AWS platform engineering and GitOps
We design and automate reliable AWS and Kubernetes platforms, safer delivery workflows, and preview and UAT environments your engineers can understand and own.
Learning partners
From AWS foundations to day-two operations
One connected platform system for safer delivery, clearer ownership, and engineering decisions backed by operational evidence.
AWS accounts, networking, identity, runtime choices, data, backups, and cost controls.
EKS, Helm, operators, cluster services, GitOps, and developer-ready platform foundations.
Terraform, CI/CD, Argo CD, preview/UAT environments, promotion evidence, and rollback paths.
Scoped AI playbooks correlate Git, AWS, Kubernetes, and telemetry, then prepare reviewed changes and evidence through existing GitOps controls.
Monitoring, alerting, dashboards, runbooks, distributed load tests, and useful service signals.
Practical cloud security findings, AI-assisted review, remediation plans, and compliance evidence.
One connected platform. Implemented inside your AWS accounts, repositories, and engineering workflow.
From executive priority to team ownership
Leadership gets clear choices and visible risk. Engineering gets a buildable architecture, reviewable change, and an operating model the team can own.
/ What business constraint must change?
We turn the commercial priority into a focused platform brief: safer change, faster feedback, lower idle spend, or clearer operational ownership.
/ What is the simplest credible technical path?
We map the current AWS, Kubernetes, security, cost, and team constraints, then select the smallest credible architecture that supports the outcome.
/ How will every change remain reviewable?
Terraform, CI, Helm, and Argo CD turn the design into repeatable delivery. AI prepares evidence and change proposals; engineers review and approve.
/ Can risk be seen before it becomes disruption?
Dashboards, alerts, preview checks, security reports, cost signals, and runbooks make service health and engineering trade-offs visible.
/ Can the team improve the platform without us?
We test the handover in your real accounts and repositories, document decisions, coach operators, and leave a prioritised improvement path.
Agentic skill model
Each skill is a scoped operating playbook built around recurring AWS, Kubernetes, and GitOps situations. It gathers evidence and prepares an action without giving AI an independent production approval path.
An alert, review request, preview failure, cost anomaly, or security finding starts a scoped skill.
The skill reads the Git history, manifests, telemetry, cloud evidence, runbooks, and ownership rules it is permitted to access.
Battle-tested diagnostic steps constrain what to inspect, compare, and rule out before a recommendation is formed.
The agent drafts a pull request, report, or runbook action with its assumptions, expected impact, and rollback path.
An engineer reviews, changes, approves, or rejects the proposal. Git records the decision and its evidence.
GitOps applies the approved desired state. The skill compares the result with expected signals and records the outcome.
Control boundary: AI reads and prepares. Engineers decide and approve. Git records and audits. GitOps applies and reconciles.
After handover
Implementation, decisions, runbooks, and review gates remain inside your accounts, repositories, and normal team practices.
One delivery model, two reliability postures
Stage + previews
Stage stays stable while temporary preview namespaces appear for a pull request and disappear when the work closes.
Share platform services, autoscale workloads, and use Spot where interruption is acceptable.
A pull-request label creates one service or a coordinated stack from a recorded revision.
Each preview gets its own route and configuration, then tears down automatically.
Agent-assisted operations
Scoped skills turn operational evidence into a reviewable proposal, never an unapproved production action.
Telemetry, Git history, manifests, costs, and security findings.
Correlate service, Kubernetes, AWS, and Git state.
Draft a change, report, or runbook step with supporting evidence.
People approve, Git records, and Argo CD reconciles.
Controlled by design: AI prepares. Engineers approve. Git records. GitOps reconciles.
Grow without rebuilding delivery
Keep the repositories, review gates, reconciliation, and observability. Change only the controls that the risk demands.
Case files
Selected examples. Each shows the starting constraint, the system we built, and what changed.
SaaS platform
A reviewable path from low-cost stage to production-ready AWS boundaries, owned in code.
Major fast-food brand
A lean contact workflow with private attachments, validation, and visible failure signals.
SaaS platform
Teams can launch one-service previews or coordinated UAT stacks from a pull request.
Principles that compound after handover
Every engagement should leave two durable outcomes: a stronger platform and a team that can operate, explain, and improve it.
Less manual work, fewer errors, and more consistency.
Controls and evidence are part of the platform, not an afterthought.
Your team can improve what it can see and measure.
AI organises evidence and drafts changes; engineers approve the work.
Production changes still follow engineer review and your existing GitOps controls.
Have a platform problem in mind?
Share what you are running, what is slowing the team down, and the outcome you need. We will identify the most useful next step.
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