AI  Data Engineering

Our AI Data Engineering Services refer to a set of processes, tools, and techniques that are used to collect, store, process, and transform data in a way that supports the development and deployment of Artificial Intelligence models and applications.

Data Generation

Getting the right data in place means that sometimes datasets will need to be created from scratch (manually or automatically). Our language and domain experts will help you create the best dataset for your use case for any language at a scale.

Data Labeling

Labeled data is necessary for a supervised learning approach in Machine learning models training, where the model is trained using labeled examples in order to learn to make predictions on new data. Our powerful AI Data Platform allows us to label data 10x faster in a combination of human-in-the-loop, machine learning and rule-based approach.

Data Augmentation & Synthetic

Sometimes real-world data is scarce or difficult to obtain. We can increase the size and diversity of the training data by transforming or modifying existing data to create new synthetic data points or creating new fresh data by a combination of our language and domain experts and engineering techniques.

Data Quality Assurance

Data quality is critical for a successful Machine Learning implementation. We have built a ton of data quality features within our AI Data Platform which allows our Data QA teams to ensure the accuracy and quality of our client’s data.

Data Bias Detection & Mitigation

ML models are as biased as the data they are trained on and are affected by human biases such as sexism and racism, or any other harmful social biases, even if in subtle ways. Our ML team builds custom test frameworks with a battery of unit tests to detect social biases within language based models.

Data Anonymization

Data anonymization allows the data to be used to train ML models or to incorporate it in your data pipeline without compromising personal information. Our engineers are able to identify and remove sensitive information from datasets, such as names and addresses, in order to protect the privacy of individuals