Trunk-based flow
Short-lived branches, feature flags and continuous integration replacing long-running release branches and merge hell.
Engineering Velocity
Ship on Friday afternoon without anyone flinching
Deployment fear is a systems problem. We rebuild the delivery path — trunk-based flow, automated quality gates, immutable artefacts, progressive rollout and one-click rollback — until releasing becomes the least eventful part of your week.
0×
Increase in deployment frequency
0%
Reduction in change failure rate
0 min
Median lead time, commit to production
Overview
Measured against DORA, not against a tool list
We baseline the four DORA metrics — deployment frequency, lead time, change failure rate and time to restore — then attack whichever constraint is actually binding. Often it is not the pipeline at all: it is a manual approval, a shared test environment or a fragile database migration process.
The platform we build gives developers a self-service path to production with guardrails, not a ticket queue. Golden pipeline templates, ephemeral preview environments, policy-as-code and paved-road service templates.
Observability comes with it: structured logs, traces, RED and USE metrics, SLOs with error budgets, and alerts that page a human only when a customer is genuinely affected.
GitOps delivery
Declared desired state in Git, reconciled continuously. Deploys are pull requests; rollbacks are reverts.
Guardrails, not gates
Automated policy, security and quality checks that let good changes through fast and stop bad ones cold.
SLOs with error budgets
Reliability targets tied to customer experience, with alerting derived from burn rate rather than raw CPU.
What we build
Capabilities you get on day one
The components below are engineered patterns we have shipped repeatedly — not concepts we would be exploring for the first time on your project.
Golden pipelines
Reusable templates covering build, test, scan, sign, deploy and verify — adopted by new services in an afternoon.
Preview environments
Every pull request gets an isolated stack with seeded data, torn down automatically on merge.
DevSecOps gates
SAST, dependency and container scanning, IaC policy checks, secret detection and SBOM generation in the pipeline.
Observability platform
Unified metrics, logs and traces with service dashboards generated from the catalogue, not hand-built.
Progressive delivery
Canary and blue/green rollouts with automated analysis and abort on SLO regression.
Developer portal
A service catalogue with ownership, docs, dependencies, scorecards and self-service scaffolding.
Capabilities
Everything inside our devops & platform engineering practice
The full scope of the practice. Engagements typically draw on a focused subset — this is the bench you have access to.
CI/CD Engineering
- Azure DevOps pipelines
- GitHub Actions workflows
- GitLab CI/CD
- Jenkins modernisation
- Build & artefact management
- Release orchestration & approvals
- Trunk-based development enablement
- Feature flag & progressive delivery
Containers & GitOps
- Docker image engineering & hardening
- Kubernetes cluster operations
- Helm chart libraries
- Argo CD & Flux GitOps
- Kustomize & environment overlays
- Service mesh operations
- Autoscaling & capacity engineering
- Multi-tenant namespace governance
Infrastructure Automation
- Terraform module engineering
- Terragrunt & workspace strategy
- Ansible configuration management
- Pulumi & CDK
- Secrets management (Vault, Key Vault)
- Ephemeral preview environments
- Policy as code (OPA, Kyverno)
- Golden AMI / image pipelines
Observability & SRE
- Prometheus & Grafana stacks
- ELK / OpenSearch logging
- Loki, Tempo & OpenTelemetry
- Distributed tracing instrumentation
- SLO / SLI definition & error budgets
- Alert design & on-call rotation
- Incident response & postmortems
- Chaos & resilience testing
Platform Engineering
- Internal developer platform (IDP)
- Backstage service catalogue
- Paved-road service templates
- Self-service environment provisioning
- Developer experience metrics
- DevSecOps pipeline integration
- Cost visibility per service
- DORA metrics instrumentation
Business impact
The outcomes clients measure
Figures are medians across delivered engagements in this practice. We will baseline your own numbers during discovery rather than promise these.
30×
More frequent deploys
From monthly release nights to multiple production deployments per day, per team.
82%
Fewer failed changes
Automated gates, preview environments and canary analysis catching regressions pre-blast-radius.
11 min
Commit to production
Median lead time after pipeline consolidation and removal of manual handoffs.
↓ 65%
Less unplanned work
SLO-driven alerting and postmortem follow-through reduce recurring firefighting.
Technology stack
DevOps & Platform Engineering technology stack
Selected per engagement against your existing estate, your team's skills and total cost of ownership — never by partnership tier.
