AI Training Data: Crowdsource data from real people to power smarter AI
Gather crowdsourced AI training data from hundreds or thousands of targeted real world users to refine algorithms and train AI to build faster, smarter products.
Target on location, demographics (age, gender, income, etc), devices, employment, and using custom screening questions.
Run tests to collect engagement data in your product, through BetaTesting (videos, images, spreadsheets), or through any third party website.
Our platform allows you to easily manage large groups of testers through test processes that span days or weeks.
Get unique real-world data from any audience
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Crowdsource real data in real environments to superpower your AI training and build a better experience for your users.
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Collect any type of AI training data through any real-world process, including in-app behavioral data, content (images, writing, videos, voice, etc), device data (e.g. through SDKs) and more to build better AI models faster than ever.
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Easily collect data for any process, even those that take place over hours, days, or place.
Design custom workflows for any data gathering process
Create any test process, even those that span multiple days or weeks with our Test Workflow builder.
Collect data in your app or through our platform
Get the exact data you need in whatever format you need it. Have testers use your app to share data for your models, such as voice, location, biometrics, and more. Or use our platform to crowdsource images, videos, device logs, voice, spreadsheets, and more - from anywhere in the world.
Manage large groups of testers without any headaches
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Optionally require that all testers agree to NDA terms
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Re-target past testers, build a tester community, and create cohorts and groups via tags
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Send messages and communicate with testers one-to-one or send broadcast messages en-masse
Case Studies: Collect human-powered data for AI Models
BetaTesting gathered in-car images from hundreds of users to power AI and machine learning for global automotive leader Faurecia
BetaTesting recruited hundreds of targeted users with a wide variety of car models around the world.
Participants provided photos several times per day over a week at specific angles and in various weather conditions.
Data included tagging each image to allow for automated processing with AI and machine learning software
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Iams collected pictures and videos of dog nose prints to improve its app to help rescue dogs
Just as human fingerprints are one-of-a-kind, each dog’s nose print is distinct due to its unique pattern of ridges and grooves.
BetaTesting recruited owners of a wide variety of dog breeds, and the app was tested in various lighting conditions to improve the nose print detection technology
Hundreds of pictures were collected to improve AI models to accurately identify each dog’s nose
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Zocdoc sourced a wide mix of testers to interact with their AI virtual assistant
A mix of testers with different backgrounds and wellness concerns were recruited
Testers went through the website workflow to book appointments
Users interacted with an AI virtual assistant to allow the company to collect data and refine AI models with real audio and conversational data
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