Why Sigma?


The key: Efficient processes & optimal balance between human resources and ML-assisted tools

We all want the highest quality with the fastest turnaround at the lowest price. Others say you need to choose two. We believe you deserve all three.

Sigma provides the best prices through a comprehensive optimization approach.

Making the process of data collection or annotation cost-efficient requires the optimization of every single step in the process. However, optimization is constrained by aspects of the data collection/annotation such as quality, compliance with privacy regulations, security, or speed, which cannot be compromised.

Sigma provides affordable data services by optimizing the following aspects:

  • Team created for the project. Each data collection or annotation project has its specific characteristics. Sigma selects the team based on its vetted candidates’ skills and knowledge and their previous experience in similar projects.
  • Efficient tools. Tools have to be intuitive and, at the same time, they have to be designed to facilitate the most efficient workflow while minimizing the probability of making mistakes. The tools need to be implemented to avoid latencies so that there is no downtime, and they must allow for an optimal combination of human and machine learning capabilities. Finally, tools have to facilitate the quality assessment process by having specific QA features.
  • Efficient processes. All the processes must flow with no interruption. It is also essential to understand and take into account human limitations to organize the work accordingly. Sigma considers factors such as human fatigue in the design of the tools’ user interfaces and the work organization.
  • Comprehensive QA methodology. High-quality training data requires human intervention at some level. Therefore, achieving high quality is, mainly, a human factor issue. Understanding what humans are good at and the sources of errors is crucial to designing the work procedures, the tools, the optimal combination of ML-assisted tools and human resources, and the QA methodologies.