Start Training AI
Stop Training AI on the Real Word
on Real World
Collected by real humans in real environments
through mobile devices.
195 countries
195 countries
195 countries
2+ million mobile devices
2+ million mobile devices
2+ million mobile devices
Zero synthetic shortcuts
Zero synthetic shortcuts
Zero synthetic shortcuts
Start Training on the Real World
AI training data from the physical world - collected by real humans in real environments through mobile devices. Audio. Video. Images. GPS. Sensor data.
Start Training on the Real World
AI training data from the physical world - collected by real humans in real environments through mobile devices. Audio. Video. Images. GPS. Sensor data.
Start Training on the Real World
AI training data from the physical world - collected by real humans in real environments through mobile devices. Audio. Video. Images. GPS. Sensor data.
Start Training on the Real World
AI training data from the physical world - collected by real humans in real environments through mobile devices. Audio. Video. Images. GPS. Sensor data.
Trusted by
You are in good company:
You are in good company:
Your AI Was Trained on the Internet. The Real World Doesn't Look Like That.
Your AI Was Trained on the Internet. The Real World Doesn't Look Like That.
Most AI models train on digital-first data - web-scraped images, synthetic audio, studio video, database coordinates.
The problem? Real-world deployment looks nothing like this.
Your model fails when it encounters:
Car traffic in Los Angeles vs. Hong Kong
Car traffic in Los Angeles vs. Hong Kong
Ground level data in Stockholm vs. Tokyo
Ground level data in Stockholm vs. Tokyo
Code-switching between languages
Code-switching between languages
Poor lighting and cluttered spaces
Poor lighting and cluttered spaces
Cultural variations in behavior and object usage
Cultural variations in behavior and object usage
0%
0%
over 90% of AI models lose reliability in production environments, primarily due to unmonitored data drift and feedback gaps affecting system-level accuracy.
0%
0%
over 90% of AI models lose reliability in production environments, primarily due to unmonitored data drift and feedback gaps affecting system-level accuracy.
0%
0%
over 90% of AI models lose reliability in production environments, primarily due to unmonitored data drift and feedback gaps affecting system-level accuracy.
Rwazi provides AI Datasets based
Rwazi provides AI Datasets based
Rwazi provides AI Datasets based
on the Real World.
on the Real World.
on the Real World.
What We Collect:
What We Collect:
What We Collect:
Audio
Native speakers in 100+ languages and 195+ countries, real accents/dialects, environmental noise, edge cases
Audio
Native speakers in 100+ languages and 195+ countries, real accents/dialects, environmental noise, edge cases
Video
Real environments, natural lighting, human behavior in authentic contexts
Video
Real environments, natural lighting, human behavior in authentic contexts
Computer Vision
product variations, real rooms, real fridges, real clutter, diverse lighting
Computer Vision
product variations, real rooms, real fridges, real clutter, diverse lighting
GPS
Real movement patterns, traffic, urban/rural diversity
GPS
Real movement patterns, traffic, urban/rural diversity
Sensor Data
Accelerometer, gyroscope, magnetometer, ambient light, proximity, LiDAR, Proximity Sensor, Ambient Light Sensor, Barometer, Radio (Cellular, Wi-Fi and Bluetooth)
Sensor Data
Accelerometer, gyroscope, magnetometer, ambient light, proximity, LiDAR, Proximity Sensor, Ambient Light Sensor, Barometer, Radio (Cellular, Wi-Fi and Bluetooth)
Broad Range of Digital Devises
Mobile phones, drones, smart glasses, wearables we deploy whatever captures your reality best.
Broad Range of Digital Devises
Mobile phones, drones, smart glasses, wearables we deploy whatever captures your reality best.
