Building automation systems, AI workflows and data infrastructures that turn operations into scalable digital systems.
• AI automation workflows
• Python-based data systems
• API integrations and backend services
• Computer vision experimentation
• Operational infrastructure for digital businesses
My work focuses on designing systems where data, automation and AI interact to create scalable operational processes.
Computer vision system designed to evaluate the quality of real estate images using AI.
Key ideas
- Image feature extraction using modern vision models
- Quality scoring pipeline
- Data-driven evaluation for visual assets
Stack Python · CLIP · Scikit-learn · FastAPI
Status: 🚧 In development
Experimental computer vision pipeline for real-time parking space detection.
Key ideas
- Video frame analysis
- Object detection pipeline
- Real-time parking availability estimation
Stack Python · OpenCV · Computer Vision
Status: 🚧 Active experimentation
Framework exploring coordination between AI models, workflows and operational systems.
Focus
- Capability-based task delegation
- Modular workflow orchestration
- Integration with operational infrastructure
Status: 🚧 Early-stage architecture
Designing the digital backbone for a real estate operation.
Components
- Modular CRM architecture
- Structured relational data models
- Automation workflows
- Operational reporting pipelines
Status: Active development (private repositories)
Systems Architecture
Automation Systems
AI Orchestration
Data Modeling
Operational Infrastructure
Python
SQL
Supabase
n8n
REST APIs
Scikit-learn
• AI orchestration architectures
• Computer vision pipelines
• Workflow automation systems
• Infrastructure for AI-powered operations
Developing digital infrastructures where automation, data and intelligence layers compound over time, enabling scalable and semi-autonomous operations across different business domains.