What our audit looks like — a sample report for Northbeam Analytics
This is an abridged version of the report we hand back after an infrastructure audit. Northbeam Analytics is a fictional B2B SaaS composed from real engagement patterns: a 25-engineer team on AWS, Node.js + Postgres, with an in-flight migration to Kubernetes.
- Client
- Northbeam Analytics (B2B SaaS, ~25 engineers)
- Duration
- 6 weeks, fixed scope
- Format
- Written-first, no required calls
- Stack
- AWS · EKS · Node.js · Postgres · GitHub Actions · Datadog
- Scope
- Delivery, observability, FinOps, IaC drift
- Team
- 1 lead DevOps + 1 platform engineer
3 mid-complexity findings · 4 sprints of changes · written final report
Three findings of moderate DevOps complexity
- Finding 01 / CI/CD01 / 03
Deploys rely on manual steps and tribal knowledge
What was painful
Production releases were triggered from personal scripts owned by 2–3 engineers. Quality gates were missing, staging routinely diverged from prod, and rollback took 20–40 minutes of manual actions inside the release window.
What we did
Standardised GitHub Actions with required checks, a lint/test/build matrix and Docker builds with immutable tagging. Added a canary deployment to EKS at 5% traffic with automatic promotion based on health signals and one-click rollback via kubectl rollout undo.
What we got
Lead time from merge to production dropped from 5 days to 1.5 days. Deployment failure rate fell from 18% to 6%. Rollback became predictable (< 90 seconds).
- Finding 02 / Observability02 / 03
Metrics, logs and traces live in silos
What was painful
Datadog covered part of the services, CloudWatch covered the rest, tracing was not wired in. Alerts were built on host-level thresholds, paged through the night and did little to help incident triage.
What we did
Consolidated metrics, logs and traces into a unified service catalog. Moved alerts to SLO-based thresholds (latency p95, error rate, error-budget burn). Built dashboards and runbooks for three key services and added a post-incident review template.
What we got
MTTR improved from 70 to 26 minutes (−63%). Pager noise dropped by 48%. The team got a coherent single-pane view of production for the first time.
- Finding 03 / FinOps03 / 03
Cloud bill grows faster than the team
What was painful
RDS instances sat idle on weekends, the EKS nodegroup was over-provisioned for peak load and no savings plan was in place. The monthly AWS bill grew 8–12% month over month with no link to traffic growth.
What we did
Right-sized RDS and the EKS nodegroup, introduced schedules for non-production environments, committed to a 1-year compute savings plan for the steady baseline. Launched a weekly FinOps review with ownership tied to cost drivers.
What we got
Cloud spend dropped by 28% with no performance regression. The team gained a repeatable budget-control practice anchored to service ownership.
Numbers after 6 weeks of work
Every metric maps to a concrete change above — it is not a vague "reliability improvement" but a trace from finding to outcome.
From $8,000 · 4–6 weeks · fixed scope
The price covers the audit, implementation of priority changes and a written final report with a roadmap. Final pricing is set after a written brief, based on the size of the stack and the team's priorities.
- Fixed scope, not hourly billing
- Written final report + 90-day roadmap
- Optional follow-up as a retainer after the audit
Want this kind of readout for your stack?
Share your stack, risk areas and priorities in writing. Within 1–2 business days you will get a response with a preliminary audit outline and a price estimate.
- [email protected]
- response
- Written communication only
- reply
- Within 1–2 business days
Disclaimer:Northbeam Analytics is a composite example built from real engagement patterns. The numbers are realistic but do not refer to a specific deal.