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Case studies.

Real projects with real outcomes. See how we helped engineering teams transform their cloud operations and build platforms that actually work.

CASE STUDY

Northline Freight: Rebuilding deployment confidence across a regional logistics network

Context

Northline Freight is a regional logistics company serving the West Coast with a distributed fleet management system, real-time tracking platform, and customer portal. The engineering team had grown to 35 people across backend, frontend, and operations, with systems spanning three AWS regions.

The Challenge

Deployments were manual and frightening. The team had built 15 microservices without a coherent infrastructure strategy. Account structure was chaotic, IAM policies were overly permissive, and CI/CD was fragmented across different tools. New services took weeks to deploy. Incidents were slow to diagnose because observability was scattered.

KEY OUTCOMES

  • → 42% faster release lead time (8 weeks → 4.7 weeks)
  • → 99.95% uptime SLA achieved
  • → 68% reduction in production incidents
  • → 31% lower AWS monthly spend
  • → Solo deployments by junior engineers in week 12

What we built

Foundation Layer

  • • Multi-account AWS structure (prod, staging, dev, infra accounts)
  • • Cross-account IAM roles for teams with least-privilege patterns
  • • Unified networking with strict security group baselines
  • • Cost allocation tags and guardrails to catch runaway spend

Developer Platform

  • • Standardized CI/CD pipeline template (GitHub Actions + Terraform)
  • • Service scaffolding for consistent Kubernetes deployments
  • • Pre-built monitoring and alerting patterns for new services
  • • Self-service secrets management with automatic rotation

Observability

  • • SLO-driven monitoring across all services
  • • Distributed tracing for request flows across regions
  • • Alert quality improvement: 85% reduction in false positives
  • • Incident runbooks and automated response playbooks

Knowledge Transfer

  • • Weekly architecture office hours for team questions
  • • Runbooks and playbooks for common operational scenarios
  • • Video walkthroughs of deployment, incident response, scaling
  • • Infrastructure code review process and standards

Technologies & Practices

AWS Multi-Account Kubernetes Terraform GitHub Actions Prometheus/Grafana SLO-Based Monitoring

"ForgeHarbor didn't just hand us a cloud foundation—they taught us how to build on it. Six months later, our team is shipping twice as fast and we have the confidence to extend the platform ourselves. The deployment playbooks alone have saved us countless hours."

Sarah Chen

VP of Engineering, Northline Freight

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CASE STUDY

Cedarwell Health: Establishing governed cloud foundations for a growing care technology team

Context

Cedarwell Health provides electronic health records and care coordination software to regional healthcare systems. The engineering team had scaled from 8 to 25 people in 18 months, and infrastructure was growing on an ad-hoc basis. HIPAA compliance was a growing concern.

The Challenge

The team had one AWS account with mixed workloads (prod, test, infrastructure). Access patterns were unclear, audit trails were incomplete, and there was no clear path for enforcing healthcare compliance requirements. Building new services took too long because security review was ad-hoc and painful.

KEY OUTCOMES

  • → HIPAA-aligned foundation with audit logging and encryption
  • → 3x faster onboarding for new services (6 weeks → 2 weeks)
  • → 100% audit coverage of API calls and data access
  • → Automated compliance checks in CI/CD pipeline
  • → Clear separation between dev, test, and production

What we built

Compliance Foundation

  • • Multi-account structure aligned with HIPAA's security rule
  • • CloudTrail and VPC Flow Logs for complete audit trail
  • • Encryption at rest and in transit by default
  • • Network isolation with VPC endpoints and private connectivity

Access & Identity

  • • AWS SSO integration with role-based access control
  • • PHI access logging and alerting
  • • Time-bound credentials for sensitive operations
  • • Regular access review and certification process

Automation & Governance

  • • Policy as code for security guardrails
  • • Infrastructure as code with compliance validation
  • • Automated security scanning in CI/CD
  • • Regular compliance reporting and attestation

Team Enablement

  • • Service templates with compliance built in
  • • Security training for engineering teams
  • • Runbooks for common compliance questions
  • • Quarterly architecture reviews with security focus

Technologies & Practices

AWS CloudTrail VPC Security Policy as Code HIPAA Compliance Secrets Management Audit Automation

"We were worried about HIPAA compliance slowing us down. Instead, it's made us faster. We know what we're building complies by default. The automated checks caught issues that would've been problems later."

Marcus Webb

Engineering Lead, Cedarwell Health

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CASE STUDY

Arc & Alloy: Reducing production noise for a multi-product industrial analytics company

Context

Arc & Alloy provides industrial equipment monitoring and predictive analytics to manufacturing plants. The company operates three independent product lines across multiple Kubernetes clusters, with different observability stacks and monitoring philosophies.

The Challenge

The on-call team was drowning in alerts. Hundreds per day, but only a few were actually actionable. Alert fatigue was affecting reliability, and developers had zero visibility into system behavior. Incident response took hours because there was no clear way to diagnose what was broken.

KEY OUTCOMES

  • → 94% reduction in alert volume (1,240 → 75 per day)
  • → MTTR improved from 45 min to 12 min average
  • → Unified observability across all product lines
  • → 99.98% uptime across all products
  • → On-call rotation restored to manageable level

What we built

Observability Standardization

  • • Unified metrics collection across all services
  • • Instrumentation library for consistent application metrics
  • • Centralized log aggregation with structured logging
  • • Distributed tracing for request flow visibility

SLO-Based Alerting

  • • Service-level objectives aligned with business impact
  • • Burn-rate based alerts that predict SLO breach
  • • Alert grouping to reduce noise and context switching
  • • On-call load balancing and escalation policies

Incident Response

  • • Automated incident detection and routing
  • • Response playbooks with runbook automation
  • • Blameless post-mortems and learning process
  • • On-call training and incident practice

Developer Tools

  • • Self-service dashboards for product teams
  • • Observability-as-code in service repos
  • • Alert testing in staging before production
  • • Easy access to logs and traces for debugging

Technologies & Practices

Prometheus Grafana OpenTelemetry SLO Framework Kubernetes Events Incident Automation

"We went from alert burnout to actually knowing what matters. On-call went from terrible to manageable. The reliability work has been the best investment we've made in infrastructure."

Jennifer Patel

Security & Reliability Lead, Arc & Alloy

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