Who we are
Think of Datawizzards as a technological happenstance spawned from iTechHub that was founded by machine learning scientists with the sole goal of integrating machine learning capabilities in software products.
Research. Discover. Innovate.
We are harnessing the powers of AI and applied data science to solve practical problems.
Data-driven technological capabilities that enable efficient, reliable and productive decision-making processes.
To understand the theoretical aspects of AI and Data Science techniques and apply them to practical problems facing Africa and the world at large.
Building AI-driven data products at scale.
Do you struggle to realize the value of technology in your business? Is it too costly? Are you using the right tools? Let us figure all that out for you.
Do you understand what your data means? Are you collecting and storing data properly? What about your individual customer needs? Forget BI, think AI.
Are you struggling with efficiency? Or perhaps convenient processes? What about the cost of your operations? You need end-to-end automation, let us help.
You want a bot? Perhaps some API integration with cloud services? Call center operations, and customer service in general, is a deep learning problem.
How can we visualize data in a way that generates significant insights? Stop looking at the obvious, find the mysteries in your data. We subscribe to the four principles of analytics, from diagnostic to predictive!
Planning on harvesting the power of cloud platforms? Azure, AWS, GCP and the likes? We can help you.
22
PROJECTS COMPLETED4
YEARS IN SERVICE5
TOTAL CLIENTS16
AWARDS WONThinking practically about Data and Deep Learning.
2018 Top15YoungGeeks.
#IgniteHack meeting with the teams, @qdelwa giving guidance alluding that she’s glad Datawizzards has a registered business to help set focus on their solution. The Public Service Hackathon took place in September led by @MinAyandaDlodlo.
Datawizzards produced a working model of a real-time fraud detection solution, using machine learning to flag anomalous behaviour.