Solution overview
The science of Artificial Intelligence (AI) uses machine learning algorithms to help computers learn from existing data in order to forecast future behaviours, outcomes and trends. Machine learning enables computers to act without being explicitly programmed, augmenting – rather than replacing – human capabilities.
ANEGIS build AI applications that intelligently sense, process and act on information, to automate business processes and increase speed and efficiency. Leveraging Microsoft AI services and infrastructure including Azure Machine Learning, Cognitive Services and Bot Framework, ANEGIS develop intelligent solutions for high value, complex enterprise scenarios.
Machine learning use cases
- Use the Microsoft machine learning platform to analyse Dynamics 365 for Sales data to automatically predict which products to recommend based on customer purchasing trends. Leverage the power of Microsoft Cognitive Services APIs, such as Text Analytics APIs to detect sentiment, key phrases, topics, and language from the text found in Dynamics 365 data.
- Combine Azure Bot Service with Cognitive Services Language Understanding to build powerful enterprise productivity bots. Streamline routine work activities by integrating external systems, such as Office 365 calendar and customer data stored in Dynamics 365.
- Optimise retail product assortment and space planning decisions at the local level. Develop complex predictive models including floor space, product substitutability, customer demographics and purchase habits.
Predictive maintenance with IoT
Predictive maintenance techniques are used to anticipate when an in-service machine will fail, so that maintenance can be planned in advance. Operational data from IoT sensors can be combined with other data sources, such as environmental conditions, to build predictive models.
The predictive strategy uses machine learning in a supervised learning process. This learning process requires data – the full life history of a series of devices – to train an AI model. The more complete the service life data, the more accurate the model. To learn to predict failures, the data must contain instances of those failures. The predictive maintenance strategy aims to replace equipment and parts on a just-in-time basis, avoiding unplanned failures and maximising service life.
Real-time analytics can be set up without having to manage complex infrastructure and software, making it easy to configure dashboards with live metrics such as a machine’s performance, operating conditions, behaviours and failure potential.
