CASE STUDIES
How we help teams stabilize and scale
Anonymized engagements, described by problem, approach, architecture, and outcome. We describe results honestly – no invented metrics.
Growth-stage SaaS platform
Stabilizing a Kubernetes platform under growing load
A product team on EKS whose cluster had become hard to operate as usage grew – turning incident noise into a platform the team could trust.
Problem
The cluster had grown organically. Namespaces were snowflakes, deployments were risky, and alerting was noisy enough that real incidents were easy to miss. Engineers were spending significant time operating infrastructure instead of building product.
Approach
We reviewed the platform the way a staff engineer would, established guardrails and paved-road deployment patterns, and cleaned up the observability so alerts mapped to real customer impact. Changes were rolled out incrementally so the team kept shipping throughout.
Architecture
- Standardized EKS workload patterns and resource guardrails
- GitOps-based, reproducible deployments with clear rollback paths
- Observability reworked around service-level signals instead of raw noise
- Runbooks and ownership for the highest-impact failure modes
Outcome
- Reduced day-to-day infrastructure operations for the product team
- More consistent, lower-risk deployments
- Clearer signal in alerting, so real incidents surface faster
- Standardized environments that new services can adopt by default
AWSEKSKubernetesTerraformGitOpsPrometheusGrafana
Scaling software company
Bringing an organically-grown AWS estate into code
An engineering team whose AWS footprint had outgrown manual operation – made reproducible, safer to change, and ready to scale.
Problem
Infrastructure had accumulated as manual changes and partial automation. Environments drifted, changes were risky, and there was no dedicated Platform or SRE ownership to bring order to it.
Approach
We codified the AWS estate in Terraform, established environment and delivery standards, and introduced safer CI/CD. The work prioritized quick wins first, then the structural changes that reduce recurring risk – so value landed early.
Architecture
- AWS infrastructure defined and versioned in Terraform
- Consistent, reproducible environments replacing manual drift
- CI/CD pipelines with validation and rollback built in
- Automated backups and documented recovery paths
Outcome
- Reproducible infrastructure defined in code rather than tribal knowledge
- Faster, safer environment changes
- Automated backups and clearer recovery posture
- A foundation the team can scale on without re-learning it each time
AWSTerraformGitLab CIDockerDatadog
Facing something similar?
Start with an Infrastructure Reliability Assessment, or tell us what’s hurting and we’ll point you to the right first step.