approach, you can trust Innodata’s annotated data to drive impactful and reliable AI/ML training.
Main Website Form 2025
Power Leading AI Model Development with
High-Quality Annotated Training Data.
Trust Innodata's subject matter experts to deliver accurate, reliable, and domain-specific multimodal data annotation, supporting use cases from search relevance and agentic AI to content moderation and beyond.
Image, Video, + Sensor Data Annotation
From faces to places, fuel your visual-based and CV machine learning models with high-quality annotated data.
Popular Use Cases:
- Autonomous Vehicle LiDAR
- Robotics
- Anomaly Detection
- Product Identification
- Facial Recognition
- Object Detection
- And More...
Text, Document, + Code Data Annotation
Train your models with high-quality data annotated from the most complex text, code, and document sources.
Popular Use Cases:
- Agentic AI Training
- Search Relevance
- Recommendation Engines
- Natural Language Generation
- Multilingual Translation
- Entity + Relationships
- And More...
Speech + Audio
Data Annotation
Scale your AI/ML models and ensure model flexibility with diverse annotated speech and audio data.
Popular Use Cases:
- Virtual Assistants
- Multilingual Transcriptions
- Speech-to-Text
- Audio Classification
- Regional Identification
- Intent Capture
- And More...
