Featured Work
Selected projects demonstrating AI product development, process automation, and systems integration capabilities. Each project showcases end-to-end ownership from problem identification through solution delivery and impact measurement.
ClaimGuard
Healthcare Claims Validation Platform
AI-powered validation combining traditional rule-based checks with advanced medical reasoning
The Challenge
Healthcare providers face billions in lost revenue from billing errors—gender-procedure mismatches, age-inappropriate treatments, duplicate services, and anatomical inconsistencies that slip through manual review processes. Traditional validation relies on rule-based systems that catch obvious errors but miss nuanced clinical logic violations.
Product Screenshots
AI-Driven Audit Recommendations for Immediate Action
Actionable intelligence, not just insights. The app prioritizes interventions—from payment holds to manual reviews—streamlining the audit process and reducing downstream waste or fraud.
Claims Error Pattern Visualization by Type and Severity
Visual breakdown of billing anomalies, grouped by error type and severity. Surfaces root causes and quantifies risk distribution, enabling payers to focus resources where they'll have the greatest impact.
Real-Time ROI & Efficiency Analysis
A single batch revealed $6,134 in recoverable value within seconds. Real-time detection of billing errors with instantly surfaced ROI and efficiency gains measurable at first glance.
Technical Implementation
Stack
- • Python, Streamlit
- • OpenAI GPT-4
- • FastAPI architecture patterns
Key Features
- • Parallel processing (5 concurrent workers)
- • LRU caching with 85%+ hit rate
- • Real-time performance analytics
- • Medical reasoning engine
The Solution
ClaimGuard validates healthcare claims in real-time, providing:
Medical reasoning explaining clinical impact
Business impact analysis with operational consequences
Financial impact with recovery cost estimates
Regulatory compliance assessment for CMS
What I Learned
The healthcare domain revealed the power of combining deterministic rules with AI reasoning. Traditional systems catch obvious errors, but AI can explain why errors matter and predict downstream consequences. The parallel processing architecture taught me how to balance speed, cost, and accuracy in production AI systems.
Wolfstitch Cloud
AI Dataset Preparation SaaS
Production cloud platform transforming documents into AI-ready training datasets
The Challenge
AI/ML engineers and researchers spend hours manually preparing training datasets—converting documents, chunking text, counting tokens, formatting for different models. Existing tools are either too complex (enterprise solutions), too limited (single-format converters), or require cloud dependency (privacy concerns).
Product Interface
Clean, Intuitive Upload Interface
Professional-grade document processing with intelligent chunking, 40+ file formats, and beautiful export options. Simple drag-and-drop interface that handles everything from PDFs to presentations.
Multi-Format Support
Upload any document type—PDF, DOCX, XLSX, PPT, TXT, HTML, MD, EPUB, and more. The system intelligently handles format conversion and text extraction.
Instant Results with Token Analysis
Processing completes in seconds with detailed analytics—chunk count, token distribution, and preview of first chunks. Download JSONL files ready for GPT-4 training.
Technical Architecture
Frontend
- • Next.js 15, TypeScript
- • Tailwind CSS, shadcn/ui
- • Progressive enhancement UI
Backend
- • FastAPI, Python
- • Wolfcore processing library
- • Railway auto-scaling
Infrastructure
- • Cloudflare CDN
- • PostgreSQL + Redis
- • Zero data retention
Key Features
- • Semantic boundary detection
- • GPT-4 tokenization
- • End-to-end encryption
Results & Impact
Time to Market
Launched production SaaS 6-8 weeks ahead of roadmap
Performance
99.9%+ uptime with global CDN optimization
Scale
Auto-scaling infrastructure handles enterprise volumes
Security
End-to-end encryption, zero retention, GDPR compliant
What I Learned
Shipping a production SaaS taught me the difference between "it works" and "it works reliably at scale." Railway's auto-scaling, Cloudflare's CDN, proper error handling, health monitoring, and graceful degradation—these aren't nice-to-haves, they're table stakes for production. The privacy-first architecture (zero retention) became a differentiator and competitive advantage.
Skills Demonstrated Across Projects
Technical Capabilities
- • AI/ML orchestration & LLM integration
- • Full-stack development (Next.js, React, FastAPI)
- • Cloud architecture (Railway, Cloudflare)
- • Data integration & ETL pipelines
- • Enterprise platforms (SharePoint, ServiceNow, Jira)
Business & Process
- • Process engineering & optimization
- • Cross-functional coordination
- • Requirements gathering & analysis
- • Project management (Agile & Waterfall)
- • Business impact assessment
Product Development
- • Rapid prototyping & MVP delivery
- • User experience design
- • Product strategy & pricing
- • Technical documentation
- • Go-to-market planning
Interested in Working Together?
I'm currently seeking opportunities in AI Product Management, Business Analysis, and Process Optimization roles.