Quality Management
Big Data Processing
WorldWide Learning
Making AI Work for
Owners and Users
The ZK Machine Learning
Network powering AI
Robotics
Integration Partnerships:
Supported By:
Zoro Ecosystem
WorldWide Learning
Big Data Processing
Quality Management
Making AI Simple
Accessible / Tokenized
Leading
Onboarding Levels
3
Community engagement
900M
Evolution
9
Types of learning models
WEB2
Community engagement
Chair
Photo
Shooting
Audio
Dog
Video
Harvester
Video
Сar sound
Audio
Medicine
X-Ray
Fire
Video
 Sounds of fire
Audio
Programing
Code review
Medicine
X-Ray
Chair
Photo
Shooting
Audio
Dog
Video
Harvester
Video
Сar sound
Audio
Medicine
X-Ray
Fire
Video
 Sounds of fire
Audio
Programing
Code review
Medicine
X-Ray
Chair
Photo
Shooting
Audio
Dog
Video
Harvester
Video
Сar sound
Audio
Medicine
X-Ray
Fire
Video
 Sounds of fire
Audio
Programing
Code review
Medicine
X-Ray
Our CEO 
Chetana Desai
CEO & Co-Founder
Ex. Head of Product at NEAR Protocol, ex. Technical Product Leader at Microsoft
"In the era of RAG-based systems, where reasoning relies on both learned weights and external knowledge, the true bottleneck shifts from compute to the precision, structure, and reliability of training and retrieval data — because no generation is better than the information it depends on."
An opportunity to start a new income stream
and become a part of the future in AI.
For AI Owners
Cost reduction
for quality control
Flexibility
to adapt to special tasks
Real people
driven by a large and active user base
Rapid results
driven by a large and active user base
High-quality outputs
ensured by dual oversight
For Users
Fast access
via Telegram
Skill development
in machine learning
Earn tokens
to adapt to special tasks
DAO voting
to adapt to special tasks
Flexible roles
to adapt to special tasks
Making AI Work for Owners and Users
Mobile App
User selection
User selection
Each user goes through an advanced training process before contributing to model training
Simple evaluation
Simple evaluation
A user-friendly interface allows faster more efficient and accurate task completion.
User Rating
User Rating
Task quality impacts user ratings, affecting the difficulty of future tasks and the earnings for them.
Task Review
Task Review
Completed tasks are checked by experienced users, ensuring minimal error rates
The highest quality learning
User selection
Each user goes through an advanced training process before contributing to model training
Simple evaluation
A user-friendly interface allows faster more efficient and accurate task completion.
User Rating
Task quality impacts user ratings, affecting the difficulty of future tasks and the earnings for them.
Task Review
Completed tasks are checked by experienced users, ensuring minimal error rates.

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