DevOps, AI-accelerated.
CI/CD pipelines, infrastructure-as-code, and continuous monitoring — engineered for teams that need to ship faster, break less, and scale without a firefighting culture.
Shipping software is a competitive weapon.
In today's digital world, smooth integration between an efficient IT system, development workflow, and business ensures continuous and progressive application delivery. Teams that deploy daily outpace teams that deploy monthly — not just in features, but in their ability to respond to market signals. Learn more on our engineering capabilities page.
Elite DevOps teams deploy on-demand. Mature CI/CD pipelines make every merged commit a potential release.
High-performing teams recover from incidents in under an hour. Automated rollback and observability make this repeatable.
Elite organizations keep change failure rates below 15%. Automated testing in the pipeline is the lever.
Eight disciplines,
end to end.
From first commit to production monitoring, our DevOps practice covers the full lifecycle. Each capability below compounds with the others — combine them to build a genuinely continuous engineering system. Pair with our enterprise engineering practice for large-scale deployments.
- 01
Infrastructure Management
Provision, scale, and decommission cloud infrastructure programmatically — no manual drift, no config surprises in production.
- 02
Configuration Management
Treat every server, container, and cloud resource as code. Puppet, Ansible, and Terraform enforce consistency from dev to prod.
- 03
Environment Setup
Identical dev, staging, and production environments spun up from a single manifest. Eliminates the 'works on my machine' class of bugs.
- 04
Continuous Integration
Every commit triggers automated build, test, and security scan. Developers always merge against validated, up-to-date code.
- 05
Continuous Delivery
Release-ready artefacts produced on every green build. Human approval gates where they matter; automation everywhere else.
- 06
Continuous Monitoring
Metrics, logs, and traces correlated across your stack. Anomalies surface before users notice — and before SLA clocks tick.
- 07
Test Automation
Unit, integration, performance, and security tests wired into the pipeline. Quality shipped as a property, not a phase.
- 08
Build Automation
Deterministic, reproducible builds — Dockerized, cached, and versioned. The same artefact that passed tests is the one that ships.
Integration and delivery
running in parallel.
Continuous integration keeps the codebase always-valid. Continuous delivery makes every green build a shippable artefact. Together they remove the coordination tax that slows most teams.
Always-validated code, always available.
Continuous integration ensures developers always have access to the most up-to-date and validated code. This prevents costly delays by allowing many developers to work on the same source code at the same time with confidence.
- Automated build on every commit
- Parallel test execution
- Fast feedback loops (under 10 min to green/red)
- Branch protection enforced by the pipeline
Every green build, ready to ship.
CD extends CI so that every passing build is packaged, staged, and ready to promote to production. Human approval gates stay where they add value — governance checkpoints, compliance sign-offs — while everything below that line runs automatically.
- Blue/green and canary release strategies
- Environment-parity across dev, staging, and prod
- Automated rollback on failing health checks
- Release dashboards tied to user-facing metrics
AWS and Azure,
pipeline-native.
We build DevOps workflows that use native cloud services — not generic scripts bolted onto managed infra. The result is tighter observability, lower ops overhead, and no surprise costs from un-managed tooling.
Pipeline-native on AWS
CodePipeline orchestrates the flow; CodeBuild compiles and tests; ECR stores your images; ECS or EKS runs them. Every stage is auditable and rollback-ready.
Full-stack on Azure DevOps
Azure DevOps Services connects boards, repos, pipelines, artefacts, and test plans in one surface. Seamless integration with Microsoft identity, Active Directory, and the Azure resource graph.
Also available: Google Cloud (GKE, Cloud Build, Cloud Deploy), DigitalOcean, and bare-metal / hybrid configurations.
Intelligence built
into the pipeline.
Generic DevOps tooling misses the AI-era workloads — model training jobs, GPU clusters, experiment tracking. We extend your CI/CD to cover the entire stack. Explore how this connects to our AI & ML engineering practice.
Predictive auto-scaling
ML models trained on traffic patterns pre-scale capacity before demand spikes — not after latency degrades.
AI-assisted anomaly detection
Monitoring agents that learn your baseline and flag deviations, reducing alert fatigue while catching real incidents earlier.
Automated test generation
AI-generated edge-case tests integrated into the CI run. More coverage without proportionally more engineering time.
ML model deployment pipelines
Shadow deployments, canary releases, and automated rollback tuned for model serving — the CI/CD equivalent for your AI layer.
Security posture scanning
Static analysis, dependency vulnerability checks, and IaC security scanning run at commit time, not post-audit.
Release intelligence
Deployment dashboards that correlate release events with user-facing metrics — so every ship decision is evidence-backed.
Infrastructure depth
since 1998.
Our DevOps practice draws on patterns shipped with Oracle, AstraZeneca, Abbott, Vodafone, McDonald's, and BCG — faster to production, fewer wrong turns, and a delivery rhythm your team will recognize.
27 years of infrastructure depth
We've shipped infrastructure for Oracle, AstraZeneca, Abbott, BCG, and MTN. Your environment is a variant on patterns we've hardened across 7,000+ projects.
Multi-cloud fluency
AWS, Azure, and GCP practitioners under one roof. Migrations, multi-cloud splits, and disaster-recovery designs handled without vendor lock-in.
Security baked in, not bolted on
DevSecOps from day one — pipeline security scanning, least-privilege IAM policies, and secret management wired before the first production deploy.
AI pipeline expertise
We connect your ML model training and serving pipelines to the same CI/CD infrastructure as your application code. One deployment story, end to end.
Follow-the-sun delivery
Teams in Ahmedabad, Dubai, Beverly Hills, and Melbourne mean your pipeline doesn't stop when your timezone does.
Measurable outcomes
Deployment frequency, lead time, MTTR, and change failure rate — we baseline them at kickoff and drive them in the right direction before handoff.
Answers for
engineering leaders.
01What does a typical DevOps engagement with IndiaNIC look like?
02Can you migrate our existing infrastructure to AWS or Azure without downtime?
03Do you support on-premises and hybrid environments?
04How do you handle infrastructure for AI and ML workloads?
05What monitoring and alerting tools do you use?
06How quickly can we expect deployments to go from once a week to multiple times daily?
Ship faster,
break less.
Start with a pipeline audit — current deploy frequency, MTTR, change failure rate — and a clear prioritized roadmap. Most teams are running CI/CD in production within six weeks.