Projects

Challenge:

Development organization needed centralized AI-assisted development infrastructure with standardized workflows and budget management across distributed team.

Solution:

• Deployed Claude Code workspaces via Coder on Hetzner infrastructure for centralized development
• Four-tier CLAUDE.md configuration hierarchy for standardized AI assistant behavior
• Cross-repository template synchronization system with bidirectional sync
• Automated token budget tracking and optimization
• TIDE workflow (Think → Implement → Deploy → Evaluate) integration

Results:

• ✓ Centralized AI-assisted development infrastructure for distributed team
• ✓ Reduced token costs by 70% with budget optimization
• ✓ Cut developer onboarding time from days to hours
• ✓ Achieved 90%+ consistency in AI output quality

Challenge:

Need to deploy Claude-powered applications across development, staging, and production environments with zero-downtime guarantees and enterprise security requirements.

Solution:

• Three-environment deployment pipeline (development/preview/production)
• Auto-Claude pipeline for autonomous feature builds with QA review gates
• Zero Trust security architecture with Cloudflare Access
• Automated SSL/TLS certificate management
• Edge-native deployment on Cloudflare infrastructure

Results:

• ✓ Maintained 99.9%+ uptime across production environments
• ✓ Reduced deployment time from hours to minutes (8x improvement)
• ✓ Zero security incidents with Zero Trust architecture
• ✓ Eliminated manual SSL certificate management overhead

Challenge:

Development teams needed real-time collaboration platform integrating 8+ specialized AI assistants with GitHub PR workflows and instant synchronization across distributed teams.

Solution:

• WebSocket-based real-time synchronization with bounded history management
• Multi-agent executor supporting 8+ AI assistants (code review, docs, testing, deployment)
• GitHub PR review automation with AI-powered narrative generation
• High-performance backend achieving sub-10ms latency
• Cross-platform distribution via npx with automatic binary downloads

Results:

• ✓ Reduced PR review time by 50%+ with AI-assisted reviews
• ✓ Achieved <10ms WebSocket latency for real-time collaboration
• ✓ Increased team productivity by 30%+ with unified workflow
• ✓ Supported 6 platform targets with single npx command

Key Capabilities:

• Multi-stage computer vision pipeline combining MediaPipe pose estimation, BiRefNet segmentation, and Gemini LLM analysis
• Bottom-up kinetic chain analysis (Ankle → Knee → Hip → Pelvis → Thorax → Cervical)
• Privacy-preserving features with background blur and face blur
• Deployed to production clinical workflow on Palantir Foundry
• German-language output for end users with detailed biomechanical feedback

Results:

• ✓ Deployed to production clinical workflow processing 40+ patient assessments daily
• ✓ Reduced biomechanical assessment time from 30 minutes to 5 minutes (80%+ reduction)
• ✓ Achieved 90%+ accuracy in posture deviation detection
• ✓ 100% GDPR compliance with privacy-preserving blur layers

Challenge:

Clinical teams needed automated biomechanical assessment reports, but manual LLM orchestration was error-prone and required technical expertise.

Solution - Key Capabilities:

• Visual DAG editor for designing specialized LLM agent pipelines with dependency resolution
• Multi-stage analysis workflow: pose estimation → anatomical assessment → biomechanical interpretation → clinical recommendations
• Node-based architecture enabling modular, reusable AI analysis components
• Real-time cycle detection and validation ensuring robust pipeline execution
• Produces high-quality, structured biomechanical analysis reports through agent collaboration

Challenge:

Development organization needed to coordinate AI-assisted development across 200+ concurrent workspaces while managing merge conflicts and ensuring code quality consistency.

Solution - Key Capabilities:

• Four-level CLAUDE.md configuration hierarchy for organization-wide AI assistant standardization
• TIDE workflow (Think → Implement → Deploy → Evaluate) with autonomous builds
• AI-powered merge conflict resolution for parallel development workflows
• Cross-repository template synchronization keeping 200+ workspaces aligned
• Token budget management system optimizing AI usage across development teams

Results:

• ✓ Reduced merge conflict resolution time by 70%+ with AI automation
• ✓ Synchronized 200+ repositories with zero manual intervention
• ✓ Reduced developer wait time by 60%+ with autonomous builds
• ✓ Maintained consistent code quality across all projects

Challenge:

Veterinary professionals needed instant access to multi-GB medical knowledge base with image-based disease identification capabilities and full citation traceability.

Solution - Key Capabilities:

• Multi-GB knowledge base: High-quality veterinary science textbooks with semantic search across text and images
• Patient-specific analysis: Loads individual medical case histories for personalized diagnostic recommendations
• Multi-modal disease detection: Analyzes uploaded images, semantically compares with textbook reference images to identify conditions
• Explainable AI: Split-view PDF source viewer highlighting exact pages, chunks, and images used in response generation
• Grounded responses with citation chain: Every recommendation traceable to authoritative veterinary literature

Key Capabilities:

• Multi-source review scraping: Trustpilot and ProvenExpert with automated regular updates
• Deployed to Palantir Foundry code repository with scheduled pipeline orchestration
• Sentiment analysis and competitive intelligence for business insights
• Merck Veterinary Manual scraper: 5000+ structured articles for ML/NLP training datasets
• Robust error handling, rate limiting, and anti-detection mechanisms for production reliability