~/case-studies

Anonymized delivery stories

Real industry problems, concrete methods, and outcomes from production platform work — written so engineering and leadership teams can see what BetterCallDevOps delivers.

Anonymized composite engagements. Client names, employer references, and environment-specific figures are not published. Outcomes describe verified delivery patterns from production platform work.

SaaS · Azure & Kubernetes

Real-Time Infrastructure Monitoring Platform

How a custom Node.js and Prometheus monitoring platform replaced manual infrastructure checks across Azure and Kubernetes environments.

Discuss similar work

↓ operator toilPlatform-owned observabilityAKS + Azure coverage

A product engineering organization running multiple Azure subscriptions and AKS clusters had outgrown ad-hoc monitoring scripts and spreadsheet-based health checks.

Industry issue

Teams scale cloud estates faster than their observability model — manual checks hide blind spots until customers report outages, and commercial APM tools are often misaligned with infra/platform ownership.

Client challenge

Platform operators spent hours on repetitive VM, load balancer, and cluster checks. Alerts were inconsistent, ownership was unclear, and there was no single view of infrastructure health before application-level monitoring kicked in.

Approach

Design and build a purpose-fit monitoring layer: Prometheus for metrics collection, a Node.js control plane for alert routing and enrichment, and Grafana dashboards aligned to operator runbooks — owned by the platform team, not a generic SaaS bundle.

What we did

›Mapped critical infrastructure signals across Azure IaaS, networking, and AKS control-plane/workload health
›Built a Node.js alerting service with deduplication, severity tiers, and on-call routing rules
›Deployed Prometheus scrape targets and recording rules for capacity and availability signals
›Created Grafana dashboards for platform operators with drill-down into cluster and edge components
›Documented ownership, escalation paths, and handover procedures for internal maintenance

Technology

Node.jsPrometheusGrafanaAzure MonitorAKSAlertmanager-style routing

Deliverables

›Monitoring platform baseline architecture
›Alert routing and severity model
›Operator dashboards and runbook links
›Handover documentation for internal teams

Outcome

Infrastructure health became continuously observable instead of periodically guessed. Operators gained a maintainable platform they could extend without per-check manual toil.

Proof & results

✓Manual infra health checks removed from daily operator routine for the scoped estate
✓Single pane for Azure + AKS platform signals before APM layer
✓Zero additional commercial monitoring SaaS licensing for the platform scope
✓On-call received actionable alerts with ownership and severity — not raw metric floods

Regulated technology · PCI / ISMS readiness

VAPT & Security Patching Program

Designing a recurring Nessus-based vulnerability scanning program with defined CVE remediation SLAs and audit-ready compliance evidence.

Discuss similar work

Audit-readyCVE SLA governanceAuthorized VAPT cadence

A technology company preparing for PCI DSS and ISMS-aligned audit conversations had scan output but no operating model to prioritize, assign, and close findings with evidence.

Industry issue

Security scanning without governance produces PDF dumps — findings age, ownership blurs, and audit teams cannot trace closure. Regulated environments need recurring cadence, SLAs, and proof, not one-off scans.

Client challenge

Nessus reports accumulated across servers and cloud assets. Critical CVEs lacked owners, patch windows were undefined, and there was no repeatable evidence pack for assessors.

Approach

Stand up an authorized, recurring vulnerability management lifecycle: normalize findings, risk-rank against production exposure, assign SLAs by severity, integrate with ticketing, and package closure evidence for compliance reviews.

What we did

›Defined authorized scan scope, cadence, and rules of engagement for production-adjacent assets
›Normalized Nessus output into a prioritized backlog with severity-based remediation SLAs
›Mapped findings to PCI DSS control themes and ISMS evidence expectations
›Built ticket workflows with owner assignment, due dates, and retest validation steps
›Produced audit-ready evidence templates: scan reports, remediation records, exception approvals

Technology

NessusIssue trackingPatch orchestrationSIEM correlation exportsAzure WAF posture review

Deliverables

›Vulnerability management operating model
›CVE prioritization and SLA matrix
›Evidence collection templates for audits
›Retest and closure validation checklist

Outcome

Vulnerability work shifted from reactive PDF reviews to a governed program with traceable ownership and assessor-ready documentation.

Proof & results

✓Recurring scan cadence with defined critical/high CVE closure SLAs
✓Finding-to-owner mapping for production-scoped assets
✓Audit-ready evidence pack structure for PCI/ISMS conversations
✓Authorized testing boundaries documented — no out-of-scope probing

Product engineering · AKS delivery

CI/CD & Kubernetes Deployment Automation

Re-architecting Jenkins and GitLab pipelines with automated security gates and canary deployment logic on AKS.

Discuss similar work

Gated CI/CDCanary on AKSSafer releases

A growing application team on AKS relied on manual deployment steps, shared Jenkins jobs with unclear promotion rules, and inconsistent rollback behavior under production pressure.

