Services
Consulting that removes the constraint holding your team back.
Each service starts with your problem, not a tool list. Engagements are scoped around concrete deliverables and a clear expected outcome — with no promises of guaranteed savings, security or uptime.
Pick the challenge that sounds most like yours
DevOps Consulting
Faster, repeatable releases through well-designed CI/CD, GitOps and release engineering.
What's covered
- CI/CD pipeline design and optimization
- Build and release automation
- GitOps workflows
- Release engineering
- Deployment strategies (rolling, blue/green, canary)
- Developer productivity improvements
Where it hurts
The problem
Releases depend on a few people who know the manual steps. Pipelines differ between repositories, builds are slow or flaky, and a failed deployment pulls developers away from product work.
What we do
The solution
I review how code moves from commit to production, then standardize it: reusable pipeline templates, automated tests and builds, versioned artifacts, controlled promotion between environments and documented rollback paths.
Where you end up
Expected business outcome
Deployments become routine rather than events. Teams can release more often with less coordination, and when something fails the pipeline makes the cause easier to find.
Typical deliverables
- Current-state delivery assessment with prioritized findings
- Reusable pipeline templates for GitHub Actions, GitLab CI or Jenkins
- Environment promotion and approval model
- Rollback and release runbooks
- Handover session and written documentation
DevSecOps Consulting
Security controls built into the delivery pipeline instead of bolted on before release.
What's covered
- SAST, SCA and container scanning in CI/CD
- Dependency and software supply-chain security
- Secrets management
- Infrastructure as Code security
- Kubernetes security hardening
- Security policy automation
- Vulnerability remediation workflows
Where it hurts
The problem
Security reviews happen late, scan results land in a spreadsheet nobody owns, and vulnerable dependencies or images reach release stages before anyone notices. Audits mean a scramble for evidence.
What we do
The solution
I integrate SAST, software composition analysis, container and IaC scanning into CI/CD with severity-based gates, move secrets into a managed store, harden Kubernetes workloads and define who remediates what — and how exceptions are approved.
Where you end up
Expected business outcome
Issues surface earlier, when they are cheaper to fix. Controls are applied consistently across services, and scan results become auditable records rather than one-off reports.
Typical deliverables
- Pipeline security gap assessment
- Scanner integration (e.g. Snyk, Trivy, Checkov, Semgrep) with tuned thresholds
- Severity gate and exception policy
- Secrets migration plan and implementation
- Remediation ownership model and reporting
Cloud Infrastructure Consulting
Well-architected AWS, Azure and GCP foundations — secure networking, IAM, resilience and cost visibility.
What's covered
- AWS, Azure and GCP architecture
- Infrastructure provisioning
- Cloud migration planning and execution
- Network and IAM architecture
- High availability and disaster recovery
- Cloud cost visibility and optimization
Where it hurts
The problem
The cloud account grew organically. Networking and IAM are hard to reason about, there is no tested disaster-recovery plan, and the monthly bill keeps rising without clear ownership.
What we do
The solution
I design or review the cloud foundation — account structure, networking, identity, high availability and backup — and plan migrations in stages with clear rollback points. Cost reviews start from measurement: tagging, ownership and usage data.
Where you end up
Expected business outcome
An environment the team can understand and change safely, with resilience decisions made deliberately and cost drivers visible enough to act on.
Typical deliverables
- Architecture review against well-architected principles
- Target-state architecture diagrams
- Migration plan with sequencing and rollback points
- IAM and network design documentation
- Cost visibility report with prioritized opportunities
Kubernetes & Platform Engineering
Production-ready Kubernetes and internal platforms that make the right path the easy path.
What's covered
- Kubernetes architecture and operations
- Containerization
- Helm and deployment automation
- Reusable infrastructure modules
- Internal developer platforms
- Environment standardization
Where it hurts
The problem
Kubernetes was adopted, but every team configures workloads differently. Resource settings are guesses, deployments are inconsistent and developers wait on the platform team for routine changes.
What we do
The solution
I review cluster architecture and workload configuration, then build standard building blocks: Helm charts, reusable modules, sensible defaults for probes, resources and security context, and self-service paths for common tasks.
