DevOps Consulting Services

You get a data-backed plan for improving the software delivery system behind your product.

• Engineering Practices Benchmark. You get an objective, data-backed read on where your org stands — and what to fix first. We compare your delivery practices with DORA and industry benchmarks.
• Delivery Pipeline Audit. Find exactly what’s slowing releases and how much faster they could go. We pull lead time for changes and deployment frequency data straight from your version control and CD systems.
• Infrastructure and Toolchain Review. Your architecture and toolchain need to support current performance demands. We assess scalability, toolchain sprawl, and system security to build your modernization plan.
• Baseline Metrics. Establishing clear metrics for your current system performance and risk profile creates a reliable benchmark for measuring progress over time. You get a hard baseline to prove the ROI of everything you change next — real deployment times and stability metrics, not estimates.
• Transformation Roadmap. The final roadmap shows your team what to do first, what it will cost, and which DORA metrics it will improve.

We automate tests and enforce coverage and performance standards so substandard code never reaches production.

• CI/CD Pipelines. We design CI/CD pipelines around your application architecture, automating the path from build through deployment while keeping release checks and rollback controls in place.
• Quality Gates. We set up quality gates to maintain a secure production environment.
• Infrastructure as Code. Defining your infrastructure in declarative code creates repeatable environments and supports faster system recovery. We use tools like Terraform and Pulumi to manage your infrastructure with version-controlled code.
• GitOps Implementation. We implement GitOps to keep your infrastructure aligned with the desired state defined in your repository. We ensure your team traces every change easily. Your repo is the source of truth, enforced through Argo CD or Flux.
• Progressive Delivery. Canary releases and blue-green deployments expose changes to a small share of traffic first, limiting the blast radius and enabling rollback in seconds.

You get a cloud architecture that scales without driving up costs.

• Landing Zones. We build a secure cloud foundation with network, access, and compliance guardrails baked in, so teams can launch new workloads without rebuilding the controls each time.
• Multi-Cloud Architecture. Connecting your cloud environments to on-premises infrastructure creates a unified ecosystem. A shared control layer lets you place workloads across cloud and on-premises environments.
• Kubernetes Platforms. We design your workloads to scale gracefully and recover from disruptions. We run them on Kubernetes with dynamic resource allocation based on real workload patterns.
• Serverless and Autoscaling. Designing your architecture around serverless functions and elastic resources lets your applications adapt automatically to shifts in demand. This setup responds to traffic spikes without manual intervention and helps align compute costs with actual usage.
• Cloud Migration. We move workloads in controlled waves, using verified data replication and rollback checkpoints to keep the cutover low-risk.

You get a self-service platform that helps teams ship faster.

• Platform Discovery. We map the delivery workflow to find delays and infrastructure work that teams can automate.
• Internal Developer Portal. Developers self-serve environments and deploys in minutes instead of waiting on ops, cutting the 30-40% of time senior engineers lose to infrastructure plumbing. Built on frameworks like Backstage or Port.
• Golden Paths and Service Templates. Every new service ships with your security and architecture standards baked in, and code-quality rules enforced from line one.
• On-Demand Environments. Developers can spin up isolated, production-like environments on demand and tear them down when the work is done. This removes wait time without leaving idle infrastructure running.
• Adoption and Governance. A platform only works if engineering teams choose to use it. We support the rollout with hands-on training and use adoption data to refine the golden paths and guardrails over time.

You see where cloud spend is going, what each workload or transaction costs, and where cloud cost drift begins.

• Cost Allocation. We implement a resource-tagging framework through infrastructure as code. Automated tagging shows each team what it spends and where.
• Rightsizing. We size resources from live usage data instead of guesses, cutting spend on idle capacity without starving workloads at peak.
• Commitment Optimization. You lock in lower rates on the capacity you actually use long-term. We use past usage to choose commitments your team can fully use.
• Cost Guardrails. We set up automated alerts to catch runaway cloud spend early. We apply automated scripts to pause idle environments and protect your budget.
• Cloud Unit Economics. We translate your cloud spend into unit economics using cost-per-transaction metrics. Connecting cloud costs to business results helps leaders make pricing and scaling decisions.

Security checks are built into the delivery pipeline, so every change is traceable.

• Shift-Left Security. We help your developers catch vulnerabilities right during the coding process, not after it ships. We add SAST and DAST scans to the pipeline so problems are caught during the build.
• Supply Chain Security. You can verify the origin of every artifact. SBOM checks and artifact signing verify each artifact before release.
• Policy as Code. Compliance becomes an automated gate instead of a pre-release scramble. We build compliance rules into every commit to secure your merges.
• Secrets Management. Using secure vaults and short-lived credentials reduces the risk of credential leaks and enforces access controls across your infrastructure.
• Compliance Automation. Automated release records provide audit-ready evidence.

You modernize critical systems in controlled slices while production stays available and data remains protected.

• Dependency Mapping. You get a clear map of a system nobody fully understands anymore, plus a safe plan to break it apart. Our AI maps legacy dependencies, and a senior engineer reviews every architecture decision.
• Containerization. We package legacy apps into standardized container images so they run consistently from laptop to production and drop into cloud infrastructure without rewrites.
• Strangler-Fig Decomposition. We replace the monolith one component at a time, avoiding a risky big-bang cutover.
• Data Migration. We run both systems in parallel and reconcile their data automatically, so rollback stays simple.
• Progressive Cutover. Gradually increasing user load while monitoring agreed health signals helps maintain uninterrupted service.

We use AIOps to surface likely root causes and automate low-risk remediation under human control.

• AIOps Data Foundation. We combine logs, metrics, and traces so the system can diagnose incidents faster.
• Alert Correlation. Your on-call sees the incidents that matter instead of a wall of alerts. Correlation groups related events into a single incident and filters the noise, reducing alert fatigue.
• Root Cause Analysis. You cut time-to-diagnosis from hours to minutes. The system reviews recent changes and points engineers to the most likely cause.
• Predictive Scaling. Your infrastructure scales ahead of demand spikes using historical demand patterns.
• Automated Remediation. Automated runbooks resolve known, low-risk incidents, while human approval remains in place for higher-impact changes.