Case Studies

These case studies represent real projects with actual outcomes. Details have been adjusted to protect client confidentiality, but the challenges, approaches, and results are representative of our work.

B2B Software

ModernizationDevOpsMicroservicesPerformance

Enterprise SaaS Platform

Size: 150 employees, $20M ARR

Constraints

Could not afford downtime. Had to maintain feature velocity during migration. Limited engineering bandwidth for refactoring.

The Problem

A 6-year-old monolithic Rails application was slowing the team down. Deployment took 2 hours and often failed. The codebase was brittle-every change risked breaking something else. Customer churn was increasing due to reliability issues, and the team could not ship features fast enough to compete.

Why They Chose Us

After evaluating 3 consultancies, they chose us because we proposed an incremental migration plan that maintained feature velocity. Other firms wanted a 12-month big-bang rewrite that would freeze their roadmap. Our approach let them ship features while modernizing, and our fixed-price milestone structure reduced risk.

Our Approach

Started with a technical debt audit to identify the highest-impact areas. Broke the monolith into services incrementally, starting with the most problematic modules. Set up CI/CD pipelines with automated testing and blue-green deployments. Implemented comprehensive monitoring with Datadog. Worked alongside their team, reviewing code and pairing on complex migrations.

Outcome

Deployment time dropped from 2 hours to 15 minutes. Customer-reported incidents decreased by 70%. The team started shipping features 3x faster. Platform uptime improved from 97.2% to 99.9%. Engineering morale improved significantly.

Results by the Numbers

Deployment Time

Before

2 hours

After

15 min

Incidents

Before

~30/month

After

~9/month

Feature Velocity

Before

After

Platform Uptime

Before

97.2%

After

99.9%

What We Shipped

Microservices architecture with 5 core services
CI/CD pipelines (GitHub Actions + AWS CodeDeploy)
Monitoring dashboards and alerting (Datadog)
API gateway with rate limiting and authentication
Migration runbooks and rollback procedures
Architecture documentation and team training

LegalTech

AI EnablementLLM IntegrationProduct Engineering

Legal Technology Startup

Size: 40 employees, Series A funded

Constraints

Required 95%+ accuracy for regulatory compliance. Needed human-in-the-loop for quality control. Had to work with existing document storage system.

The Problem

Legal teams were spending days manually reviewing contracts and legal documents. The process was slow, expensive, and error-prone. Competitors were starting to use AI, putting pressure on the company to innovate or risk losing market share.

Why They Chose Us

They needed someone with both AI expertise and legal domain understanding. Their internal team had no ML experience, and general AI consultants didn't understand legal compliance requirements. We had previously built document processing systems for regulated industries and could explain accuracy tradeoffs clearly to non-technical stakeholders.

Our Approach

Built a custom AI pipeline using GPT-4 for document classification and entity extraction. Implemented a RAG system with Pinecone for precedent matching across historical cases. Created validation workflows so legal professionals could review and correct AI outputs. Integrated with their existing document management system. Set up monitoring for AI accuracy and cost.

Outcome

Automated 85% of initial document review work. Review time dropped from 3-4 days to 4-6 hours. Achieved 94% accuracy on classification tasks, exceeding the 95% threshold when combined with human review. The feature became their primary competitive advantage and drove a 40% increase in customer interest.

Results by the Numbers

Review Time

Before

3-4 days

After

4-6 hours

Automated Work

Before

After

85%

Accuracy

Before

N/A

After

94%

Customer Interest

Before

Baseline

After

+40%

What We Shipped

AI document processing pipeline (Python + GPT-4)
RAG system with vector database (Pinecone)
Review interface for legal teams (React)
Document classification API
Cost monitoring and optimization scripts
Accuracy evaluation framework
Documentation for prompt iteration and improvement

HealthTech

Design SystemsUXAccessibilityReact

Healthcare Platform

Size: 200 employees, 3 product lines

Constraints

HIPAA compliance required. Had to support 3 separate products with different frameworks. Accessibility was non-negotiable for government contracts.

The Problem

Three product lines had completely different UIs. Every new feature required rebuilding basic components. Design-to-dev handoff was slow and inconsistent. The company was failing accessibility audits, blocking enterprise contracts. New designers took weeks to understand the inconsistent patterns.

Why They Chose Us

Enterprise contracts worth $2M were blocked on accessibility compliance, making this urgent. We had built design systems for multi-product companies before and knew how to handle the politics of getting 3 teams aligned. Our portfolio showed successful WCAG 2.1 audits and we could start immediately.

Our Approach

Designed a comprehensive design system starting with an audit of existing components. Built a React component library with 40+ reusable components. Established design tokens for colors, spacing, and typography. Created Storybook documentation with accessibility guidelines. Conducted full WCAG 2.1 audit and remediated all issues. Integrated the component library with Figma using Figma tokens.

Outcome

UI development time dropped by 60%-designers could prototype in Figma and developers could implement using the same components. Achieved WCAG 2.1 AA compliance across all products, unlocking $2M in enterprise contracts. New designers and engineers onboarded 50% faster. The design system became a core competitive advantage.

Results by the Numbers

UI Development Time

Before

Baseline

After

-60%

Accessibility Compliance

Before

Failed

After

WCAG 2.1 AA

Onboarding Time

Before

Baseline

After

-50%

Enterprise Contracts

Before

Blocked

After

$2M unlocked

What We Shipped

React component library (40+ components)
Design tokens and theming system
Storybook documentation with accessibility guidelines
Figma component library synced with code
Accessibility audit report and remediation
Migration guide for existing products
Design system governance documentation

FinTech

Platform EngineeringMicroservicesPerformanceFinTech

Fintech Payment Platform

Size: 80 employees, Series B funded

Constraints

PCI compliance required. Zero downtime acceptable during scaling. Peak traffic 10x during market hours. Legacy monolith processing $50M+ monthly.