Industry issue

Manual releases and optional security gates are the dominant cause of preventable production incidents — teams ship faster on paper but cannot prove what artifact reached prod or revert safely when health checks fail.

Client challenge

Developers triggered deployments through brittle scripts. Security scans were skipped under deadline pressure. Canary logic existed in documentation, not in the pipeline. Rollbacks required senior intervention.

Approach

Re-architect the delivery path as a gated promotion pipeline: build → test → scan → approve → deploy → verify → rollback, with Helm-based progressive delivery on AKS and health checks tied to automated rollback.

What we did

›Audited existing Jenkins/GitLab jobs and removed optional bypass paths to production
›Implemented container build, registry promotion, and image vulnerability scanning on the ship path
›Added staging approval gates and environment-specific deployment permissions
›Configured canary/rolling deploy patterns on AKS with Helm and readiness/liveness probes
›Wired post-deploy health verification and documented rollback triggers
›Authored release runbooks for application teams

Technology

JenkinsGitLab CI/CDDockerHelmAKSContainer registry scanning

Deliverables

›Reference pipeline for agreed production services
›Security gate and approval matrix
›Canary/rollback playbook
›Release documentation and handover

Outcome

Production promotion became repeatable, reviewable, and reversible — teams knew which artifact shipped and how to recover without heroics.

Proof & results

✓Security scanning enforced on the production promotion path — not optional parallel jobs
✓Standardized deploy pattern across scoped AKS workloads
✓Automated rollback path when post-deploy health checks fail
✓Release audit trail from commit to production artifact

Enterprise commercial · Multi-subscription Azure

Azure Cost Optimization & CSP-to-EA Migration

Leading an Azure CSP-to-Enterprise Agreement transition with full cost-impact analysis and resource rightsizing.

Discuss similar work

Cost governanceEA transition planningRightsizing backlog

An organization approaching Azure Enterprise Agreement (EA) eligibility needed a technical and commercial narrative — not just a licensing form — linking workload placement, tagging hygiene, and rightsizing to the CSP exit path.

Industry issue

Cloud cost surprises often come from licensing model mismatch and untagged sprawl, not raw usage. CSP-to-EA transitions fail when finance and engineering lack a shared inventory and rightsizing backlog.

Client challenge

Subscriptions grew organically under CSP. Cost allocation was unreliable. Reserved capacity and sizing decisions were deferred. Leadership needed impact analysis before committing to EA structure.

Approach

Run a structured cost governance engagement: inventory workloads, classify by criticality and environment, rights-size obvious waste, define tagging policy, and model EA transition impact against current CSP spend patterns.

What we did

›Inventory applications, dependencies, and subscription layout across production and non-production
›Analyzed Azure Cost Management and Advisor recommendations for rightsizing candidates
›Defined tagging and chargeback model for finance/engineering alignment
›Built CSP-to-EA transition impact notes with workload-level considerations
›Prioritized quick-win decommission and resize actions with rollback-safe change windows

Technology

Azure Cost ManagementAzure AdvisorAzure Resource GraphTag governanceARM/Terraform inventory

Deliverables

›Cost-impact and licensing transition notes
›Rightsizing and decommission backlog
›Tagging and governance checklist
›Executive summary for commercial decision-makers

Outcome

Stakeholders could tie EA transition decisions to an evidence-backed view of technical estate, waste, and governance gaps — not spreadsheet guesses.

Proof & results

✓Full subscription inventory with environment and owner classification
✓Rightsizing backlog ranked by effort and production risk
✓Tagging model defined for ongoing cost allocation
✓CSP-to-EA impact narrative suitable for finance and engineering review

High-traffic SaaS · Reliability engineering

SRE Observability & Alert Rationalization

Introducing SLO-based alerting, dependency tracing, and incident runbooks to reduce alert fatigue and improve production response.

Discuss similar work

↓ alert fatigueSLO-driven pagingFaster triage

A SaaS team paging on infrastructure metrics had low signal-to-noise ratio — on-call engineers could not quickly tell whether users were impacted or which dependency was degrading.

Industry issue

Alert fatigue is an industry-wide reliability tax: teams monitor everything, understand nothing, and burn out on-call engineers while user-impacting incidents still slip through.

Client challenge

Hundreds of alerts fired weekly; many were non-actionable. No Service Level Objectives (SLOs) tied to user journeys. Incident response varied by who was on call. Post-incident learning was informal.

Approach

Introduce a reliability operating baseline: define SLIs/SLOs for critical journeys, instrument latency/error/dependency signals, tune alerts to SLO burn and user impact, and standardize incident/RCA templates.

What we did

›Mapped top user journeys to measurable SLIs (availability, latency, error rate)
›Defined initial SLO targets and error-budget policy with product/engineering agreement
›Consolidated redundant alerts and rewired paging to SLO and dependency degradation signals
›Added APM-style tracing context for scoped services (latency, error, dependency map)
›Authored on-call runbooks and RCA templates with corrective-action tracking

Technology

PrometheusGrafanaAzure MonitorAPM toolingPagerDuty-style on-call

Deliverables

›SLI/SLO worksheet for critical journeys
›Alert rationalization backlog and new paging rules
›Incident and RCA templates
›Reliability review cadence proposal

Outcome

On-call shifted from reacting to metric noise toward responding to user-impacting degradation with clearer mitigation paths.