Why Mobile-First Matters
Why Mobile-First Matters
Device diversity
(flagship to budget phones)
Device diversity
(flagship to budget phones)
Device diversity
(flagship to budget phones)
Device diversity
(flagship to budget phones)
Real environments
(cafes, streets, homes, factories)
Real environments
(cafes, streets, homes, factories)
Real environments
(cafes, streets, homes, factories)
Real environments
(cafes, streets, homes, factories)
Global scale, local authenticity
(195 countries, cultural context)
Global scale, local authenticity
(195 countries, cultural context)
Global scale, local authenticity
(195 countries, cultural context)
Global scale, local authenticity
(195 countries, cultural context)
Internet/Synthetic Data
Sterile empty streets, controlled studio shots, artificial perfection, zero real-world chaos
VS
Real Word
Messy lighting, varied angles, background noise, authentic environments. Chaotic crowds, real mess, actual life
Internet/Synthetic Data
Sterile empty streets, controlled studio shots, artificial perfection, zero real-world chaos
VS
Real Word
Messy lighting, varied angles, background noise, authentic environments. Chaotic crowds, real mess, actual life
Internet/Synthetic Data
Sterile empty streets, controlled studio shots, artificial perfection, zero real-world chaos
VS
Real Word
Messy lighting, varied angles, background noise, authentic environments. Chaotic crowds, real mess, actual life
Internet/Synthetic Data
Sterile empty streets, controlled studio shots, artificial perfection, zero real-world chaos
VS
Real Word
Messy lighting, varied angles, background noise, authentic environments. Chaotic crowds, real mess, actual life
AI Datasets
Use Cases
AI Datasets
Use Cases
Embodied AI & Robotics
Navigate real-world chaos - humanoid robots, delivery bots, warehouse automation, agricultural robots. Data from 195 countries showing how humans move and organize spaces.
Embodied AI & Robotics
Navigate real-world chaos - humanoid robots, delivery bots, warehouse automation, agricultural robots. Data from 195 countries showing how humans move and organize spaces.
Embodied AI & Robotics
Navigate real-world chaos - humanoid robots, delivery bots, warehouse automation, agricultural robots. Data from 195 countries showing how humans move and organize spaces.
Autonomous Vehicles
Drive beyond urban highways - chaotic traffic patterns, pedestrian behavior, weather variations from real driving conditions globally.
Autonomous Vehicles
Drive beyond urban highways - chaotic traffic patterns, pedestrian behavior, weather variations from real driving conditions globally.
Autonomous Vehicles
Drive beyond urban highways - chaotic traffic patterns, pedestrian behavior, weather variations from real driving conditions globally.
Retail & E-Commerce
See shelves as they actually are - poor lighting, clutter, packaging variations across 195 countries. Shelf monitoring that works everywhere.
Retail & E-Commerce
See shelves as they actually are - poor lighting, clutter, packaging variations across 195 countries. Shelf monitoring that works everywhere.
Retail & E-Commerce
See shelves as they actually are - poor lighting, clutter, packaging variations across 195 countries. Shelf monitoring that works everywhere.
Voice AI
Understand humans globally - 100+ languages, real accents, code-switching, background noise from authentic environments.
Voice AI
Understand humans globally - 100+ languages, real accents, code-switching, background noise from authentic environments.
Voice AI
Understand humans globally - 100+ languages, real accents, code-switching, background noise from authentic environments.
Healthcare AI
Serve diverse scenarios - medication packaging photos, medical terminology audio, clinical environment photos, health instruction transcription.
Healthcare AI
Serve diverse scenarios - medication packaging photos, medical terminology audio, clinical environment photos, health instruction transcription.
Healthcare AI
Serve diverse scenarios - medication packaging photos, medical terminology audio, clinical environment photos, health instruction transcription.
