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Analytics

– Recommendation Engines, especially for Customer Experience

 

– Cluster Algorithms for customer segmentation and targeted campaigns

 

– Data and Text Mining for analysis of underlying circumstances and solutions, for example in automatically quantifying and documenting customer queries and related answers

 

– Regression Analytics for trend analysis and deviation from mean, for example in quality assessment

Artificial Intelligence

The design and implementation of related Machine Learning models and solutions is a central focus within our projects.

 

The underlying software tools and ecosystem is typically open-source, or a market-leading solution, as preferred by the client.
Projects have been completed or initiated with:

 

– PredictionIO and Spark MLlib

– Scala for development within Spark MLlib

– Python and a variety of libraries

– Tensorflow 2.0

– Visual Analytics tools including Knime and Rapidminer

Enterprise applications may be implemented within the client ecosystem, or in any Cloud environment. Advanced consultancy is available system architecture issues, including highly scalable systems with Docker and Kubernetes.

Prior to 2016, a primary focus of Applaud was the development of cross-platform mobile applications, for iOS and Android, with one underlying source code. Given this background, current applications continue to include mobile front-end systems for mobile devices, as well as web-browsers.

Projects are completed following established Agile development methodology, or as turn-key development assignments. Project durations vary between three months and multiple years. In many cases, the Applaud teams works closely with the client organization, providing the consultancy, business analyst and software competencies requested by the client.

Collaborative Filtering in Recommendation Engines

DevOps Cycle

Supervised Machine Learning

Cloud Architecture: Docker Container Orchestration with Kubernetes

Tools and Systems

Great things in business are never done by one person. They’re done by a team of people.