About Customer:
Customer is an India-based e-mobility startup building modular, customizable, and connected electric two-wheelers. The platform integrates IoT and data-driven decision-making into its vehicles, enabling real-time monitoring of battery performance, vehicle diagnostics, energy consumption, and operational health across its fleet, with a focus on smart mobility and sustainability.
Industry:
Electric Mobility (E-Mobility)
Offering:
AWS IoT Core + Real-Time Analytics + MLOps
Business Challenges:
- Limited visibility into vehicle performance reduced operational insights across the fleet.
- Inability to process high-frequency telemetry at scale delayed decision-making.
- Lack of predictive maintenance capabilities increased service costs.
- Uncertainty around battery degradation reduced customer confidence.
- Fragmented analytics workflows slowed innovation cycles.
Solution Implemented:
- Secure, MQTT-based telemetry ingestion through AWS IoT Core, with each connected vehicle provisioned using X.509 certificates, giving full, continuous visibility into fleet-wide vehicle performance.
- High-throughput real-time streaming via Amazon Kinesis Data Streams, with AWS Lambda handling validation, anomaly detection, and orchestration, enabling telemetry to be processed at scale without delay.
- An Amazon SageMaker predictive maintenance model, continuously retrained on operational and diagnostic telemetry, to flag service needs before failures occur.
- An Amazon SageMaker battery state-of-health and range-estimation model, trained on historical and real-time battery telemetry, to give accurate, ongoing visibility into battery degradation.
- A unified Amazon S3 data lake and Amazon DynamoDB operational store, orchestrated through an AWS CodeBuild-driven MLOps pipeline with Amazon CloudWatch observability, replacing fragmented analytics workflows with a single scalable foundation.
Services Used:
- AWS IoT Core
- Amazon Kinesis
- AWS Lambda
- Amazon DynamoDB
- Amazon S3
- Amazon SageMaker
- AWS CodeBuild
- Amazon CloudWatch
- AWS IAM
Business Benefits:
- 60–70% improvement in telemetry visibility – full, real-time visibility into fleet-wide vehicle performance, directly closing the original blind spot.
- 50–60% faster data processing – high-frequency telemetry now flows through the pipeline in near real time, eliminating prior processing delays.
- 40–50% improvement in predictive maintenance readiness – service needs are flagged proactively, reducing unplanned downtime and service costs.
- 45–55% better battery performance insight accuracy – clearer, more reliable visibility into battery degradation, restoring customer confidence.
- 70–80% increase in analytics scalability – a unified, future-ready platform in place of fragmented analytics workflows.
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Frequently Asked Questions
Rapyder helped Customer Mobility move from fragmented, low-visibility vehicle data to a unified, real-time IoT and MLOps platform on AWS - improving telemetry visibility by up to 70% while enabling predictive maintenance and scalable analytics.
Each challenge was matched to a specific piece of the architecture: AWS IoT Core closed the vehicle-visibility gap, Kinesis and Lambda solved telemetry processing at scale, and dedicated SageMaker models addressed predictive maintenance and battery degradation directly.
60–70% improvement in telemetry visibility, 50–60% faster data processing, 40–50% better predictive maintenance readiness, 45–55% more accurate battery performance insights, and 70–80% greater analytics scalability.
Depending on the use case, organizations can benefit from:
- Real-time visibility into connected device and fleet performance
- Faster, more reliable telemetry processing at scale
- Predictive maintenance and reduced unplanned downtime
- Automated, continuously retrained ML models
- Stronger device and data security
- Lower operational overhead through cloud-native automation
While this case study focuses on a connected electric mobility platform, Rapyder's IoT and MLOps capabilities can be applied across industries such as Manufacturing, Logistics, Energy & Utilities, Healthcare, Automotive, and Smart Infrastructure. (This last point reflects Rapyder's broader capabilities rather than the case study itself.)
