Client Engagements
These case studies represent anonymized and representative engagements with fictional examples illustrating the types of problems we solve and outcomes our clients achieve.
Reducing deployment risk for a Midwest logistics software company
62%
Reduction in failed deployment recovery time
8h → 20m
Deployment window reduction
15m
Critical rollback time vs 24h previously
Client Profile
Regional provider of transportation management software serving mid-sized logistics companies across the Midwest. $30M ARR, 150+ employees, built on containerized microservices on AWS.
The Challenge
Deployments had become a bottleneck. Each release required 8+ hours of preparation, comprehensive manual testing, and a carefully choreographed cutover procedure during a maintenance window. A single missed step or unexpected error could mean a 24+ hour rollback, during which customers couldn't operate their logistics networks.
The team wanted to ship more frequently, but deployment risk made that impossible. Leadership was hesitant to allow releases outside the quarterly window.
Our Approach
1. Pipeline Redesign
Built a CI/CD pipeline with automated testing gates, including integration and end-to-end tests that ran on every commit.
2. Canary Deployments
Implemented canary rollouts: new versions deployed to 10% of traffic first, with automated rollback if error rates exceeded thresholds.
3. Infrastructure as Code
Migrated all infrastructure to Terraform, making rollbacks a simple git revert rather than a manual procedure.
4. Observability Layer
Set up custom dashboards showing deployment health: error rates, latency, customer-facing transaction success.
Results & Impact
Technical Outcomes
- ✓ Deployment window: 8 hours → 20 minutes
- ✓ Rollback time: 24 hours → 15 minutes
- ✓ Failed deployments: 1 per quarter → less than 1 per year
- ✓ Deployment frequency: quarterly → 5-10 per week
- ✓ Mean time to recovery: 4 hours → 12 minutes
Business Impact
- ✓ Feature velocity increased 3x
- ✓ On-call load reduced (fewer urgent page-outs from deployments)
- ✓ Customer confidence in updates improved
- ✓ Engineering team morale: shipping became fun, not stressful
- ✓ Ability to respond to critical bugs within hours, not weeks
"We spent two years trying to fix deployment problems internally. Aperture Orbit got us to reliable, fast deploys in 12 weeks. The shift in how we talk about releases—it went from dread to routine—changed everything."
— VP of Engineering, Logistics Software Company
Establishing cloud cost ownership across a multi-site manufacturer
28%
Cloud cost reduction in year one
14
Business units now track costs independently
$2.1M
Annual savings projected long-term
Client Profile
Global specialty manufacturer with 6 facilities and 14 distinct business units, each running their own applications on AWS. Annual cloud spend: $7.5M and growing 35% year-over-year without clear understanding of why.
The Challenge
Finance couldn't map cloud costs to business units. Engineering didn't see cost data and thus had no incentive to optimize. Reserved instances sat unused. Orphaned development environments ran in production accounts.
Without visibility, cost control was impossible. The CFO wanted to flatten spending; engineering said cloud was inherently expensive.
Our Approach
1. Cost Allocation Model
Designed a tagging standard that mapped every resource to a business unit and cost center. Built automated enforcement into CloudFormation and Terraform.
2. Visibility Dashboard
Created dashboards showing each business unit their daily costs, with drill-down to service level. Updated hourly.
3. Rightsizing Analysis
Audited 18 months of CloudTrail and cost data. Identified underutilized instances, mismatched instance types, and unused managed services.
4. Budget Guardrails
Set monthly budget alerts per business unit. Unauthorized resources trigger automated notifications.
Results & Impact
Cost Wins
- ✓ Eliminated $840k in orphaned dev/test resources
- ✓ Right-sized instances: $320k annual savings
- ✓ Reserved instances and savings plans: $520k annual savings
- ✓ Database consolidation: $180k annual savings
- ✓ Year 1 total: $1.86M reduction (28% of baseline)
Organizational Impact
- ✓ Business units now own their cloud costs
- ✓ Engineering incentivized to optimize, not spend
- ✓ Finance can forecast accurately
- ✓ Board visibility into cloud ROI by business unit
- ✓ Preventative, not reactive, cost management
"Cloud costs were a black box. Now every business unit manager can see exactly what they're spending. That visibility changed behavior. People shut down unused environments. Engineering made better architecture choices. Finance and IT actually trust the numbers now."
— CFO, Specialty Manufacturing Company
Improving production visibility for a healthcare workflow company
48%
Reduction in critical alert noise
72%
Improvement in incident diagnosis time
54m → 8m
MTTR reduction
Client Profile
Healthcare workflow software company serving hospital systems and surgical centers. Mission-critical platform processing 50,000+ patient care workflows daily across distributed AWS infrastructure.
The Challenge
The on-call team was paged constantly—30-40 alerts per week—but most were false positives or low-urgency. When real problems occurred, diagnosis took an hour or more because the team didn't have correlated signals (logs, traces, metrics) to point to root cause. Doctors and clinic managers waiting for system recovery called in angry.
The team was burned out. They had observability tools but didn't know how to use them effectively. Leadership was nervous about reliability.
Our Approach
1. Service-Level Objectives
Defined SLOs for each critical service: 99.95% availability for workflow processing, 99.9% for API responses under normal load.
2. Alert Policy Redesign
Reduced from 200 alert rules to 35, focused on user-visible impact. Eliminated threshold-based alerts in favor of anomaly-based detection.
3. Distributed Tracing
Instrumented all services with OpenTelemetry. Traces now show the exact path a request took and where latency accumulated.
4. Runbook-Driven Response
For each alert, linked to a runbook. Diagnosis steps happened in parallel, not serial. Escalation paths were clear.
Results & Impact
Observability Wins
- ✓ Alert volume: 30-40/week → 2-4/week
- ✓ Alert accuracy: 35% signal → 92% signal
- ✓ Diagnosis time: 50+ minutes → 8 minutes average
- ✓ MTTR: 54 minutes → 8 minutes
- ✓ Customer-visible incidents: 12/year → 2/year
Organizational Impact
- ✓ On-call team retention improved
- ✓ Confidence in production operations increased
- ✓ Faster feature releases (less fear of breaking things)
- ✓ Customer satisfaction with reliability improved
- ✓ Predictable, manageable on-call rotation
"We threw more monitoring at the problem and made it worse. Aperture Orbit helped us think about observability differently—not more data, but better data. The right data. Now when something breaks, we know what and why almost immediately. That changes everything in healthcare."
— Director of Engineering, Healthcare Workflow Company
Quick Reference
Midwest Logistics
Transportation Management
Challenge
8-hour deployment windows, risky releases
Result
20-minute deployments with automated rollback
62% deployment risk reduction
Measurable improvement
Multi-site Manufacturer
Manufacturing Operations
Challenge
$7.5M cloud spend with no visibility
Result
Cost ownership by business unit, optimized spending
28% cost reduction year 1
Measurable improvement
Healthcare Workflow
Healthcare IT
Challenge
Alert fatigue and slow incident diagnosis
Result
Smart alerts and distributed tracing for fast diagnosis
48% alert reduction, 72% faster diagnosis
Measurable improvement
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