Smart Cities & IoT
Work in real urban environments - traffic in unorganized systems, informal settlements, cultural differences in space usage
Smart Cities & IoT
Work in real urban environments - traffic in unorganized systems, informal settlements, cultural differences in space usage
Smart Cities & IoT
Work in real urban environments - traffic in unorganized systems, informal settlements, cultural differences in space usage
AR/VR & Spatial Computing
Understand real spaces - home layouts across cultures, lighting variations, furniture density globall
AR/VR & Spatial Computing
Understand real spaces - home layouts across cultures, lighting variations, furniture density globall
How It Works
How It Works
1
Define Requirements
Use case, modalities, geographies, volume. Quote in 48 hours.
2
Mobile Collection
2M+ contributor network, real-time validation, multi-tier QA.
3
Annotation
Domain experts, custom schemas, human-in-the-loop validation.
4
Delivery
Cloud delivery (S3/GCS/Azure), full provenance docs.
1
Define Requirements
Use case, modalities, geographies, volume. Quote in 48 hours.
2
Mobile Collection
2M+ contributor network, real-time validation, multi-tier QA.
3
Annotation
Domain experts, custom schemas, human-in-the-loop validation.
4
Delivery
Cloud delivery (S3/GCS/Azure), full provenance docs.
1
Define Requirements
Use case, modalities, geographies, volume. Quote in 48 hours.
2
Mobile Collection
2M+ contributor network, real-time validation, multi-tier QA.
3
Annotation
Domain experts, custom schemas, human-in-the-loop validation.
4
Delivery
Cloud delivery (S3/GCS/Azure), full provenance docs.
1
Define Requirements
Use case, modalities, geographies, volume. Quote in 48 hours.
2
Mobile Collection
2M+ contributor network, real-time validation, multi-tier QA.
3
Annotation
Domain experts, custom schemas, human-in-the-loop validation.
4
Delivery
Cloud delivery (S3/GCS/Azure), full provenance docs.
How Rwazi Compares to
Scale AI, Appen, and Clickworker
How Rwazi Compares to
Scale AI, Appen, and Clickworker
Starter
Rwazi
Scale AI
Appen
Clickworker
Feature
Real-World Mobile Collection
Real-World Mobile Collection
Real-World Mobile Collection
Physical-world
across 195 countries
Digital-first
Limited physical
Inconsistent
Mobile-native
Mobile-native
Mobile-native
2M+ mobile devices
Desktop focus
Limited
Web-based
Geographic coverage
Geographic coverage
Geographic coverage
195 countries
US/Europe bias
Limited coverage
70 countries
Data modalities
Data modalities
Data modalities
Audio, video, image, GPS, sensor
Images/text
Audio/text
Basic tasks
Pricing transparency
Pricing transparency
Pricing transparency
Transparent tiers
Opaque ($93K)
Complex
Transparent tiers
Quality
Quality
Quality
Multi-tier validation
98%+ (claims)
Variable
Low pay risk
Compliance
Compliance
Compliance
GDPR, SOC 2 in progress
FedRAMP, SOC 2
SOC 2, ISO 27001
Limited
Scale AI plays in digital-first AI - screens, internet data, synthetic generators.
Rwazi plays in physical-world-first AI.
2 million mobile users collecting authentic data from real environments in 195 countries. Making your models more competitive with real life data
Real-Word Mobile Collection
Mobile-native
Geographic Coverage
Data Modalities
Pricing Transparency
Quality
Compliance
Rwazi
Scale
Appen
Clickworker
Real-Word Mobile Collection
Mobile-native
Geographic Coverage
Data Modalities
Pricing Transparency
Quality
Compliance
Rwazi
Scale
Appen
Clickworker
Enterprise-Grade Quality You Can Trust
Enterprise-Grade Quality You Can Trust
Multi-tier validation
(automated + human)
Multi-tier validation
(automated + human)
Multi-tier validation
(automated + human)
Consensus annotation
Consensus annotation
Consensus annotation
Continuous monitoring
(drift detection, feedback loops)
Continuous monitoring
(drift detection, feedback loops)
Continuous monitoring
(drift detection, feedback loops)
Transparent Pricing
No Lengthy Sales Cycles.
