AI  Engineering Services

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.

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.

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 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.

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 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

Custom ML systems are trained to recognize certain types of patterns that help automate multiple different workflows. Our ML team helps you identify the right algorithm and/or leverage out-of-the-box cutting edge ML solutions to tackle your specific business case.

Build and launch your NLP application for international markets. Our language based NLP and Linguist teams can help adapt your ML algorithm to any language and market to increase your audience.

ML model audits are important because they help to ensure that an ML model is accurate, reliable, and free of bias. We evaluate and assess the performance, reliability, and bias of an ML model. This typically involves collecting and analyzing data on the model's performance, comparing it to benchmarks or other models, and identifying areas for improvement.

Deploy your ML models in Azure, AWS, Google Cloud or other Serverless GPU inference for ML models cloud provider. We help architecting, planning, implementing and operating your Models and workloads on the cloud for optimum performance and accuracy.

Tools, Dashboards & Integrations. AI/ML projects need to be encapsulated within software frameworks to extract the most out of them. Our team of full stack software developers will help you build the tools, dashboards, apps or software integrations needed to maximize the value of your AI product.

AI needs to be fully integrated with your existing systems to harness the power of ML automation Our engineering team will develop custom APIs and App hooks to maximize the benefits of the AI solutions right into your ecosystem

Putting the right infrastructure and team in place will help automate the testing and deployment of your ML models. Our Quality team, integrated by data scientists, quality engineers, analytic QAs and Software Developers in Test will build any custom set of tools to assure a high level of performance across products that will help you stay ahead of the competition