Crowdsourcing concepts

Tap into the wisdom of the crowd on a large scale.

Keys to clean and accurate training data

Our methodology based on years of research and unique industry expertise can help you successfully tap into the wisdom of the crowd on a large scale. If you want to efficiently use the knowledge of thousands of people to get clean and accurate data for your ML needs, follow our tips for each of these essential steps. 

  • 1. Decomposition
    Break your task down into the smallest possible steps and make each one a separate task.
  • 2. Instructions
    The more comprehensive the instructions, the more accurate the results.
  • 3. Interfaces
    A good interface makes it easy for users to perform the same repeated actions quickly and correctly.
  • 4. Quality control
    Carefully plan and configure a quality control system to ensure high-quality results.
  • 5. Pricing
    Find the optimal price based on speed 
    and quality.
  • 6. Results
    After the pool is finished, aggregate the results and check statistics.

Research Benchmarks

Quality control lies at the heart of crowdsourcing. 
Use our examples as benchmarks to achieve the described levels of quality on popular research datasets.

Research papers

Browse through some of our Research team's latest work.

Useful resources

  • API
    Integrate on-demand global crowdforce & build fully automated ML pipelines.
  • Python library
    We have an open-sourced library with a client that covers all API functionalities.
  • Public datasets
    Use our datasets for your projects or collect your own data that meets your needs.

Get started now

Let's talk about the ideal solution for your data needs.