Services
We help organisations that have already invested in Kubernetes but are now experiencing the second-order problems that emerge at scale - inconsistent architecture, fragmented tooling, operational strain, and rising costs.
Platform Engineering Assessment
Typical engagement: 2-4 weeks
A short diagnostic engagement to review your Kubernetes platform architecture, operating model, and reliability practices. Designed for organisations that know there are issues but want an expert assessment before committing to a larger programme.
What we assess
- Cluster architecture and configuration
- Platform team operating model and governance
- Deployment and release practices
- Observability and alerting maturity
- Infrastructure as Code quality and patterns
- Developer experience and self-service capabilities
What you get
- Platform architecture review with findings
- Maturity assessment across key dimensions
- Risk and gap analysis
- Prioritised improvement roadmap
- Recommended next steps with effort estimates
Best suited for
Engineering leaders who need clarity on what to fix first. Typically triggered by platform reliability concerns, upcoming scaling requirements, or a need to justify investment in platform improvement.
Platform Engineering Transformation
Typical engagement: 3-6 months
A delivery-led engagement focused on restructuring and standardising Kubernetes environments so they can scale reliably and be operated safely. We embed with your team and ship production-ready infrastructure in your environment, using your tools and change processes.
Typical workstreams
- Platform architecture redesign and cluster standardisation
- GitOps operating model implementation
- Infrastructure as Code restructuring
- Developer self-service and internal platform capabilities
- CI/CD pipeline design and migration
- Platform governance and ownership model
- Cost optimisation
What you get
- Standardised, well-governed Kubernetes platform
- Reduced operational burden on platform teams
- Self-service developer workflows
- Documented architecture and runbooks
- Knowledge transfer and team enablement
- Measurable improvement in delivery velocity
Best suited for
Organisations where the problem is already understood and the priority is implementation. Often follows an assessment, or is engaged directly when platform teams are under operational strain and leadership needs a scalable, reliable operating model.
Reliability & Observability Engineering
Typical engagement: 2-4 months
Focused engagements to improve production visibility, alerting quality, telemetry architecture, and reliability practices. We help teams move from reactive firefighting to structured reliability engineering with clear signals and measurable objectives.
Typical workstreams
- Observability architecture design and implementation
- Telemetry pipeline design (metrics, logs, traces)
- Instrumentation strategy and rollout
- Alerting redesign and noise reduction
- SLO/SLI framework implementation
- Monitoring stack deployment and migration
- Observability cost optimisation
What you get
- Clear, actionable production visibility
- Reduced alert fatigue and faster incident diagnosis
- Scalable telemetry architecture
- SLOs aligned with business objectives
- Documented observability standards
- Lower telemetry and logging costs
Best suited for
Teams experiencing alert fatigue, poor production visibility, telemetry sprawl, or rising observability costs. Often engaged alongside or after platform transformation work, or as a standalone engagement for organisations with specific reliability concerns.
Production AI Platform Engineering
Typical engagement: 4-8 weeks (design) ยท 3-6 months (implementation)
Your platform team is being asked to support AI workloads. The clusters exist. The pressure from leadership exists. What doesn't exist is a safe, repeatable, production-grade path that doesn't compromise everything you've already built. Most organisations discover this gap when the first inference workload hits production and immediately creates problems the platform wasn't designed to handle - GPU contention, unpredictable latency, cost blowouts, and no clear operating model for who owns what.
Typical workstreams
- Platform readiness assessment for AI inference workloads
- Inference runtime architecture and deployment patterns
- GPU and accelerator scheduling, quota, and tenancy policy
- Reliability engineering for model-serving services
- Observability and cost attribution for inference endpoints
- Security, compliance, and policy controls for model serving
- Self-service interface and golden paths for inference
What you get
- Production-ready Kubernetes foundation for inference workloads
- Standardised deployment patterns for online inference
- GPU governance model with quota, isolation, and right-sizing
- Latency, throughput, and cost baselines with SLOs
- Security and compliance controls for model serving
- Self-service operating model with clear ownership boundaries
- Knowledge transfer and team capability uplift
Not covered by this service
- Model development, selection, or fine-tuning
- Training pipelines and feature engineering
- Data science workflows and notebook environments
- Prompt engineering or RAG application development
Best suited for
Enterprise platform teams under pressure to support AI inference workloads on existing Kubernetes infrastructure. Typically triggered when leadership has committed to AI initiatives but the platform team has no established, governed path to production for inference services.
Capabilities
Practices and disciplines we bring across all engagements.
Kubernetes & Containers
- Cluster architecture and design
- Multi-cluster management
- Autoscaling and right-sizing
- Security hardening
Infrastructure as Code
- Modular IaC architecture
- State management
- Automated plan and apply workflows
- Drift detection and remediation
Observability
- Metrics, logs, and traces
- Instrumentation strategy
- Dashboard and alerting design
- Telemetry cost management
CI/CD & GitOps
- Pipeline design and optimisation
- GitOps operating models
- Release management
- Deployment automation
Cloud Platforms
- AWS and GCP
- Cost optimisation
- Multi-cloud strategy
- Migration planning
Security & Compliance
- Regulated environments (PCI-DSS)
- Policy enforcement
- Secrets management
- Identity and access controls
AI & GPU Infrastructure
- Inference runtime architecture
- GPU scheduling and governance
- Accelerator right-sizing
- Inference observability and cost attribution
Not sure which engagement fits?
Most clients start with a conversation. We'll help you figure out the right approach.
Frequently Asked Questions
How do engagements typically start?
Most clients start with a conversation to understand the current state and goals. From there, we either begin with a 2-4 week assessment to identify gaps and priorities, or move directly into delivery if the problem is already well understood.
Do you replace our platform team?
No. We embed with your existing team and work in your environment, using your tools and change processes. The goal is to ship production-ready infrastructure and transfer knowledge so your team can own and operate everything we build.
What size organisations do you work with?
We typically work with organisations that already have Kubernetes in production and a platform team of at least 3-5 engineers. The problems we solve - architectural inconsistency, operational strain, cost governance - tend to emerge once infrastructure reaches a certain scale.
Can you help with AI inference if we haven't started yet?
Yes. The best time to build inference platform foundations is before the first production model deployment, not after. We help platform teams design GPU governance, deployment patterns, and operating models so inference workloads land on a platform designed for them.
How long does a typical engagement last?
Assessments run 2-4 weeks. Transformation and implementation engagements typically run 3-6 months. AI inference platform design starts at 4-8 weeks, with implementation extending to 3-6 months depending on scope.
What cloud providers do you work with?
Primarily AWS and GCP. Our work is Kubernetes-native, so the platform patterns, tooling, and operating models we build are largely cloud-agnostic, but the underlying infrastructure integration is provider-specific.