Our solutions

  • Cognitive/Intelligent Solutions
  • Feature Engineering
  • AI/ML at scale
  • Disrupt with AI

AI and cognitive computing are often used interchangeably. However there are some key differences.

AI focuses on using algorithms to empower machines to augment human thinking. Its about making computers solve complex problems by learning from data

Cognitive systems mimicks human reasoning behavior.  They typically use self learning technologies such as data minining, image and voice recognition, natural language processing amongst many others

At CloudBC our teams are equipped with key skills towards dealing with unstructured data and solution building in NLP, image recognition and audio analytics.

Feature engineering involves the use of domain knowledge to synthesize variables from raw data.  These variables carry enough signal strength to impact the predictions in a significant way.

As part of the exploratory data analysis, our data scientists work with domain experts to identify, select, transform and include appropriate features to enhance model performance.


Scalable machine learning involves building systems and component so that as a whole, it helps reach towards the solution rapidly. The following steps are often adopted in building a scalable AI/ML solution

  1. Picking the right framework/language.
  2. Using the right processors.
  3. Data collection and warehousing.
  4. The input pipeline.
  5. Model training.
  6. Distributed machine learning.
  7. Other optimizations.
  8. Resource utilization and monitoring.

Without a doubt AI is a disruptive technology as it transforms the digital technology at a large scale by automating various tasks which are manually inefficient.

Leaders in all industries need to be thinking about whether, how, and where they should be investing in AI-based technologies. This means understanding the available AI technologies and then analyzing existing and potential business processes, staffing models, data assets, and markets to identify ways that AI can be used to improve quality, speed, and functionality, as well as to drive top-line revenue growth. Now is the time to have this discussion.

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