Scroll Top


Enabling AI as a service for enterprises to drive efficient business management


Concentrate on extracting value from data for a fraction of the cost and time that actually takes.

We helped them with:

  • The development of a data platform that allows individuals and businesses to experiment with AI for a variety of reasons without a huge upfront investment and with less risk.
  • Automating the entire AI/ML Pipeline to enable data scientists to stay focused on deriving value from the data.


Solution scope included:

  • Built on open source: Used Kubeflow, SageMaker and Kubernetes for developing, orchestrating, deploying, and running scalable and portable MLOps pipelines.
  • Embedded ML/DL/AI: Train models with incorporated open source AI learning system libraries to scale knowledge.
  • Elastic deploy options: Build, manage, secure, and scale storage and compute resources quickly and easily in public, private, or your own data centres.
  • Data Analytics in AWS using AWS glue, Redshift, Kinesis etc.
  • Visualization using PowerBI, Tableau and AWS QuickSight.


  • Increase capacity and output (end to end process and automation)
  • Enables data scientists to carry out data and analytics tasks while encompassing visualization, interactive exploration, deployment, performance engineering data preparation, and data access.
  • Improved transparency, explainability and reproducibility.
  • It’s simple to set up new business or operational logic. 
  • The selection, training, and deployment of models are all automated. 
  • New models can be created, tested, and deployed quickly by data scientists.


Privacy Preferences
When you visit our website, it may store information through your browser from specific services, usually in form of cookies. Here you can change your privacy preferences. Please note that blocking some types of cookies may impact your experience on our website and the services we offer.