Our client is an eCommerce Analytics platform. With tens of thousands of data points, the company was looking for a scalable and fast way to tag its data sets. The company started building a dataset with 2 internal resources dedicated to data processing but wanted to improve the latency while ensuring the quality of the output tags. They realized the super.AI solution can help them achieve both objectives.
We helped the customer categorize products and identify the brands from product items with our NER data program. We used the original processed data as training data for the additional projects we ran with them.
We were able to deliver a 99.1% accuracy in the POC. Based on this, the customer was able to increase the amount of data they were processing. On top of the significant quality, we delivered the processed data to the customer ahead of the time. Thanks to the increase of automation, they were also able to gradually reduce the costs.
Using super.AI, we were able to significantly accelerate our data processing efforts. We were worried that increasing the automation would reduce the overall quality of the dataset, but Super.AI was able to deliver human quality at AI speeds. We’ve gotten almost perfect accuracy