Case studies

Real results from real engagements

Every project is different, but the outcomes are consistent: teams deliver faster, systems run better, and companies get through pressure without theater.

30 days

Shipping a Crypto Casino Platform

Everyrealm's CEO reached out to discuss the engineering team's struggles. I stepped in to stabilize the team, break the rewrite deadlock, and ship a quality product. We delivered in 30 days.

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43% savings

Cutting Cloud Costs Without Sacrificing Performance

A growing company was burning cash on AWS with no visibility into costs. I audited the infrastructure, rationalized architecture, and cut monthly spend by 43%.

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6 weeks

Building an AI-Powered Content Discovery Platform from Concept to Launch

Led technical architecture and implementation for a content discovery platform, delivering a production-ready MVP in 6 weeks with AI-generated content and sub-$100/month operating costs.

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30 days

Shipping a Crypto Casino Platform

The Challenge

Everyrealm's CEO reached out to discuss the engineering team's state - the team was demoralized and struggling to deliver. The crypto casino platform had been in development for months but was plagued with quality issues - ledger functionality was unreliable, payment processing for bets and wins had significant bugs, and the codebase was becoming increasingly unstable. The team had fallen into a common trap: rather than stabilizing and shipping value, engineers were advocating for large-scale rewrites and "improvements" that weren't aligned with product direction. There was a deeply entrenched belief that the existing code couldn't be fixed and needed to be rebuilt from scratch. Meanwhile, customers were waiting, and leadership needed someone who could break this deadlock, restore focus, and ship a reliable product fast.

The Approach

  • →Assessed team morale and technical capabilities on day one to understand the real blockers
  • →Identified the MVP scope that could realistically ship in 30 days without rewrites
  • →Broke the "we must rewrite everything" mindset by demonstrating that stabilizing existing code was faster and less risky
  • →Partnered with the Chief Product Officer to align engineering priorities with customer value, not engineering preferences
  • →Reestablished core agile practices: sprint planning, daily standups, and retrospectives focused on outcomes
  • →Personally reviewed critical code paths and implemented clear defect escalation mechanisms
  • →Reduced meeting overhead and eliminated long standup reports that weren't driving progress

Technical Approach

Rather than rebuilding from scratch, I focused on stabilizing what existed and implementing pragmatic solutions under extreme time pressure. The key was convincing the team that fixing and improving the current codebase was viable-and actually faster than a rewrite.

Key Technical Decisions

  • →Architecture: Managed and improved existing Node.js backend with clear module boundaries and refactoring guidelines. Proved that incremental improvement was more effective than dismissing working code for a theoretical rewrite
  • →Database: PostgreSQL was already in place, but transaction throughput was poor. Improved performance through query optimization, proper indexing, and connection pool tuning-critical for handling real-money transactions at scale
  • →Testing: Partnered with the Chief Product Officer to create clear PRDs and corresponding test plans. Implemented integration tests for critical paths (deposits, withdrawals, game outcomes, ledger integrity) and established formal defect tracking
  • →Deployment: Identified that different components had different deployment timelines and requirements. Started separating CI/CD pipelines to enable independent releases and reduce deployment risk
  • →Monitoring: Established observability direction with Prometheus and Grafana to provide real-time visibility into system performance and identify bottlenecks in production
  • →Process: Reestablished sprint planning, daily standups, and retrospectives-but made them valuable. Started each sprint with product context explaining why we were building features, not just what. Reduced meeting overhead

The Results

  • ✓Increased quality and reliability with clear defect escalation mechanisms
  • ✓Shipped production-ready crypto casino platform in 30 days
  • ✓Broke the rewrite deadlock and restored team focus on delivering customer value
  • ✓Established sustainable processes for future product development
  • ✓Restored team confidence and delivery cadence

Key Takeaway

Technical teams often gravitate toward rewrites when facing legacy code challenges, but this rarely delivers value faster than incremental improvement. By stabilizing existing systems, aligning engineering work with product priorities, and implementing pragmatic quality processes, we proved the team could deliver under pressure-and established a foundation that outlasted my engagement.

43% savings

Cutting Cloud Costs Without Sacrificing Performance

The Challenge

Cloud costs had grown out of control. What started as manageable infrastructure costs for 7-10 projects suddenly spiked-new projects were seeing 8x cost increases with no clear explanation. Multiple teams were deploying resources without governance, using expensive architectural patterns without understanding the cost implications. There was no cost visibility, no accountability, and no one who could explain why monthly AWS bills kept climbing.

The Approach

  • →Conducted comprehensive infrastructure audit across all AWS accounts
  • →Tagged all resources and implemented cost allocation tracking
  • →Identified idle resources, over-provisioned instances, and inefficient architectural patterns
  • →Audited and decommissioned resources from abandoned or completed projects
  • →Created and documented cost-effective patterns to replace expensive approaches
  • →Migrated appropriate workloads to serverless architectures
  • →Implemented architecture review process: all new features required cost analysis before implementation
  • →Established budgets, alerts, and approval processes for new resources
  • →Provided training to engineering teams on cost-effective architectural patterns

Technical Approach

This wasn't just about turning off unused instances. It required deep architectural analysis to understand why resources were provisioned the way they were, identifying systemic cost drivers, then redesigning patterns for cost efficiency without sacrificing performance. The key was establishing architectural oversight so teams couldn't unknowingly deploy expensive solutions.