The Problem

Payment processing platform was hitting performance limits at 1,000 transactions per second. Database was maxing out during peak hours causing failed transactions. Customers threatened to churn due to reliability issues. The monolithic architecture made it impossible to scale individual components.

Why They Chose Us

Customer churn was accelerating and they needed someone who could work fast without breaking PCI compliance. We had scaled payment systems before and could show similar architecture patterns that worked. Our guarantee of 10x capacity improvement within 90 days or the milestone was free gave them confidence to move forward.

Our Approach

Implemented event-driven architecture with Kafka for asynchronous processing. Split payment processing into dedicated microservices. Migrated to distributed PostgreSQL with read replicas. Added Redis caching layer for hot data. Set up auto-scaling infrastructure on AWS with Kubernetes. Implemented comprehensive monitoring with custom SLAs and alerting.

Outcome

System now handles 10,000+ transactions per second with room to grow. Database load reduced by 75% through caching and read replicas. Zero failed transactions during peak periods. Cloud costs actually decreased by 35% through efficient resource utilization. Platform uptime improved from 98.1% to 99.95%.

Results by the Numbers

Transaction Capacity

Before

1,000 TPS

After

10,000+ TPS

Database Load

Before

95% CPU

After

25% CPU

Platform Uptime

Before

98.1%

After

99.95%

Cloud Costs

Before

Baseline

After

-35%

What We Shipped

Event-driven microservices architecture (Kafka + Spring Boot)
Kubernetes cluster with auto-scaling (AWS EKS)
Distributed database setup with read replicas
Redis caching layer for performance
Real-time monitoring dashboard and SLA tracking
Load testing framework and runbooks
PCI compliance documentation and audit support

EdTech

DevOpsKubernetesCloud MigrationCost Optimization

Online Learning Platform

Size: 120 employees, 500K+ students

Constraints

Cannot disrupt learning during semester. Must maintain FERPA compliance. Limited DevOps expertise in-house. Budget constraints required cost-neutral migration.

The Problem

Running on legacy EC2 instances with manual deployments. Scaling for exam periods required all-hands-on-deck weekends. Deployment failures meant rollbacks took hours. Infrastructure costs were ballooning with growth. No disaster recovery plan in place.

Why They Chose Us

They had limited DevOps expertise and needed someone who could both implement and train their team. Migration had to be cost-neutral because budget was tight. We offered to include comprehensive team training and knowledge transfer as part of the engagement, and our track record showed we could actually reduce costs through right-sizing.

Our Approach

Migrated to Kubernetes on AWS EKS with zero-downtime blue-green deployments. Implemented GitOps workflow with ArgoCD for declarative infrastructure. Set up horizontal pod autoscaling based on CPU and custom metrics. Created helm charts for all services. Established disaster recovery with cross-region backups. Trained internal team on Kubernetes operations.

Outcome

Deployments went from 4 hours to 15 minutes with automated rollbacks. Infrastructure now auto-scales during exam periods with no manual intervention. Disaster recovery tested and proven with <1 hour RTO. Cloud costs reduced 40% through right-sized resources. Internal team now manages the platform independently.

Results by the Numbers

Deployment Time

Before

4 hours

After

15 min

Scaling Process

Before

Manual (hours)

After

Automatic

Cloud Costs

Before

Baseline

After

-40%

Recovery Time

Before

Unknown

After

<1 hour

What We Shipped

Kubernetes cluster setup (AWS EKS)
GitOps CI/CD pipeline (GitHub Actions + ArgoCD)
Helm charts for all applications
Auto-scaling configuration (HPA + Cluster Autoscaler)
Disaster recovery procedures and testing
Infrastructure as Code (Terraform)
Team training and operations documentation

SaaS

Product EngineeringMVPFull-StackStartups

B2B Analytics Startup

Size: Pre-seed, 5 employees

Constraints

Tight budget ($50K total). Launch deadline for investor demo in 10 weeks. Founders non-technical. Needed to validate product-market fit quickly.

The Problem

Founders had validated demand through customer interviews but had no MVP. Needed to build a functional product to close first customers and raise seed round. Previous outsourcing attempt failed after 6 months and $80K with no working code.

Why They Chose Us

After getting burned by an offshore agency, they needed someone local who would communicate clearly and deliver working code. Our 2-week discovery sprint let them test our competence before committing to the full project. Fixed-price milestone structure meant they knew exactly what they'd spend, and we had references from other startups who successfully raised funding using MVPs we built.

Our Approach

Started with 2-week discovery sprint to validate technical approach and define MVP scope. Built React frontend with Next.js and TypeScript. Created Node.js API with PostgreSQL database. Implemented authentication, billing integration (Stripe), and analytics dashboard. Deployed on Vercel with AWS RDS. Weekly founder demos to validate features. Delivered comprehensive documentation for future development team.

Outcome

Delivered working MVP in 9 weeks on time and on budget. Product used to close 3 pilot customers in first month. Founders raised $2M seed round using the MVP. Clean, maintainable codebase enabled founders to hire internal team. Zero post-launch critical bugs. System handled first 100 customers with no infrastructure changes needed.

Results by the Numbers

Time to Market

Before

No product

After

9 weeks

Budget

Before

$50K allocated

After

On budget

Pilot Customers

Before

After

3 in month 1

Seed Funding

Before

Pre-seed

After

$2M raised

What We Shipped

Full-stack web application (Next.js + Node.js)
Authentication and user management
Stripe billing integration
Analytics dashboard with data visualizations
Admin panel for customer management
Automated deployment pipeline
Technical documentation and architecture guide

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