The SAY Network delivers affordable AI training data through a global community with ownership.
Bespoke Data and Eval to Train Your Models
Voice Data
High-quality voice recordings and transcriptions with customizable attributes such as accents, emotions, age groups, and speaking styles.
Voice Data
Our network collects and validates speech data from native speakers worldwide, ensuring authentic pronunciation and natural language patterns.
Human Evaluation
Expert review and validation of AI outputs across multiple domains and use cases.
Human Evaluation
Our community of domain experts provides nuanced feedback on model outputs, helping identify biases, errors, and areas for improvement in your AI systems.
Synthetic Data Validation
Human verification of AI-generated content to ensure quality and authenticity.
Synthetic Data Validation
Our network helps validate and improve synthetic data by providing human feedback on authenticity, accuracy, and natural variation in AI-generated content.
Content Moderation
Scalable human moderation for training safer and more responsible AI systems.
Content Moderation
Expert moderators help identify and filter inappropriate content, ensuring AI training data meets ethical standards and compliance requirements.
From startups to enterprises, SAY Network provides the data infrastructure needed to build and improve AI models.
Voice API Case Study
A voice API company faced a critical challenge: developing robust APIs for low-resource languages where quality training data was scarce.
Their mission to democratize voice technology across diverse languages required extensive, high-quality voice samples from native speakers - a resource that proved difficult to source through existing resources.
Through SAY Network, we rapidly developed a global community of native speakers, collecting precisely tagged voice samples with various moods and scenarios.
Sample Recording
Native Hausa Speaker - Market Scene Description
A bara, kamfanoni 4 sun shiga cikin hanar sadwa dinmu. Dukkansu sun samu ci gaba mai inganci.
Data Sanitization Case Study
We have cleansed training data that needed standardization to improve model performance.
Through SAY Network's global community, we provided comprehensive data standardization services, ensuring consistency across their entire dataset. This significantly improved their model's performance and reduced training errors.
How the Network Works
Requirement Submission
AI companies define their data collection needs and quality standards.
Network Activation
Our protocol matches requirements with the right community contributors.
Data Collection
Contributors gather and submit high-quality training data through our platform.
Quality Validation and Delivery
Verification process ensures data meets AI training standards.
How Our Community Benefits
The SAY Network is built on community ownership through the $SAY token. Addtionally, every client payment is distributed to benefit our community and strengthen the network.
Contributor Rewards
Performance-based incentives for quality data contributions
- Quality-based distribution
- Activity-level bonuses
- Long-term contributor benefits
Community Growth
Supporting network expansion and community development
- Community development
- Training programs
- Regional expansion
Network Operations
Sustainable development and quality assurance
- Platform development
- Quality control
- Network security