Where you end up
Expected business outcome
More consistent workloads, fewer surprises during deploys and scaling, and developers who can ship without filing a ticket for every environment change.
Typical deliverables
- Cluster and workload readiness review
- Standardized Helm charts or Kustomize bases
- Autoscaling, probe and resource configuration guidelines
- Platform golden-path documentation
- Operational runbooks
Infrastructure as Code
Terraform and Ansible automation that keeps environments reproducible, reviewed and versioned.
What's covered
- Terraform module development
- Ansible automation
- Infrastructure version control
- Reusable environment provisioning
- Configuration drift reduction
Where it hurts
The problem
Infrastructure is created by hand in the console. Staging no longer matches production, nobody is sure which changes were made when, and rebuilding an environment would take days.
What we do
The solution
I codify infrastructure in reusable Terraform modules and Ansible roles, separate environment configuration, and add plan review, policy checks and approvals to the change process.
Where you end up
Expected business outcome
Environments that can be recreated from version control, changes that are reviewed before they apply, and less configuration drift over time.
Typical deliverables
- Terraform module library with examples
- Remote state and locking configuration
- Plan / policy / apply pipeline
- Import plan for existing hand-built resources
- Contribution and review guidelines
Observability & SRE
Metrics, logs and alerts that help engineers find problems quickly — and reliability targets everyone agrees on.
What's covered
- Prometheus and Grafana
- Centralized logging and ELK
- Alerting and incident response
- Service-level objectives and indicators
- Reliability reviews
- Operational runbooks
Where it hurts
The problem
Logs live on individual servers, dashboards show everything except what matters, and alerts are so noisy they get ignored. Incidents are diagnosed by whoever happens to remember how the system works.
What we do
The solution
I centralize logs and metrics with Prometheus, Grafana and the ELK stack, define service-level indicators and objectives, rationalize alerts so each has an owner and an action, and write the runbooks engineers need at 3 a.m.
Where you end up
Expected business outcome
Shorter, calmer investigations, alerts people trust, and a shared, measurable definition of “reliable enough” to guide engineering priorities.
Typical deliverables
- Observability gap assessment
- Dashboards for golden signals per service
- Alert catalogue with owners and runbook links
- SLI/SLO definitions and error-budget policy
- Incident response and post-incident review templates
AI & Cloud-Native Infrastructure
Deployment, scaling and secure configuration for LLM-powered applications and AI services.
What's covered
- Infrastructure for AI-powered applications
- LLM application deployment
- Containerized AI services
- Secure application configuration
- Monitoring, scaling and deployment automation
Where it hurts
The problem
An AI feature works as a prototype, but moving it to production raises new questions: how to deploy model-serving and LLM-calling services, where API keys live, how to control cost and latency, and how to know when it misbehaves.
What we do
The solution
I containerize AI services, design deployment and scaling for them alongside the rest of the platform, manage provider credentials and configuration securely, and add monitoring for latency, errors, usage and spend.
Where you end up
Expected business outcome
AI features delivered through the same reviewed, automated and observable path as the rest of the product — rather than a fragile side project.
Typical deliverables
- Deployment architecture for AI workloads
- Container images and CI/CD for AI services
- Secrets and provider-key management
- Usage, latency and cost dashboards
- Scaling and fallback guidelines
Engagement models
Choose how we work together.
One-time assessment
Teams that need an expert review and a clear, prioritized roadmap.
- CI/CD, cloud, Kubernetes or security review
- Written findings ranked by risk and effort
- Roadmap walkthrough session
- Most common
Fixed-scope project
A well-defined outcome — a pipeline, a migration, a platform build.
- Agreed scope, deliverables and timeline
- Hands-on implementation
- Documentation and handover
Monthly retainer
Ongoing DevOps capacity without a full-time hire.
- Reserved hours each month
- Priority response for platform issues
- Continuous improvement backlog
Ongoing consulting
Fractional DevOps leadership and advisory for growing teams.
- Architecture and hiring guidance
- Regular reviews with your engineers
- Strategy for platform, security and reliability
You'll talk directly with Shahrooz Mehmood
Not sure which service fits?
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- Personal reply within two business days