Proof & results

✓Critical user journeys linked to measurable SLIs and SLOs
✓Non-actionable alert volume reduced for scoped services
✓Incident response follows documented runbooks and RCA structure
✓Dependency and latency context available during triage — not host metrics alone

Scale-up product team · First production Kubernetes

Production AKS Platform Hardening

Hardening a greenfield AKS cluster with node-pool strategy, network policies, ingress safety, and progressive delivery for a team entering production Kubernetes.

Discuss similar work

Production AKSZero-downtime deploysPlatform hardening

A team promoted their first AKS cluster from proof-of-concept to production without defined node pools, network boundaries, or a safe rollout model — common when Kubernetes adoption outpaces platform maturity.

Industry issue

Most AKS incidents trace to platform shortcuts: single node pool, over-permissive RBAC, missing network policies, and deploy scripts that bypass health gates. Teams need production boundaries before traffic scale, not after an outage.

Client challenge

Workloads shared a single pool with system components. RBAC was broad. Ingress and TLS were manually managed. Deployments were rolling updates without health-gated rollback. No runbooks for cluster operations.

Approach

Apply production AKS reference patterns: separate system/user pools, least-privilege RBAC, network policies, Application Gateway/Ingress hardening, Helm-based deploys with health checks, and operator runbooks.

What we did

›Split system and user node pools with appropriate sizing and autoscaling policies
›Hardened RBAC and introduced namespace isolation with network policies
›Standardized ingress/TLS via Application Gateway with health probes
›Implemented Helm deploy patterns with readiness gates and rollback on failure
›Integrated Azure Monitor for cluster and workload signals
›Delivered ops runbooks: scale events, cert rotation, deploy failure recovery

Technology

AKSAzure CNIApplication GatewayHelmHPAAzure MonitorNetwork policies

Deliverables

›AKS hardening checklist (completed for scoped cluster)
›Reference Helm deploy pattern
›Ingress and TLS configuration guide
›Cluster operations runbook outline

Outcome

The cluster moved from POC posture to a production platform with defined boundaries, safer deploys, and operability documentation the team could own.

Proof & results

✓System/user pool separation with autoscaling configured
✓Network policies enforced for scoped namespaces
✓Health-gated deploys with documented rollback path
✓Platform runbooks delivered for ongoing operations

Product engineering · Generative AI on Azure

Azure AI Foundry & Production Model Operations

Standing up Azure AI Foundry, governed model deployment, production tracing, and cost-controlled LLM operations for a team shipping AI features.

Discuss similar work

Azure AI FoundryProduction tracingGoverned model ops

A product team launched LLM-powered features using direct API keys and prototype flows. Usage grew, but there was no model versioning, production tracing, cost visibility, or safe path to promote prompt changes.

Industry issue

AI features outpace platform maturity — teams ship prompts and models to production without tracing, rollback, token budgets, or private networking. Incidents become impossible to debug and cloud bills become unpredictable.

Client challenge

Multiple models and prompt variants ran in parallel with no ownership. Application Insights did not capture end-to-end inference paths. Token spend had no alerts. Security wanted private endpoints and RBAC before wider rollout.

Approach

Treat AI like production software: Azure AI Foundry as the control plane, versioned model and prompt promotion, tracing integrated with Application Insights, cost budgets and dashboards, and CI/CD gates before production inference changes.

What we did

›Designed Azure AI Foundry workspace layout with environment separation and RBAC boundaries
›Deployed and registered Azure OpenAI and custom model endpoints with private networking where required
›Implemented prompt flow orchestration with versioned promotion and rollback paths
›Wired production tracing and evaluation hooks into Application Insights / Foundry observability
›Configured token and compute cost alerts, usage dashboards, and budget guardrails
›Added CI/CD checks for prompt, model, and inference endpoint changes before production
›Documented model promotion, incident triage, and cost review runbooks for the internal team

Technology

Azure AI FoundryAzure OpenAIApplication InsightsAzure MonitorPrivate endpointsAKSGitLab CI/CD

Deliverables

›AI platform reference architecture for scoped workloads
›Model deployment and tracing baseline
›Cost and safety governance checklist
›Model promotion and rollback runbook

Outcome

AI workloads moved from prototype API usage to a governed production platform — teams could trace inference, control spend, and promote model changes without bypassing safety or audit paths.

Proof & results

✓End-to-end inference tracing available for scoped production flows
✓Model and prompt changes promoted through a defined path — not direct prod edits
✓Token and compute budgets with alerting before runaway spend
✓Private networking and RBAC applied to production AI endpoints
✓Rollback path documented when model or prompt regression is detected

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