Transparent Pricing
No Lengthy Sales Cycles.
Data Complexity
Consumer opinions vs. loT sensor streams
Data Complexity
Consumer opinions vs. loT sensor streams
Data Complexity
Consumer opinions vs. loT sensor streams
Collection Difficulty
US fridge photos vs. Eritrean geopolitical views
Collection Difficulty
US fridge photos vs. Eritrean geopolitical views
Collection Difficulty
US fridge photos vs. Eritrean geopolitical views
Volume Required
100 samples vs. 1M responses
Volume Required
100 samples vs. 1M responses
Volume Required
100 samples vs. 1M responses
Volume discounts available.
Volume discounts available.
Volume discounts available.
Ready to Connect?
Stop Training on the Internet.
Start Training on the Real World.
Physical-world AI data from 195 countries. Built for systems that exist outside of labs.
Trusted by Fortune 500 companies
Stop Training on the Internet.
Start Training on the Real World.
Trusted by Fortune 500 companies
Stop Training on the Internet.
Start Training on the Real World.
Physical-world AI data from 195 countries. Built for systems that exist outside of labs.
Trusted by Fortune 500 companies
Stop Training on the Internet.
Start Training on the Real World.
Trusted by Fortune 500 companies
Trusted by
You are in good company:
You are in good company:
Frequently Asked Questions
Frequently Asked Questions
What is an AI dataset?
An AI dataset is a structured collection of real-world data used to train, validate, and improve machine learning models. Unlike synthetic or web-scraped data, Rwazi's datasets capture authentic human behavior, environmental context, and physical-world complexity across 195 countries - giving your models the ground truth they need to perform in production.
Why does dataset quality determine model success?
Garbage in, garbage out. Your model is only as good as the data it trains on. Low-quality datasets scraped from the internet, generated synthetically, or collected in controlled labs create models that fail in real-world conditions. Quality datasets reflect actual human behavior, environmental diversity, and edge cases your model will encounter in production. That's the difference between 95% accuracy in testing and 60% in the field.
How does Rwazi capture and validate data?
Rwazi combines human intelligence and automated systems to capture real-world data at scale. Our global network of 2+ million mobile users collects information from verified sources across 195 countries, while advanced validation layers and expert annotators ensure accuracy, consistency, and reliability at every stage. Multi-tier validation, consensus annotation, and continuous monitoring keep quality high.
Which data formats are supported?
We support standard formats including CSV, JSON, XML, XLSX, and TXT, as well as custom formats tailored to your project's needs. All datasets are delivered in clean, ready-to-use structures optimized for AI training and compatible with major ML frameworks like TensorFlow, PyTorch, and scikit-learn.
How long does a dataset project take?
Timelines vary based on project complexity and scale. Typical dataset projects range from 1 to 4 weeks, with smaller collections delivered in just a few days. Our agile workflow allows for iterative delivery so you can begin testing early while we continue data collection. Need it faster? We offer 48-hour rapid deployment for urgent projects.
What is an AI dataset?
An AI dataset is a structured collection of real-world data used to train, validate, and improve machine learning models. Unlike synthetic or web-scraped data, Rwazi's datasets capture authentic human behavior, environmental context, and physical-world complexity across 195 countries - giving your models the ground truth they need to perform in production.
Why does dataset quality determine model success?
Garbage in, garbage out. Your model is only as good as the data it trains on. Low-quality datasets scraped from the internet, generated synthetically, or collected in controlled labs create models that fail in real-world conditions. Quality datasets reflect actual human behavior, environmental diversity, and edge cases your model will encounter in production. That's the difference between 95% accuracy in testing and 60% in the field.
How does Rwazi capture and validate data?
Rwazi combines human intelligence and automated systems to capture real-world data at scale. Our global network of 2+ million mobile users collects information from verified sources across 195 countries, while advanced validation layers and expert annotators ensure accuracy, consistency, and reliability at every stage. Multi-tier validation, consensus annotation, and continuous monitoring keep quality high.
