What we work on, in detail.
Five practices, each described by the problems it addresses, the skills it needs and what you have at the end. We do not publish case studies, because we do not name clients — so the scope is stated plainly instead.
Platform Engineering & DevOps
A paved road from commit to production: pipelines, environments and Kubernetes that product teams can use without filing tickets.
Problems we address
- Deploys are manual, slow or depend on one person.
- Every team builds its own pipeline, and none of them match.
- Kubernetes clusters grew without standards for resources, security or upgrades.
- Rollbacks are improvised under pressure.
Competencies
- CI/CD design: build, test, scan, promote
- Kubernetes operations and upgrades
- Containers and the image supply chain
- Environment and secret management
- Self-service templates for developers
What you keep
- Versioned pipeline templates
- Cluster baseline: policies, quotas and defaults
- A documented, tested rollback path
- Runbooks for routine operations
Cloud Infrastructure & Architecture
Environments on AWS, Azure or Google Cloud that are reproducible, secure by default and sized for what they actually run.
Problems we address
- The cloud bill keeps growing and nobody can explain why.
- Production was built by hand in the console and cannot be recreated.
- Permissions and network rules have piled up without review.
- Backups exist, but nobody has tested a restore.
Competencies
- Account structure and landing zones
- Infrastructure as Code with Terraform
- Identity, network and encryption design
- Cost analysis and rightsizing
- Backup, recovery and multi-zone design
What you keep
- Architecture document with decision records
- Infrastructure code in your repositories
- Cost report with prioritised actions
- A tested recovery procedure
Software Engineering
Backend services, APIs and web platforms written to be read, tested and operated by the team that inherits them.
Problems we address
- A core service cannot change without breaking something else.
- APIs grew without contracts, versioning or documentation.
- Database queries slow the product down as data grows.
- A new product needs a first version built to last.
Competencies
- API and domain design
- Backend services in Rust, Python and TypeScript
- Relational data modelling and query tuning
- Automated testing strategy
- Code review and incremental refactoring
What you keep
- Working software in your repository
- API contracts and documentation
- Test suites that run in CI
- Architecture notes for maintainers
Reliability & Observability
Measure what users experience, alert on what threatens it, and practise recovery before an incident forces it.
Problems we address
- Users notice outages before the team does.
- Alerts fire constantly, so nobody trusts them.
- Incidents are handled from memory, and they repeat.
- Nobody can say how reliable the service is meant to be.
Competencies
- SLIs, SLOs and error budgets
- Metrics, logs and distributed tracing
- Burn-rate alerting and on-call design
- Incident response and blameless reviews
- Load, failure and restore testing
What you keep
- SLOs agreed with the business
- Dashboards and alert rules as code
- Incident and escalation runbooks
- A post-incident review template
AI Engineering
LLM and machine-learning features that run in production with evaluation, cost limits and monitoring — not only in a demo.
Problems we address
- An AI prototype works in a demo but not on real data.
- Nobody can tell whether a prompt or model change made answers better or worse.
- Inference cost and latency are unpredictable.
- Sensitive data may be reaching an external model.
Competencies
- LLM integration and retrieval (RAG)
- Evaluation sets and regression testing
- Data pipelines for training and retrieval
- Model serving, caching and cost control
- Guardrails and data-handling rules
What you keep
- Production integration behind your API
- An evaluation suite with a baseline
- Cost and latency dashboards
- Data-flow documentation