Natural Language Processing (NLP)
Our Recommender Systems use existing customer data to recommend each customer a curated list of products, increasing sales and reducing customer fatigue. Our solutions are hosted on Azure and accessed through custom APIs.
Maximise Customer Retention by identifying customers likely to switch providers, and offer them targeted retention mechanisms. Our custom models allow this process to be optimised for maximum profit margins.
Use our Sales Forecasting engine to optimise dynamic pricing, roster staff and manage inventory. Our models also determine the dominant causal factors that influence sales, allowing you to fine-tune them to your advantage.
Visualisations help get the most value out of AI/ML model insights. Our concise, interactive dashboards leverage the combination of AI/ML to tell compelling stories, offering granularity to suit every level of the organisation.
Complex, multi-variate regression analysis of engineering problems requires a deep understanding of the fundamental physics and machine learning expertise. Our solutions allow customers to draw inferences from inherently complex numerical data.
Anomaly Detection algorithms offer real-time early warning of machinery failure. When coupled with our interactive dashboards, this approach enables predictive maintenance, preventing unplanned downtime and maximising productivity.
Energy Use is a complex, multi-factor problem that is a key focus of many industries. Our proprietary predictive models rely on our expertise modelling detailed usage scenarios to predict energy usage. Our cutting-edge solutions are built on Azure ML Studio.
Classification is a major feature of engineering applications. Our expertise in supervised and unsupervised learning allows classification of unstructured, multi-factor data, allowing businesses to target specific approaches to individual categories.
Natural Language Processing Solutions
Text Similarity allows you to identify similar sections of text across different documents by extracting the deep semantic meaning of language. Deep Blue AI’s solutions are hosted on Azure and accessed through custom APIs.
Sentiment Analysis studies on social media feedback are becoming crucial as companies employ increasingly innovative marketing campaigns. Our algorithms harness the power of custom NLP solutions to quantify sentiment from unstructured text.
Question Answering (QA) systems enable automated responses to customer queries based on your specific domain, saving tedious helpdesk labour, reducing the response time and improving customer satisfaction.
Classification of text into categories saves money and time on tedious human labour. Our cutting-edge NLP models leverage the power of machine learning to automatically bin text samples based on their content and language.
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.