Productivity is a core competitive advantage in today’s rapidly changing business environment. Large enterprises (or AI departments within large enterprises) have their own labeling teams. They do not need to cut costs or increase productivity but require high security and accuracy. However, small to medium-sized AI solution companies are trying to find solutions to survive in a highly competitive market. They are striving to maximize resources, streamline operations, and increase work productivity with the aim of optimizing costs while maintaining stable quality. In the long run, this also brings about price competitiveness compared to larger companies.

One of the important aspects of evaluating product quality after processing is defining the dataset. A dataset is perceived through the eyes of many who process it. When multiple people’s perspectives and minds are involved in data processing, they will have different observations and definitions. Although they have been trained before undertaking the task, differences between individuals can still occur. In addition, each person’s training time and awareness can vary, resulting in minor differences in the results. These data processing employees do not all have long-term experience or sometimes only handle 1-2 projects, therefore the consistency of the dataset is reduced. So, when conflicting issues of productivity and consistency arise, how can a team come to a consensus?

With diverse supplies from professional agencies, small and medium-sized AI solution companies can overcome all the challenges mentioned above thanks to the development of systematic processes in data annotation. Data outsourcing companies provide you with high-quality data at an optimized cost that meets your requirements.

The scale and location of allow us to source and recruit the necessary number of candidates in a short period of time, and we can expand the scale of your team without compromising the quality of work provided. Contact us today to learn more about how we can help you.