Engineering Transformations
Real-world examples of how we help companies build better systems. These are representative examples based on common engagement patterns.
Kubernetes Migration & Internal Developer Platform
Typical Engagement: 4 months
Challenge
A growth-stage SaaS company was running on legacy VM-based infrastructure with manual deployment processes. Deployment times were measured in hours, rollbacks were risky, and developers spent significant time on infrastructure issues instead of feature development.
Approach
- Conducted infrastructure and workflow assessment
- Designed Kubernetes architecture with GitOps (Flux) for declarative deployments
- Built CI/CD pipelines and automated testing workflows
- Created Internal Developer Platform with self-service capabilities
- Implemented comprehensive observability (metrics, logs, traces)
- Trained engineering team on new workflows and best practices
Outcomes
- Deployment time reduced from hours to minutes
- Improved developer productivity with self-service infrastructure
- Reduced infrastructure costs through better resource utilization
- Established foundation for future scalability
AI-Assisted Development Workflow Implementation
Typical Engagement: 8 weeks
Challenge
An engineering team wanted to adopt AI-assisted development but lacked a structured approach. They were concerned about code quality, security implications, and ensuring AI actually improved productivity rather than creating new problems.
Approach
- AI readiness assessment across the development lifecycle
- Implemented AI-assisted coding tools with guardrails and best practices
- Built automated testing and code review workflows
- Created AI-powered documentation generation pipeline
- Established evaluation frameworks to measure quality and velocity
- Developed team training program and adoption guidelines
Outcomes
- Accelerated feature delivery with maintained code quality
- Improved test coverage through AI-assisted test generation
- Better documentation with less manual effort
- Reduced cognitive load on repetitive tasks
FHIR-Based Healthcare Data Platform
Typical Engagement: 6 months
Challenge
A healthcare technology company needed to build an interoperability platform to aggregate clinical data from multiple EHR systems. The platform had to handle diverse data formats, ensure data quality, maintain security and privacy, and support real-time and batch data flows.
Approach
- Designed FHIR-based data architecture for standardized representation
- Built secure data ingestion pipelines with validation and normalization
- Implemented encryption at rest and in transit, with granular access controls
- Created comprehensive audit logging for compliance
- Developed RESTful APIs for downstream systems
- Established data quality monitoring and alerting
Outcomes
- Successful integration with multiple EHR systems
- Secure, compliant data platform meeting industry standards
- Improved data quality and consistency
- Enabled downstream analytics and clinical decision support applications
SRE Practices & Observability Implementation
Typical Engagement: 3 months
Challenge
A B2B platform was experiencing frequent outages and long incident resolution times. The team lacked visibility into system behavior, had no defined SLOs, and incident response was reactive and unstructured.
Approach
- Implemented comprehensive observability stack (Prometheus, Grafana, distributed tracing)
- Defined SLIs and SLOs aligned with business objectives
- Built automated alerting based on SLO budgets
- Created incident response playbooks and runbooks
- Conducted chaos engineering experiments to validate resilience
- Established post-incident review process to drive continuous improvement
Outcomes
- Reduced mean time to detection (MTTD) and mean time to resolution (MTTR)
- Proactive incident prevention through SLO monitoring
- Improved system reliability and customer satisfaction
- Sustainable on-call practices with better work-life balance
Chaos Engineering & Disaster Recovery Program
Typical Engagement: 4 months
Challenge
A high-growth fintech platform processing $2B+ in annual transactions had no formal disaster recovery plan. After a 4-hour outage caused by database corruption, leadership recognized the need for a comprehensive reliability program with measurable RTO/RPO targets and proactive failure testing.
Approach
- Conducted failure mode analysis mapping all critical system components and potential failure scenarios
- Established tiered RTO/RPO targets: Tier 1 (payments) RTO 5min/RPO 0, Tier 2 (core) RTO 15min/RPO 1min
- Implemented chaos engineering program using Gremlin and Chaos Monkey with controlled blast radius
- Redesigned infrastructure for multi-AZ active-active deployment with automatic failover
- Built automated runbooks for 23 common incident types with one-command remediation
- Established quarterly game days simulating full region failures with stakeholder observation
Outcomes
- Achieved 99.99% uptime (up from 99.5%) — reducing annual downtime from 44 hours to 53 minutes
- RTO reduced from 4+ hours to under 15 minutes for all critical systems
- Discovered and remediated 47 latent vulnerabilities through chaos experiments
- Zero data loss incidents since implementation (RPO targets consistently met)
- Insurance premiums reduced 30% due to documented DR capabilities
- Passed SOC 2 Type II audit with zero availability-related findings
Note: These case studies represent typical engagement patterns and outcomes. Specific client names and metrics have been generalized to protect confidentiality. Actual results vary based on context, starting conditions, and collaboration.
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