CASE STUDY Partners With Google to Deploy Data Pipelines on Kubernetes Engine

Discover how improved time-to-market solutions on Cloud Storage and Cloud SQL to minimize operational overhead.

5 min read | Posted on 2021-03-10 partnered with Google Cloud to revamp time-to-market for hyperlocal AI solutions by running datasets and algorithms on Cloud Storage and Cloud SQL.

The objective was to minimize operational overheads with Google Kubernetes Engine and bridge the language barrier gap in the mapping industry. We did this by offering location-based experience that adapts to customers’ local needs, from last-mile delivery to native language support.

“We built a nimble AI platform on Google Cloud that allows clients to choose plug-and-play modules, depending on their use-cases. While some clients want to detect road names from street-level imagery to improve delivery accuracy, others may want to annotate a politician's name to uncover public sentiment from news articles and forums,” affirmed Ajay Bulusu, co-founder of

Google Cloud Results:

  • Improves speed to market for features released with CI/CD pipelines on Google Kubernetes Engine
  • Delivers 99% uptime on highly available google cloud to minimize survive disruptions to customers
  • Simplifies on-demand cluster creation with dataproc to accelerate compute-intensive workloads

If you are interested in learning more about how achieved these results, here’s the link to the full case study.

To know more about our hyperlocal AI solutions that would fit your business needs, reach out to us.


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