CI/CD
- GitHub Actions
- Azure DevOps
- GitLab CI
- Jenkins
- Argo Workflows
GitOps & Containers
- Argo CD
- Flux
- Kubernetes
- Docker
- Helm
- Kustomize
- Istio
IaC
- Terraform
- Terragrunt
- Bicep
- Pulumi
- Ansible
- Packer
- Crossplane
Observability
- Prometheus
- Grafana
- Loki
- Tempo
- OpenTelemetry
- ELK Stack
- Datadog
Security
- Trivy
- Snyk
- SonarQube
- HashiCorp Vault
- OPA
- Kyverno
- Syft
Platform
- Backstage
- Port
- LaunchDarkly
- OpenFeature
- PagerDuty
How we deliver
How a devops & platform engineering engagement runs
Six stages, each with a defined output. You can stop after any one of them and still hold something useful.
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DORA baseline
Measure the four key metrics, map the value stream and identify the genuine constraint rather than the obvious one.
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Pipeline blueprint
Branching model, environment topology, artefact and promotion strategy, quality gates and rollback design.
-
Reference implementation
One real service taken end to end through the new path, proving the pattern under production conditions.
-
Observability rollout
Instrumentation standards, dashboards, SLOs, error budgets and an alert policy that respects sleep.
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Scale adoption
Templates, migration support per team, documentation and office hours until the paved road is the default.
-
Continuous improvement
Quarterly DORA review, developer experience surveys, cost per service and platform roadmap.
Industries we serve with this practice
Engagement models
How to start with DevOps & Platform Engineering
Three commercial shapes. Most clients begin with an assessment and move into delivery once the plan is agreed.
Fixed-price assessment
From $12,000
Two to four weeks. Produces a prioritised backlog, target architecture, risk register and a costed delivery plan you own outright.
- Named architect
- Executive readout
- No obligation to proceed
Most common
Milestone delivery
Scoped per phase
Well-bounded phases priced against agreed acceptance criteria. Suited to migrations, integrations and defined product increments.
- Acceptance-based billing
- Change control included
- Fortnightly demos
Dedicated pod
Monthly retainer
An embedded team — lead, engineers, QA — working in your sprints and tooling with US-hours overlap from our India centre.
- Scale up or down monthly
- Your definition of done
- Direct team access
Indicative ranges for planning purposes. Final pricing follows scope confirmation — we do not quote before we understand the problem.
FAQs
DevOps & Platform Engineering — frequently asked
Do we need Kubernetes?
Often not. If you run a handful of services with predictable load, managed platforms like Azure App Service, AWS ECS or Cloud Run give you most of the benefit at a fraction of the operational cost. Kubernetes earns its complexity at scale, with many teams, or where you need portability and fine-grained control. We will recommend against it when it is overkill.
How long before we see results?
Pipeline consolidation and quality gates typically show measurable lead-time improvement within four to six weeks. Broader change — trunk-based development, test automation maturity, SLO culture — is a two to three quarter programme, because it is as much about working agreements as tooling.
Will this replace our operations team?
No, it changes what they work on. The same people move from ticket-driven provisioning and manual releases to building platform capability and reliability engineering. That transition is part of the engagement, including training and role design.
How do you handle database changes in continuous delivery?
Expand-and-contract migrations, backwards-compatible schema changes, migration tooling in the pipeline with dry runs, and a rule that deployments and migrations are independently reversible. Database change is usually the hardest constraint on deploy frequency, so we address it explicitly rather than hoping.
Can you support our platform after we build it?
Yes — co-managed or fully managed platform operations with defined SLAs, on-call coverage, upgrade management and a quarterly improvement roadmap. Many clients start co-managed and take full ownership within a year.
Related practices
Cloud Services
Migration, cloud-native engineering, Kubernetes, FinOps and resilience across AWS, Azure, Google Cloud, Oracle and IBM.
Cyber Security
Offensive testing, SOC and SIEM, identity, cloud security and compliance programmes for ISO, GDPR and HIPAA.
QA & Software Testing
Test automation, performance, API, security and mobile testing built into the delivery pipeline.
Enterprise Software Development
Custom platforms, CRM, ERP, HRMS and workflow systems engineered for the way your business actually operates.
Engineering Velocity
Ready to talk about devops & platform engineering?
Send the context — current systems, constraints, what you have already tried. An architect from this practice will reply, usually within one business day.