AI Data Solutions
Data Annotation
High-Quality Training Data to Scale AI Model Development

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...
Our Data Annotation Process.
Our data annotation process is designed to deliver accurate, high-quality datasets tailored to your AI model training needs.
-
Taxonomy CreationWe define a clear and precise structure to organize and categorize your data effectively.
-
Guidline DevelopmentDetailed guidelines are crafted to ensure consistency and accuracy across annotations.
-
Pilot Execution + DeliveryA potential pilot run validates the approach and aligns outputs with your project goals.
-
Project KickoffThe project officially launches with dedicated team members and defined milestones.
-
Single/Multi-Pass AnnotationData is annotated with one or multiple review passes to meet quality standards.
-
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.

Why Choose Innodata for Data Annotation?
Bringing world-class data labeling services, backed by our proven history and reputation.

Global Delivery Locations +
Language Capabilities
85+ languages and dialects supported by 20+ global delivery locations, ensuring comprehensive language coverage for your projects.

High-Quality Annotated Data for Advanced Use Cases
95%+ average accuracy consistently delivered. We deliver highly accurate annotated data across modalities for advanced use cases like agentic AI, search relevance, and more.

Domain Expertise Across
Industries
5,000+ in-house subject matter experts covering all major domains, from healthcare to finance to legal. Innodata offers expert domain-specific annotation, collection, fine-tuning, and more.

Quick Annotation Turnaround at Scale
Our globally distributed teams guarantee swift delivery of high-quality results 24/7, leveraging industry-leading data quality practices across projects of any size and complexity, regardless of time zones.

Annotation Specialists
Our ontologists, linguists, annotators, QA specialists, and data scientists collaborates on building ontologies, creating guidelines, and performing annotations for leading model development.

Enabling Domain-Specific
Data Annotation Across Industries.

Agritech + Agriculture

Energy, Oil, + Gas

Media + Social Media
Search Relevance, Agentic AI Training, Content Moderation, Ad Placements, Facial Recognition, Podcast Tagging, Sentiment Analysis, Chatbots, and More…

Consumer Products + Retail
Product Categorization and Classification, Agentic AI Training, Search Relevance, Inventory Management, Visual Search Engines, Customer Reviews, Customer Service Chatbots, and More…

Manufacturing, Transportation, + Logistics

Banking, Financials, + Fintech

Legal + Law

Automotive + Autonomous Vehicles

Aviation, Aerospace, + Defense

Healthcare + Pharmaceuticals

Insurance + Insurtech

Software + Technology
Search Relevance, Agentic AI Training, Computer Vision Initiatives, Audio and Speech Recognition, LLM Model Development, Image and Object Recognition, Sentiment Analysis, Fraud Detection, and More...
Looking for a Platform-Based Annotation Tool?
Enable your teams to label data at scale with our web-based annotation platform for record classification, document classification, inline classification, and image annotation.

8 out of 10 AI projects fail, with 96% of organizations facing challenges related to data quality, data labeling, and building model confidence.*
Despite advancements in automation, human expertise remains indispensable, especially in ensuring high-quality data labeling.
Human annotators provide critical contextual understanding, ensure quality control, mitigate bias, and offer adaptability —elements that automation alone cannot fully address.
Why Humans Still Matter in Data Labeling.
Let’s Innovate Together.
See why seven of the world’s largest tech companies trust Innodata for their AI needs.

We could not have developed the scale of our classifiers without Innodata. I’m unaware of any other partner than Innodata that could have delivered with the speed, volume, accuracy, and flexibility we needed.
Magnificent Seven Program Manager,
Al Research Team
CASE STUDIES
Success Stories
See how top companies are transforming their AI initiatives with Innodata’s comprehensive solutions and platforms. Ready to be our next success story?



Data collection in AI involves gathering diverse and high-quality datasets such as image, audio, text, and sensor data. These datasets are essential for training AI and machine learning (ML) models to perform tasks like speech recognition, document processing, and image classification. Reliable AI data collection ensures robust model development and better outcomes.
Innodata provides comprehensive data collection services tailored to your AI needs, including:
- Image data collection
- Video data collection
- Speech and audio data collection
- Text and document collection
- LiDAR data collection
- Sensor data collection
- And more…
Synthetic data generation creates statistically accurate, artificial datasets that mirror real-world data. This is especially beneficial when access to real-world data is limited or sensitive. Synthetic data helps with:
- Data augmentation to expand existing datasets.
- Privacy compliance by generating non-identifiable replicas of sensitive data.
- Generative AI applications requiring unique or rare scenarios.
- And more…
Innodata offers synthetic training data tailored to your specific needs. Our solutions include:
- Synthetic text generation for NLP models.
- Synthetic data augmentation for enriching datasets with diverse scenarios.
- Custom synthetic data creation for unique edge cases or restricted domains.
- And more…
These services enable efficient AI data generation while maintaining quality and compliance.
Innodata’s data collection and synthetic data solutions support various industries, such as:
- Healthcare for medical document and speech data collection.
- Finance for document collection, including invoices and bank statements.
- Retail for image data collection, such as product images.
- Autonomous vehicles for LiDAR data collection and sensor data.
- And more…
If you’re looking at AI data collection companies, consider Innodata’s:
- Expertise in sourcing multimodal datasets, including text, speech, and sensor data.
- Global coverage with support for 85+ languages and dialects.
- Fast, scalable delivery of training data collection services for AI projects.
Yes, our synthetic data for AI solutions enhance existing datasets by creating synthetic variations. This approach supports AI data augmentation, ensuring diverse training scenarios for robust model development.
We deliver high-quality datasets, including:
- Image datasets such as surveillance footage and retail product images.
- Audio datasets like customer service calls and podcast transcripts.
- Text and document datasets for financial, legal, and multilingual applications.
- Synthetic datasets for generative AI, tailored to your specific requirements.
- And more…
Synthetic data replicates the statistical properties of real-world datasets without including identifiable information. This makes it an excellent option for training AI models while adhering to strict privacy regulations.
Data collection involves sourcing real-world datasets from various modalities like image, audio, and text, while data generation creates artificial (synthetic) data that mimics real-world data. Both approaches are crucial for building versatile and high-performing AI models.
Yes, we offer LiDAR data collection for applications in autonomous vehicles, robotics, and environmental analysis, ensuring high-quality datasets for precise model training.