Best crowdsourcing AI tools

DefinedCrowd is a leading provider of high-quality AI training data. It leverages a global crowd to collect, annotate, and enrich data for machine learning models, specializing in speech, NLP, and computer vision. It offers a fully managed service to help companies build robust and unbiased AI applications at scale.

SmoothRide is an AI-powered platform for cyclists to report infrastructure issues and receive innovative solutions. By crowdsourcing problems like potholes and blocked bike lanes, it uses OpenAI to generate practical, best-practice, and creative advice, aiming to create safer and more livable cities for everyone.

Prolific is a leading platform for collecting high-quality data from a global pool of over 200,000 vetted and engaged human participants. It enables AI developers and researchers to quickly launch studies, train models, and gather reliable human feedback for tasks like data annotation, RLHF, and surveys.

Off/Script is a community-led platform that uses AI to help creators design and launch physical products, like fashion and accessories, without any upfront cost. Submit your ideas, let the community vote, and if popular, Off/Script handles production and logistics, giving you 70% of the sales revenue.

clickworker is a leading crowdsourcing platform that provides high-quality, diverse, and scalable data for training AI and machine learning models. It leverages a global community of over 7 million freelancers to generate, validate, and label data, including images, videos, audio, and text, tailored to specific project needs.

wtfitbot is a free, intelligent tool that identifies objects, plants, animals, and landmarks from your pictures. It uniquely combines AI for instant recognition with the power of crowd intelligence for guaranteed, accurate answers within 8 hours, helping you discover and learn about your surroundings.

LMArena is an open, crowdsourced platform from UC Berkeley researchers for evaluating and comparing leading AI models. Users anonymously test two models side-by-side, vote for the best response, and contribute to a dynamic, public leaderboard. It aims to make AI progress transparent and grounded in real-world human feedback.