IT Engineering & Platform Engineering
Enterprise Platform Engineering
AI-First Platform Delivery -- Secure-by-Design, Delivered at Pace
We build and industrialise internal platforms that accelerate delivery, reduce operational load, and embed governance across cloud, hybrid, and edge environments. We transform fragmented engineering practices into cohesive, repeatable capabilities.
Why Platform Engineering Is Now a Board-Level Capability
Delivery performance, resilience, compliance, and cost governance have converged into a unified challenge demanding executive attention. Platform engineering ties these concerns together, transforming from an operational consideration into a strategic imperative.
Accelerated Change Without Risk
Stakeholders demand faster innovation cycles whilst regulatory and security requirements intensify. Platform engineering provides the control plane that enables both.
Auditability as Standard
Evidence-based releases and automated compliance checks are becoming baseline expectations from boards, regulators, and enterprise customers.
Talent Scarcity Response
With engineering talent in short supply, self-service capabilities and reduced cognitive load are essential for productivity.
Governed AI Adoption
As AI capabilities accelerate, the need for guardrails, evaluation frameworks, and controlled rollout becomes critical.
Our Four Pillars
Platform Engineering (Platform-as-a-Product)
We design and deliver platforms as products with clear ownership, adoption metrics, and continuous improvement cycles.
- Define vision and product ownership
- Measure adoption and feedback
- Iterate and optimise features
- Monitor and evolve governance
DevOps & Delivery Automation (IDLC)
We streamline the entire development lifecycle through comprehensive automation, from code commit to production deployment.
- Continuous Integration/Continuous Delivery (CI/CD)
- Infrastructure as Code (IaC)
- Automated testing and quality gates
- Deployment strategies and rollback mechanisms
Security, Compliance & Reliability Engineering
We embed security and reliability into every stage of the platform lifecycle, ensuring systems are resilient and compliant.
- Shift-left security practices (DevSecOps)
- Automated compliance checks and policy enforcement
- Site Reliability Engineering (SRE) principles
- Incident management and post-mortem analysis
Agentic AI Enablement at Enterprise Scale
We guide enterprises in safely adopting and scaling agentic AI capabilities with robust governance frameworks.
- AI strategy and roadmap development
- Ethical AI guidelines and guardrails
- Reference architectures for AI agents
- Training and enablement programmes
Service Portfolios
Seven comprehensive platform engineering services covering the entire software development lifecycle.
Internal Developer Platform (IDP) & Developer Experience
Self-service portals, golden paths, automated scaffolding, and service catalogues. Proven at scale: 100+ solution teams onboarded with technical workshops and documentation.
Kubernetes & Cloud-Native Platform Engineering
Production-grade clusters via KOPS, Kubeadm, AKS, and EKS across cloud, VMware, and edge environments. CRD/Operator-style backing services, Helm-based CD pipelines, and container management with Quay.
Edge Computing & Hybrid K8s
Azure Stack HCI with AKS at edge, ARM/Bicep automation, and hybrid connectivity. Kubernetes at edge locations for regulated environments with intermittent cloud connectivity.
Infrastructure & Environment Factory (IDLC)
Automated provisioning via Terraform, Terragrunt, Puppet, Chef, and Ansible. BDD testing, drift detection, and environment factories across vSphere, AWS, Azure, and GCP.
DevSecOps & Governance-by-Default
Security embedded at every stage: AquaSec for DAST, SonarQube for SAST, policy-as-code with Terraform and Python, WAF automation, and AWS Security Hub integration.
Observability, SRE & Operational Readiness
Prometheus, Grafana, Splunk, NewRelic, Datadog, and Dynatrace. SLO frameworks, incident management with PagerDuty, and comprehensive operational runbooks.
GPU-Accelerated MLOps & AI Platforms
TensorFlow, Kubeflow, JupyterHub with on-demand GPU provisioning. ML-Ops pipelines across AWS and GCP using BigQuery, Google AI Platform, and AutoML. Model governance and monitoring.
Agentic AI Delivery Enablement
Integrating AI agents into engineering workflows with guardrails, evaluation harnesses, and human-in-the-loop controls.
Measurable Outcomes
Our approach is underpinned by quantifiable outcomes. We systematically integrate automation, standardisation, and feedback loops to enhance key metrics across delivery, productivity, and governance.
Delivery Performance (DORA Metrics)
60%
Lead Time Reduction
Time from commit to production decreases through automated pipelines and standardised environments.
300%
Deployment Frequency
Teams deploy more frequently without increasing risk through automated quality gates.
45%
Change Failure Rate Reduction
Production failures decrease through shift-left testing and automated validation.
70%
MTTR Improvement
Recovery time shortens through SRE practices, runbooks, and comprehensive observability.
Developer Productivity
25%
Onboarding Time Reduction
New developers become productive faster with standardised environments and self-service tools.
30%
Less Time on Infra Tasks
Decreased manual effort on infrastructure management, freeing development teams.
20%
Feature Delivery Velocity
Accelerated development cycles for new features and capabilities.
15%
Developer Satisfaction Increase
Improved morale and retention through better tooling and reduced friction.
Governance, Risk & Audit
90%
Compliance Automation
Automated policy enforcement ensures adherence to regulatory standards from inception.
50%
Fewer Security Vulnerabilities
Proactive identification and mitigation of risks early in the development lifecycle.
40%
Audit Prep Time Reduction
Reduced effort with comprehensive, traceable records and automated evidence collection.
100%
Operational Resiliency
Increased system stability through built-in best practices and automated recovery.
End-to-End Platform Journey
Assess & Plan
Current state analysis, capability gaps identified, target architecture defined, and a prioritised roadmap established.
Build & Develop
Platform foundations engineered, delivery automation implemented, security guardrails embedded, and operational patterns established.
Scale & Enable
Teams onboarded, golden path templates provided, comprehensive enablement rolled out, and adoption tracked.
Operate & Optimise
SLO monitoring, cost governance, and reliability improvements continuously delivered for sustained operational excellence.
Proof Points
Kubernetes Platform Modernisation
Consolidated from 47 to 12 clusters (75% reduction). Deployment frequency increased 10x. MTTR reduced by 60%. $2.3M annual infrastructure savings.
9-month engagement | Kubernetes, ArgoCD, Crossplane, Prometheus, Grafana
Developer Platform & Golden Paths
Onboarding reduced from 3 weeks to 4 hours. 85% self-service adoption. Zero compliance violations in 12 months. Developer satisfaction from 4.2 to 8.7/10.
6-month engagement | Backstage, Terraform, GitHub Actions, Vault
VMware to Kubernetes at Scale
100+ solution teams onboarded with Kubeadm-driven K8s on VMware, extended to AWS (KOPS) and Azure (AKS). Fully automated Helm CD, OpenEBS storage, and SecOps compliance. 7+ year continuous engagement.
7+ years | Kubeadm, KOPS, VMware, Terraform, Go, Helm, GO CD, CircleCI
Edge K8s on Azure Stack HCI
AKS deployed at edge locations with full IDLC automation via Terraform and Terragrunt. ARM/Bicep provisioning, EntraID IAM, and application onboarding pipeline for vendor-managed workloads.
12 weeks | Azure Stack HCI, AKS, Terraform, Terragrunt, GitHub Actions
Client identities are confidential. Detailed references available under NDA.
Ready to move faster with confidence?
Let's discuss how Arkaya can accelerate your next initiative with AI-first delivery.