Key Technical Decisions

  • →Resource Cleanup: Audited all AWS accounts to identify resources from abandoned or completed projects. Coordinated with teams to safely decommission unused EC2 instances, RDS databases, S3 buckets, and other orphaned infrastructure. Established resource lifecycle policies to prevent future accumulation
  • →Scheduled Workloads: Migrated two ECS containers running scheduled processing jobs to EventBridge Scheduler + Lambda, eliminating always-on container costs. Right-sized remaining ECS containers based on actual resource utilization
  • →Batch Processing: Migrated expensive architectural patterns to simple SQS-based processing, reducing compute costs while improving reliability
  • →Serverless Migration: Moved appropriate workloads from always-on EC2 instances to Lambda for intermittent processing, paying only for actual execution time
  • →Database: Right-sized RDS instances based on actual usage patterns and CloudWatch metrics rather than overly conservative estimates
  • →Storage: Implemented S3 lifecycle policies to automatically move infrequently accessed data to cheaper storage tiers
  • →Network: Consolidated VPCs and eliminated unnecessary data transfer between availability zones
  • →Governance: Implemented architecture review and modification process-features couldn't proceed without cost analysis and pattern approval
  • →Monitoring: Implemented cost allocation tags and AWS Cost Explorer dashboards to maintain ongoing visibility and accountability

The Results

  • ✓43% reduction in monthly AWS spend
  • ✓Full cost visibility with tagging and allocation reports by team and project
  • ✓Sustainable governance framework and architectural patterns library
  • ✓Zero performance degradation or service interruptions during migration
  • ✓Cost-per-project reduced from 8x spike back to predictable baseline

Key Takeaway

The cost savings gave the company additional runway during a challenging fundraising environment. More importantly, the architectural oversight process and pattern library prevented costs from spiraling again-new projects now launch with cost-efficient patterns from day one rather than requiring expensive retrofits.

6 weeks

Building an AI-Powered Content Discovery Platform from Concept to Launch

The Challenge

A startup needed to validate product-market fit for a consumer-focused content discovery platform without the time or budget for a full native app build. They required AI-powered content generation to scale without a large editorial team, offline capabilities for mobile users, and a cost-effective architecture that could grow from zero to thousands of users. The timeline was aggressive-investors wanted a working prototype in 6-8 weeks to validate the concept before committing to a full funding round.

The Approach

  • →Led technical architecture design, selecting Progressive Web App (PWA) over native iOS to enable 2-4 week delivery vs 6-10 week native build
  • →Designed multi-agent AI system using TypeScript and LangGraph to automatically discover trending topics, generate content, and publish without human intervention
  • →Selected managed services (Supabase, Vercel) over custom backend infrastructure to minimize DevOps overhead and enable rapid iteration
  • →Implemented cost-optimized AI strategy using GPT-4o-mini for classification and GPT-4o only for final content, keeping generation costs under $50/month
  • →Established development workflows and assigned tasks to engineering team across frontend, agent pipeline, and database layers
  • →Created comprehensive technical documentation to enable team autonomy and future handoff

Technical Approach

Rather than building everything custom, I focused on composing best-in-class managed services with strategic custom code where it mattered-the AI content generation pipeline. This allowed us to ship a sophisticated platform in weeks, not months, while keeping infrastructure costs under $100/month for MVP validation.

Key Technical Decisions

  • →Architecture: PWA with Next.js 15 over native iOS-95% code reusable for future native wrapper, instant deployment, and SEO benefits for content discovery. This cut time-to-market in half while enabling broader platform reach
  • →Backend: Supabase (managed PostgreSQL + Auth + Storage) over custom Node.js backend-eliminated need for custom API code, authentication logic, and DevOps. Row Level Security (RLS) at database layer provided unhackable security without application code
  • →AI Pipeline: Multi-agent system with LangGraph orchestration-discovery agents scrape trending topics, classifier agents decide content types, researcher agents gather data, writer agents generate articles, and publisher agents deploy to production. Fully automated content generation with quality gates
  • →Cost Optimization: Strategic model selection (GPT-4o-mini for $0.01/day operations, GPT-4o only for final content) and efficient prompting reduced AI costs by 80% vs naive GPT-4 usage. Total generation cost: ~$30-50/month for daily content
  • →Offline Strategy: Service workers + IndexedDB for cached content access, background sync for user actions. Users can browse content offline with automatic sync on reconnect
  • →Deployment: Vercel edge network for global distribution, Railway for agent containers, Upstash Redis for rate limiting. Zero manual DevOps-git push triggers automatic deployment
  • →Data Model: JSONB columns for structured content blocks (paragraphs, images, galleries, callouts) enabling rich editorial layouts without rigid schemas. Flexible schema supports diverse content types without database migrations

The Results

  • ✓Delivered production-ready MVP in 6 weeks from project kickoff
  • ✓Infrastructure costs: $90-110/month for MVP (vs $1000+/month for custom infrastructure)
  • ✓AI content generation: Fully automated discovery-to-publication pipeline generating daily content with zero manual editorial work
  • ✓Cross-platform reach: Single codebase serving iOS, Android, and desktop with native-like experience
  • ✓Development velocity: Features deploy in minutes via git push, enabling rapid iteration based on user feedback
  • ✓Scalable foundation: Architecture supports 1000+ DAU on existing infrastructure with clear path to enterprise scale

Key Takeaway

By making opinionated architectural choices focused on speed and cost efficiency, we validated the product concept in weeks rather than months-and at a fraction of typical MVP costs. The automated AI content pipeline proved that editorial scaling was viable without a large team, de-risking the core business model. The PWA approach enabled immediate user testing across platforms, and the technical foundation scales seamlessly from validation to growth phase. The startup secured seed funding based on the working prototype and user validation data.

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