Case study

How We Enabled Faster, Data-Driven Decisions for a US Based Financial Tech & Services Firm by Building a Scalable & Automated Data Ingestion Application

Automated Data Ingestion Application


  • The client wanted to profile their customers and build an ML-based model to recommend allocation across their multiple micro-investment products of Invest Now, Invest for Retirement, Invest for your Kids, and others​
  • Existing allocation process was manual without any user profiling and for most of the cases, customers had to decide their investment split across various investment options​


  • SGA profiled all the client’s customers basis their key features using a hybrid clustering technique that clearly defined the profiles aligning to business requirements​
  • Basis the user profiles, we designed the allocation engine that recommended the % split of investment amount across investment products​
  • The allocation recommendation was real-time, which was initiated during the user registration process for new users and for old users, it was provided under the recommendation section in the app​
  • We deployed the model using MLOps and built an end-to-end data pipeline for model serving and feedback collection from the customer for model refinement​



The client is a US-based financial technology and financial services company that specializes in micro-investing and Robo-investing​


  • The data pipeline was created to fetch data from multiple sources to create the master dataset for feature creation and user profiling​
  • We created a feature store after looking at 800+ metrics currently captured by the client app and filtered out the key metrics for translating into features​
  • The automated and intelligent allocation enabled higher customer satisfaction, owing to ease of usage​
  • Auto-allocation helped in higher investment and usage because of the customized allocation, reducing the hassle of allocation decisions by customers​



  • The profiling helped in providing customed allocation across the customer base for both new and existing ones​
  • The investment across products increased as most of the customers followed the recommended investment split​

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