Which data formats are supported?
We support standard formats including CSV, JSON, XML, XLSX, and TXT, as well as custom formats tailored to your project's needs. All datasets are delivered in clean, ready-to-use structures optimized for AI training and compatible with major ML frameworks like TensorFlow, PyTorch, and scikit-learn.
How long does a dataset project take?
Timelines vary based on project complexity and scale. Typical dataset projects range from 1 to 4 weeks, with smaller collections delivered in just a few days. Our agile workflow allows for iterative delivery so you can begin testing early while we continue data collection. Need it faster? We offer 48-hour rapid deployment for urgent projects.
What is an AI dataset?
An AI dataset is a structured collection of real-world data used to train, validate, and improve machine learning models. Unlike synthetic or web-scraped data, Rwazi's datasets capture authentic human behavior, environmental context, and physical-world complexity across 195 countries - giving your models the ground truth they need to perform in production.
Why does dataset quality determine model success?
Garbage in, garbage out. Your model is only as good as the data it trains on. Low-quality datasets scraped from the internet, generated synthetically, or collected in controlled labs create models that fail in real-world conditions. Quality datasets reflect actual human behavior, environmental diversity, and edge cases your model will encounter in production. That's the difference between 95% accuracy in testing and 60% in the field.
How does Rwazi capture and validate data?
Rwazi combines human intelligence and automated systems to capture real-world data at scale. Our global network of 2+ million mobile users collects information from verified sources across 195 countries, while advanced validation layers and expert annotators ensure accuracy, consistency, and reliability at every stage. Multi-tier validation, consensus annotation, and continuous monitoring keep quality high.
Which data formats are supported?
We support standard formats including CSV, JSON, XML, XLSX, and TXT, as well as custom formats tailored to your project's needs. All datasets are delivered in clean, ready-to-use structures optimized for AI training and compatible with major ML frameworks like TensorFlow, PyTorch, and scikit-learn.
How long does a dataset project take?
Timelines vary based on project complexity and scale. Typical dataset projects range from 1 to 4 weeks, with smaller collections delivered in just a few days. Our agile workflow allows for iterative delivery so you can begin testing early while we continue data collection. Need it faster? We offer 48-hour rapid deployment for urgent projects.
What is an AI dataset?
An AI dataset is a structured collection of real-world data used to train, validate, and improve machine learning models. Unlike synthetic or web-scraped data, Rwazi's datasets capture authentic human behavior, environmental context, and physical-world complexity across 195 countries - giving your models the ground truth they need to perform in production.
Why does dataset quality determine model success?
Garbage in, garbage out. Your model is only as good as the data it trains on. Low-quality datasets scraped from the internet, generated synthetically, or collected in controlled labs create models that fail in real-world conditions. Quality datasets reflect actual human behavior, environmental diversity, and edge cases your model will encounter in production. That's the difference between 95% accuracy in testing and 60% in the field.
How does Rwazi capture and validate data?
Rwazi combines human intelligence and automated systems to capture real-world data at scale. Our global network of 2+ million mobile users collects information from verified sources across 195 countries, while advanced validation layers and expert annotators ensure accuracy, consistency, and reliability at every stage. Multi-tier validation, consensus annotation, and continuous monitoring keep quality high.
Which data formats are supported?
We support standard formats including CSV, JSON, XML, XLSX, and TXT, as well as custom formats tailored to your project's needs. All datasets are delivered in clean, ready-to-use structures optimized for AI training and compatible with major ML frameworks like TensorFlow, PyTorch, and scikit-learn.
How do I sign up for the app?
Timelines vary based on project complexity and scale. Typical dataset projects range from 1 to 4 weeks, with smaller collections delivered in just a few days. Our agile workflow allows for iterative delivery so you can begin testing early while we continue data collection. Need it faster? We offer 48-hour rapid deployment for urgent projects.