Why are we building Ta-da?

As you may already know, data is the biggest expense that AI companies have to manage.

Today, there is a race among builders to create the best artificial intelligence. However, to build a good AI, you need good data to train it. During the training phase, if the data you use has limitations or quality issues, then your AI won't be as good as you need it to be. Therefore, the race is now also focused on acquiring as much data as possible, with the highest quality and accuracy possible. But this comes at a price. According to LXT (1), 59% of an AI budget is spent on data. There are three major problems related to acquiring this precious data:

  1. Ensuring high quality in the dataset. High-quality data refers to data that is accurate, complete, and consistent. Inaccurate or incomplete data can lead to incorrect predictions or decisions. Inconsistencies in the data can also lead to errors in the AI's performance. Therefore, it is essential to have a robust data collection and cleaning process to ensure the dataset is of high quality.

  2. Ensuring data diversity. For example, if you train your speech recognition AI on voices of people that are 25 to 35 years old, your AI will have a hard time understanding kids or more senior populations.

  3. Managing costs. Data collection companies charge a premium for custom data sets. Free or open-source datasets are also an option, but they come with many limitations.

Because data is the fuel for artificial intelligence, we believe that AI companies should have easier access to high-quality data at a better price. This is WHY we are building Ta-da.

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