Natural Language Processing (NLP)
Natural Language Processing Solutions
How it works
The staggering increase in information and computing power over the past few years means that companies are now able to extract detailed insights and trends from their data. Using rigorous applications of Artificial Intelligence/Machine Learning (AI/ML) algorithms, it is now possible to generate human-level performance on complex tasks that would have traditionally been done by humans. Recent advances in this field mean that this revolutionary technology delivers results in a fraction of the time and at a fraction of the cost of using human labour.
Domain Knowledge is a key aspect of AI/ML when applied to businesses. Our process allows companies to transfer their domain knowledge based on a relatively small set of data, and leverage the ability of the trained model to classify fresh data based on a deep understanding of the task at hand.
We start with a corpus of Training Data that has been labelled by the customer using expert domain knowledge; this labelling could include engineering metrics, text similarity, or retail customer information. This data is subject to domain-specific sanitisation measures to ensure that noise is handled appropriately.
Using our expertise in advanced AI/ML algorithms, we design a Model Architecture best suited to the application. Model Training is undertaken using the labelled Training Data by applying best-practice measures to optimise performance on unseen data, resulting in a Custom Model. The evaluation criteria we use ensure that the best combination of bias/variance is achieved.
At the front-end, customers will be able to interact with the Custom Model, which receives Input Data and delivers Insights; typically this is done using an API that can be designed to suit your particular use case. Insights are directly linked to the sort of Training Data considered.
The notion of Continuous Improvement using Feedback is baked into the core of our solution design. Using our custom APIs, users can provide feedback on the accuracy of the model. This data is used for Model Re-training at agreed intervals, with a view to improve model accuracy on an ongoing basis.
The modular structure of our solutions means that they offer a great deal of flexibility. We recognise that the range of AI/ML applications is broad, so to be useful any application must be able to adapt to different use cases while retaining the core infrastructure that offers optimum performance. Our platform can be used for a range of AI/ML applications with minimal customisation.