How Smart Joules Cut Release Times by 70% with future-ready DevOps

About the Customer

Smart Joules is a climate-tech company helping Indian hospitals control rising energy costs and sustainability mandates through real-time monitoring, automation, and scalable, cloud-based energy optimization. Founded in 2015, they specialize in energy efficiency for healthcare, a sector with continuous energy demands due to HVAC systems and medical equipment.

Their proprietary “DeJoule” platform combines IoT devices, cloud analytics, and an outcome-linked pricing, ensuring clients only pay from verified savings. Operating across 20+ cities and 60+ hospitals, Smart Joules has saved over ₹200 crore and prevented 200,000+ tons of CO₂ emissions. Their pay-as-you-save model and full-stack execution approach make energy efficiency accessible at scale.

Problem Statement

As Smart Joules scaled, they needed a DevOps framework that was faster, more predictable, and security-ready to support their expanding footprint.

Their existing GitHub Actions + Amazon EKS setup had been reliable, but:

• Deployment cycles were slow and required manual intervention
• Environments were inconsistent, causing QA rework and delays
• Security practices needed alignment with modern DevSecOps for VAPT readiness
• Observability gaps made issue detection reactive rather than proactive
  

They set three priorities:

1. Upgrade EKS to the latest version for performance, security, and scalability
2. Adopt GitOps for consistent, traceable deployments
3. Strengthen security to meet certification and compliance needs

Solution

Smart Joules partnered with ShellKode to modernize their DevOps stack, delivering a future-ready, cloud-native foundation.

1. Latest Amazon EKS for Performance & Scalability

Upgraded to the latest EKS version for better speed, stability, and built-in security enhancements. This reduced maintenance effort, simplified scaling, and ensured readiness for future workloads.

2. Reliable Environments Across All Stages

Using AWS CloudFormation and Git-based reusable templates, environments are now provisioned identically across dev, staging, and production. Impacting predictable releases, fewer bugs, and a 40% cut in rollout delays.

3.  Automated, GitOps-Powered Deployments

Smart Joules’ deployment process was slow, delaying releases. The CI/CD pipeline was redesigned using AWS CodePipeline and CodeBuild, integrated with GitOps principles through ArgoCD, enabling fully automated and consistent deployments. As a result, releases now happen in minutes, accelerating time-to-market and improving reliability.

4. Full Visibility with Built-in Observability

Smart Joules wanted deeper visibility into their systems to spot and resolve issues faster, especially as workloads grew. ShellKode implemented an observability stack with Prometheus for metrics collection, Grafana for dashboards, SigNoz for distributed tracing, and Slack for real-time alerts. This proactive monitoring approach improved system uptime, reduced risks, and boosted confidence during critical operations.

5. Smarter Automation with Serverless

Smart Joules optimized background processes like report generation by moving them to AWS Lambda, a serverless service that runs code only when triggered. This on-demand execution eliminated the need for always-on servers, cutting unnecessary compute costs and reducing the operational workload for the engineering team.

6. Stronger Security, Greater Trust

We put in place automated checks to spot and fix security risks early. Everything was brought into one place for easy oversight, helping Smart Joules meet VAPT certification standards. This boosted client confidence and ensured the business was ready for future growth.

Metrics

1. Faster Time to Market: The duration of release cycles dropped by 70%, reducing deployment time from hours to minutes.
2. Consistent Delivery: Environment mismatches were eliminated, cutting QA rework and reducing rollout delays by 40%.
3. Optimized Resource Use: Consolidating infrastructure led to a 60% drop in infra overhead and smoother peak-time scaling.
4. Lower Operational Costs: Moving background jobs to serverless lowered monthly compute costs significantly.
5. Improved System Reliability: Real-time observability slashed issue detection time by 45%, boosting uptime.
6. Seamless Customer Experience: Safe, zero-downtime deployments helped maintain user trust and platform stability.

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