TRAIIN DATA
Unlock trusted, high-quality data and content decisions with Raiinmaker’s
globally distributed data validation platform.
Raiinmaker delivers decentralized accuracy for content moderation and QA to dataset refinement
Why Raiinmaker?
450,000+ Global Validators
Leverage a vast, always-on network of real human validators across the world—available on demand for real-time or batch validation workflows
Drop-In Agent Integration
Plug into our native Eliza plugin or connect your own agents via API. Enable hybrid workflows for moderation, quality assurance, and more
Agentic Verification Systems
Accelerate results with our intelligent agent layer, enabling rapid decision-making and self-improving validation pipelines
Consensus-Based Decision Engine
Consensus validation ensures accuracy and transparency, customize thresholds for reputation minimums, consensus levels, and validator roles
Built For Your Stack
Flexible API Integration
Start validating in minutes with our developer-friendly APIs. Supports polling and callback modes for real-time task tracking.
Multi-Modal Input Support
• Tagging
• Categorization
• Confirmation
• Sentiment analysis
• Scale ratings
Modular Use Cases
• Content Moderation
• Brand Compliance
• Quality Assurance
• Dataset Creation & Cleanup
• HITL Training Pipelines
On-Chain Task Tracking
Every vote, validator, timestamp, and reputation score is recorded on-chain—giving you an auditable, immutable trail of every decision.
On-Chain Reputation Systems
Track performance and correctness over time for both humans and agents. Build trust into every vote with reputation-weighted validation.
Built for Scale. Tuned for Precision.
Raiinmaker brings the scalability of web3 and the nuance of human intelligence to every decision pipeline for
• Refining training datasets
• Enforcing brand standards
• Deploying real-time content filters
Data Validations:
Utilize our 450k+ user base across 190+ countries to efficiently train your data
- Classification
- Sentiment Analysis
- Confirmation
- NER Tagging
Classification
Human validators help verify assigned labels, helping train models to better recognize patterns and reduce misclassification.
Sentiment Analysis
Scale based ranking mechanism. Tasks where Validators provide a response within a numerical range, useful for sentiment analysis or rating systems (e.g., 1-10).
Confirmation
Validators review and confirm yes/no or true/false responses helping confirm information or responses.
NER Image Tagging
Objects are named and tagged inside of each task. This involves marking specific elements within a dataset, such as bounding boxes on images or highlighting text.
