PHOENIX ASIA - the digital company
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PhoenixAI is dedicated to use the science behind the data science to yield positive outcomes for businesses.
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PhoenixAI is dedicated to use the science behind the data science to yield positive outcomes for businesses.
or
We are a group of technology enthusiasts with deep rooted acumen in driving telecommunications business for more than 25 years, working with all the worlds biggest software and telecom companies. With planned development centers in Vietnam and India we wish to utilize the top talents and provide them with a platform to use data science the way it should be used.
We have a solid systematic approach towards delivering projects.
We have put in years of research in domains with proven business cases and also partnered with several industry experts to validate our assumptions. To learn more follow, Home, About, and FAQ.
We have developed measures and KPIs from rich experience and industry standards which is used at the initiation phase, aided by the techniques and science of various technology trends.
Our objective is to explore, sort and analyze megadata from various sources in order to take advantage of them and reach conclusions to optimize business processes or for decision support.
At this stage the experts present various data points related to the execution of the project. Data Structure, WBS and execution of the project is explained to the business to get buy ins on the execution strategy
At this stage we bring in huge amount of expertise in data mining to be able to extract relevant information from the sea of data and would extract proper data segregating it from the noise.
Once the evaluation phase is completed, the strategy to execute and all data point and framework selections are completed. KPIs and data structure clearly defined.
In the final stages use the mathematical science and use standard or adopted models to extract differentiated data outcome taking steps towards predictive outcome.
All models identified earlier through the data points are fed the data received through the mediation layer and trained on multiple iterations. The same outcome once it reaches a certain level of stability is used for a brown out period to test the predictions. Once the predictions start building value to the customer the model is kept under observation for any borderline anomalies and then handed over to the customer
