To achieve our objectives, we have developed a Web3 platform that unites a community of users incentivized to complete a wide range of tasks for various needs. Ta-da acts as the bridge connecting supply and demand across multiple domains, from data collection to content management and social media marketing.
This section outlines the general workflow facilitated by Ta-da through an illustrated example.
In this fictional scenario, a company requires voice recordings from thousands of users reading sentences to train its voice assistant, similar to Google Home or Amazon Alexa.
However, this process is not limited to audio data. The same approach can be applied to various other types of tasks, such as image collection for facial recognition, text retrieval for sentiment analysis, social media engagement activities, and content creation.
Ta-da handles each use case with equal efficiency. Below is an overview of the workflow provided by Ta-da:
Here are some details about each steps:
A company needs English voice recordings to train the new voice assistant it is developing. They can use Ta-da to collect a large amount of high-quality data. The company submits a job on the platform containing all their specific needs, criteria, and a budget to pay users.
Ta-da processes the job, divides it into many micro-tasks, and sends them to users (the producers) who meet the criteria (English-speaking users). In our example, a micro-task could be a simple English sentence to read. Using the application, the user records themselves reading the sentence.
Once the user is satisfied with their recording, they submit it. The data is then sent directly to Ta-da. At this stage, the validity of the data is unknown. A malicious user may have recorded a poor-quality sentence or even nothing at all.
To validate the data, it is sent to several other users from the community who act as checkers. The data is accompanied by a voting form containing questions such as whether there is any background noise and which sentence is being read. Each user listens to the recording and answers the questions on the form.
Once the checker is confident in their answers, they submit them to Ta-da. Based on the checkers' votes, Ta-da computes the outcome, which validates (or rejects) the data.
In our example, the producer's data is validated by the checkers. It is then returned to the company, and all participating users are paid in TADA tokens, using the budget set by the customer.
This example is deliberately simplified and assumes the best-case scenario where the data is valid. However, you might have several questions: What happens if the data is invalid? Who gets paid? What prevents users from submitting low-quality data? The Quality Assurance page answers these questions.
