Agentic Unconference 2026 - September 19, Chennai
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Our DevOps development services build CI/CD pipelines, Kubernetes platforms and infrastructure as code on AWS, Azure and Google Cloud. Senior engineers fix the slow, manual release path first, so your team ships small changes daily without weekend deploy windows.
DevOps development services cover the engineering that lets a team build, test, release and run software as one flow. It covers build and release pipelines, infrastructure as code, containers, automated tests, security scans and monitoring. Teams track progress with DORA metrics: deployment frequency, lead time, change failure rate and recovery time.
✓ Every release needs a war room. If a deploy means a weekend window, a runbook and five people on a call, the release process is your bottleneck. An automated pipeline turns it into a routine merge.
✓ Agile sprints finish, but the code sits waiting. Agile development speeds up planning, yet the gains stop at the board if finished work waits weeks for a release. DevOps carries each sprint through to production.
✓ Bugs reach production faster than fixes do. Manual regression testing pushes teams to batch changes, so every release carries more risk. Automated tests in CI catch a break on the commit that caused it.
✓ Auditors want evidence, not promises. SOC 2, ISO 27001 and HIPAA reviews ask who changed what, and when. A pipeline with approvals, signed builds and logs produces that evidence on every release.
> You have one app, one team and modest traffic. A Kubernetes cluster, service mesh and GitOps controller will cost more to run than they save. A managed platform such as Azure App Service or Heroku, plus one GitHub Actions workflow, is enough.
> The architecture is the real blocker. If a tightly coupled monolith forces every team to release together, a faster pipeline just ships the same bottleneck sooner. Start with application modernization services and add pipeline work as services split off.
> You're buying tools to fix a team problem. If developers still throw tickets over the wall to operations, with no shared ownership or on-call, new tooling won't move lead time. Fix ownership first, then automate.
DevOps software development starts with the pipeline. Our DevOps and quality engineering team builds CI/CD in GitHub Actions, GitLab CI or Jenkins. Every pull request runs tests, builds produce signed and versioned artifacts, and automated checks gate each deploy.
Old apps can still ship weekly. We wrap legacy .NET, Java and PHP systems in automated builds and smoke tests, and containerize what moves cleanly. Feature flags let risky changes ship dark before users see them.
Kubernetes platforms on EKS, AKS or GKE, managed through GitOps so the cluster always matches what's in Git. We set up Helm charts, autoscaling and resource limits per service, and keep the platform small enough for your team to own.
Infrastructure as code for the cloud you run. On AWS we use Terraform or CDK with CodePipeline where it fits; on Azure, Bicep with Azure DevOps Pipelines. Every environment comes from code, so staging finally matches production.
Security scans run in the pipeline, not in a quarterly audit: SAST, dependency checks and container scans on every build. After launch, our application support and IT operations team wires OpenTelemetry traces to alerts and tracks SLOs.
We add AI where the work is repetitive: Copilot code review on pull requests, test generation, and log triage that summarizes incidents. For teams shipping models, our DataOps and MLOps services add versioned data, model registries and safe rollouts.
50%
faster deployment of new features
Built a CI/CD pipeline on Azure DevOps Repos and Pipelines for a global education provider, with Application Insights monitoring, automated tests and staged rollouts.
80%
reduction in manual regression testing time across complex financial aid workflows
Mapped undocumented workflows and built a Selenium C# test framework with page objects, data-driven tests, failure screenshots and Slack alerts on every pipeline run.
100%
automated event-driven data ingestion from Zoho IoT
Replaced manual file processing on isolated EC2 instances with event-driven AWS Airflow pipelines, so IoT data now flows in on its own and backfills run reliably.
Get a 20-minute technical read from a senior engineer. No pitch deck, no sales team.
Agile is how a team plans and builds software in short cycles, and DevOps is how each cycle gets into production safely. Agile teams without DevOps finish sprints but still release quarterly, because testing and deployment stay manual. DevOps without Agile automates the delivery of large, infrequent batches. Used together, a sprint can end in production instead of in a release queue.
A platform team pays off once several product teams keep rebuilding the same pipelines, clusters and environments. Platform engineering turns DevOps practice into an internal platform, often built on Backstage, where teams serve themselves. With one or two product teams, shared pipeline templates and a well-kept cluster do the same job for far less.
DevOps cost depends far more on your starting point than on tooling. A team with Git, unit tests and one cloud account needs much less work than one that deploys by hand to on-premise servers. Tool spend is usually the smaller line, since GitHub Actions, Argo CD, Terraform and OpenTelemetry all have free or open-source options. Most of the budget goes to engineering time for pipelines, infrastructure as code and test coverage.
You can usually see results on one pilot service within weeks, long before you automate the whole estate. Capture baseline DORA metrics first, then automate the pilot end to end and compare. Rolling the same templates out to other services goes faster, because the hard decisions are already made.
No, AI is changing DevOps work rather than replacing it. Tools like GitHub Copilot and Amazon Q Developer now draft pipeline YAML, Terraform modules and test cases. AI triage can also summarize an incident in seconds. Someone still has to own the architecture, security approvals and production on-call. AI-written infrastructure code also needs the same review as any other change.
Tell us the shape of the problem. A senior engineer reads it and replies. You won't get a templated capability deck.
