CarSaver: A complete one-stop solution for all your car buying requirements

Category: Automobile/SaaS

Services: Managed Engineering Teams, AWS Supply Chain solution, DevOps, Cloud Architecture Design, and review

  • Predicted supply risks and reduced late deliveries by 60% 
  • Improved demand forecast accuracy by up to 50% 
  • Reduced the transportation costs by 20%

About CarSaver

CarSaver is an all-in-one platform for buying, leasing, insuring, and upgrading cars from over 30 top brands, built on secure AWS infrastructure to optimize supply chain management.

Problem statement

  • Needed a solution to enable demand forecasting for cars considering economic scenarios and consumer trends. 
  • Create an inventory management solution to manage a wide range of car models across brands
  • Needed an effective transportation and logistics management solution for cars. 
  • To build an efficient and secure multi-tenant solution.
  • Checking the eligible customers for Upgrades and generating deals for them
  • Displaying suitable trades to upgrade their current vehicles
  • Displaying proper finance amounts and calculations
  • For security and compliance purposes, the client wanted to audit each and every incoming request that comes into their network
  • The client wanted to ensure that the database is compliant with data protection policies to ensure compliance with regulatory requirements
  • To make infrastructure management less error-prone.

Proposed Solution & Architecture

  • Simform’s expert team used multiple AWS services to address CarSaver’s demand forecasting needs.
  • Our AWS Supply Chain experts developed a cloud-based solution for CarSaver that unifies data across cars from different brands and provides actionable insights. Further, Simform’s experts used AWS Lambda functions to automate inventory management by configuring custom triggers. 
  • Simform’s AWS experts connected transport vehicles delivering cars through IoT-based solutions and improved visibility without remote infrastructure requirements. 
  • Our team developed a system using AWS ECS as the container orchestration service to check eligible customers and generate deals. The system leveraged AWS Lambda functions to perform the necessary calculations and used DynamoDB as the database service to store customer data.
  • Our experts utilized Offerlogix, a recommendation engine integrated with the AWS ECS environment, to display the best trades for users looking to upgrade their vehicles. 
  • Amazon RDS was utilized as the data storage service for storing trade-related data.
  • To provide users with different offers, Offerlogix was used to display various deals dynamically based on user preferences and requirements.
  • AWS Lambda functions were employed to perform the calculations and return the results to users for finance amount calculations.

Metrics for Success

  • Predicted supply risks and reduced late deliveries by 60% 
  • Improved demand forecast accuracy by up to 50%
  • Reduced the transportation costs by 20%
  • The development and staging infrastructure expenses were lowered by 40-50%.
  • System downtime decreased by 3X of the original duration, reducing overall downtime.

Architecture Diagram


AWS Services

  • AWS Lambda:- We ran ETL jobs on AWS Lambda to generate prospects for the client, for marketing, for nurturing these prospects, and for generating deals for the users.
  • Amazon Aurora:- A database storage solution that we used for database compliance purposes.
  • Amazon CloudWatch:- We used AWS Cloudwatch to generate alarms and for application log generation and as a monitoring solution to monitor the resource utilization metrics.
  • Amazon CloudFront:- We have used Cloudfront to serve static content.
  • Amazon S3 buckets:- We used AWS S3 buckets to store configuration and customer data files.
  • Amazon Elastic Container Service:- All application services run in the container service. Thus, we used Amazon Elastic Container Service as a container orchestration tool for application deployment. 
  • Elastic Load Balancing:- The AWS elastic load balancing service is a service load balancer.
  • AWS Auto Scaling:- The client wanted a scalable solution. So, we used AWS autoscaling to scale up or down according to incoming traffic/load.
  • AWS ELasticache:- To cache sessions and common data to reduce pressure on backend databases.
  • AWS CDK & CloudFormation:- For IaC(infrastructure as a code).
  • AWS ALB:- We are using it for load balancing.
  • AWS Config:- AWS Config is a fully managed service that tracks the configuration history and configuration change notifications to use security and governance.

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