Automotive Retail Re-Imagined
Seamless ecosystem and most advanced and integrated end-to-end automobile service platform in the industry
Our client is a leading provider of automotive retail cloud platforms. It empowers automotive retail like garages to deliver uncompromised and unparalleled customer experiences. Through their Digital Services Experience (DSE) bringing effective vehicle diagnosis through human computer interaction based on sensory & predictive techniques all bundled through cloud enablement.
Our client wanted to provide end-to-end solutions in the automotive retail industry by creating a cloud-based platform with a seamless ecosystem that drives their operations intelligently. Starting from vehicle services scheduling, smart phone check-in, vehicle services monitoring, connecting customers when needed and Sensor based finally to payment with complete transparency to customers. All the above features to be accomplished through Artificial Intelligence Features and Machine Learning capabilities.
This is where MX Techies came in. Our team worked as a Technology partner along with the client’s Engineering team to build a cloud platform and inter-connected garage equipment using sensors.
Seamless flow of information across dealership operations
We were involved in developing cloud-based platform (SaaS) and Configuring IOT Beacons and multiple sensors in garage. Human computer interaction architecture was created to retrieve information from vehicle to platform with Beacons and sensors connected to Apache Kafka, which consumes all the data and sends to Kafka Consumers, data insights where mixed, sent to web sockets and finally the transformed data has been sent to cloud platform for diagnosis, analysis, and servicing vehicles.
The current architecture that is on on-premises HDFS using Cloudera, is being migrated to AWS with the above Snowflake setup being transformed into Microservice architecture causing the service to pull data from different sources.
Real Time Actionable Intelligence
Once the data was in the data lake, the multiple pipelines moved data from ‘raw’ state to ‘refined’ and finally to ‘production’ ready in parquet HDFS Datawarehouse based on few metrics. Amazon EMR transient clusters were implemented. The visualization and BI tool adopted was Tableau.
The key insights gained were Customer Behavior, Key Events of engagement and Event Diary & achieved in real- time. This enabled our client to participate in deals like amazon prime day and monitor the real-time trends and behavior live.
- improvement in real- time feedback and live monitoring
- Speedy delivery and release cycles
- Agile/Scrum CI/CD Dev Ops
- Core App Stack: AWS, Python, Node API, React-UI, JMeters, Kafka, Kafka consumer, WebSocket.