As a first step, data is collected to gather relevant data that is needed to train the AI model.

Next is Data Preprocessing where we remove noise, handle missing values, and convert data into a suitable format for further processing.

For supervised learning, the AI model will be trained using a labeled dataset. The input data is paired with the correct output.

After training, we evaluate the model on a separate dataset that it hasn’t seen before.

Now, the AI model processes new incoming data (queries) and generates responses in real-time.

Our Artificial Intelligence as a Service (AIaaS) will also collect user interactions and feedback to improve the model’s performance over time.

We employ necessary regular maintenance and updates to ensure that the model remains accurate and relevant.

Only when the model performs well on the test sets, it will be deployed to a production environment. Our AI development services team will integrate the model into a web application, a mobile app, or another system.