Off-the-Shelf AI
Training Datasets
Pre-built AI training datasets that accelerate large-scale model development without the delays of custom collection.
Current Dataset Coverage
Explore Innodata’s off-the-shelf datasets built for post-training, evaluation, and domain-specific model improvement.
STEM Data
Expert-curated datasets across mathematics, physics, biology, chemistry, and engineering.
Coding Data
Datasets for code generation, debugging, software reasoning, and benchmark-style evaluation.
Agentic Workflow Data
Datasets for multi-step reasoning, tool use, and structured workflows supporting agent-based model behavior.
Multimodal Data
Datasets spanning text, image, audio, and interface interactions for evaluating multimodal model performance.
Specialized Domains
Datasets for edge cases, niche workflows, and specialized domains such as CBRNE and 3D CAD, supporting advanced model evaluation and testing.
Finance Data
Datasets spanning finance, applied research, deep research tasks, and analytical reasoning.
Healthcare Data
Medical case studies and medical Q&A datasets built to support clinical and health-related model evaluation.
Robotics & Physical AI
Datasets for embodied reasoning, egocentric perspectives, and real-world task execution in physical environments.
Custom Data
Semi-custom adaptations, targeted extensions, domain expansion, and support for model-stumping requirements.
Production-Ready AI Training Data, Available Now
Production-Ready
Pre-built datasets optimized for AI training and evaluation
Scalable Volume
Thousands of structured questions across core domains
Advanced Difficulty Tiers
Graduate and PhD-level Question & Answer (Q&A) and Question, Solution, Answer (QSA) datasets.
Continuously Refined
Ongoing dataset analysis to identify model weaknesses and close reasoning gaps faster.
Adaptable
Adaptable and extendable to meet evolving model requirements
Access Scalable AI Training Data Without Custom Delay
Share your target domains and volume requirements to review available datasets and timelines.