top of page


Extending the capabilities of Healthcare with AI and Sparkflows

Sparkflows accelerates healthcare delivery by improving patient outcomes, reducing costs, and enhancing the overall quality of care through a Best-In-Class AI-powered Business-driven

Self-Service Data & AI  Solution building Platform.


Healthcare Business Opportunity

Healthcare Business needs to address Data challenges and adopt AI-driven culture

Business Challenges
IT Challenges
Negative Impact
Screenshot 2023-08-08 at 3.15.58 PM.png

Incomplete view of patient data

Lack of scalable Integration with Cloud Clusters and Data Lakes

Inability to handle growing no of use cases

Screenshot 2023-08-08 at 3.35.38 PM.png


Screenshot 2023-08-08 at 3.18.41 PM.png

Fraudulent Claims

Incomplete Security and Governance Models

Lack of Multi-persona Collaboration

Screenshot 2023-08-08 at 3.38.04 PM.png

Delay in Time-to-market

Screenshot 2023-08-08 at 3.21.14 PM.png

Disproportionate Member Costs

Unmanageable Cost of Data Movement and Process Execution

Rising costs of skilled resources

Screenshot 2023-08-08 at 3.40.12 PM.png

Missed Business


Screenshot 2023-08-08 at 3.24.09 PM.png

Inability to detect adverse effects

Performance and scalability Issues for migration , integration and modernization

Data outpacing legacy systems

Screenshot 2023-08-08 at 3.40.46 PM.png

Declining Revenue

Multi-Persona Self-Service
Data Science Platform

Leverage the Self-Service Push-Down Data-Centric AI Platform on Data Lakes to build Business-driven Point-n-Click Apps, Pipelines, and Workflows for quickly turning large amounts of data into actionable insights through minimal Touchpoints

Healthcare Data Products

Build solutions using pre-built templates


Sparkflows at a large Healthcare Company

Company Objective

A very large healthcare insurance company operating in all states of US had a requirement to speed up business case development and boost overall productivity and engineering efficiencies. 

The data engineers and analysts in each region need to be empowered with self-serve advanced analytics and deliver top quality results quickly.

Business Use Cases

Customer had identified several data engineering and Big Data analytics science use cases including :

  • EHR data analysis

  • Patient Admission record analysis

  • Claims data analysis


Complex & Time-Consuming Engineering process

The current data engineering processes are somewhat complex and time consuming. They involve a lots of local coding and little automation. Oftentimes lacks regulated process. Cloud automation and Compute Push-down are not easy to implement. 

Screenshot 2023-09-15 at 5.59.31 PM.png

Hard for Data Analysts, Scientists, Engineers and Product to Collaborate

In the absence of a powerful Self-Service Co-development Platform, it is impossible for the users to collaborate.

Screenshot 2023-09-15 at 6.01.24 PM.png

Getting Value Out of Data

Not enough Data Analytics and ML applications are currently in production. Hence, they need to be quickly built.

Screenshot 2023-09-15 at 6.02.27 PM.png

Inability to scale out use cases

Need to scale from local machine to cloud. Need to boost productivity and reduce time-to-market while accelerating the solutions.


Fast development and deployment on top of AWS Datalake and EMR. Quick delivery of Business use cases and fast Time-to-Market

Sparkflows was installed in the secure air-gapped cloud environment.

Admin quickly configured the secure connections with AWS and RDS.

Data Engineers could quickly connect to Data Lakes and perform scalable ETL, automatically generate distributed code.

Users could create the visual point & click workflows in minutes using existing templates.

They could read data from S3, DynamoDB, RDS etc., transform them and perform extensive Data Quality and generate Reports at scale.

The jobs would run distributed on EMR and automatically scheduled

through Airflow and hence could scale easily.

Using the 450+ functions in Sparkflows and 80+ ML algorithms, the

users were quickly able to build accurate ML models.

Multiple teams quickly collaborated with each other by sharing projects

and workflows in secure manner and by pushing workflows to Github.

Domain knowledge could be easily persisted and reused.

Business teams quickly built Analytical Apps and powerful reports in

the same project.



Increase in User Adoption


Reduction in Time to Market


Increased Collaboration


Higher Accuracy of Models


Higher Sales of SKUs which are

difficult to sell otherwise

bottom of page