Wayne Fourie
Senior AI & Software Engineer | LLM/RAG, Computer Vision
New to Contra
Wayne is ready for their next project!
CamBridge - Camera Middleware
Engineered a protocol translation layer that bridges Chinese IP cameras (Hikvision, Dahua) with professional NVR platforms like Milestone and Genetec. The middleware abstracts vendor-specific protocols into a unified interface for stream management and event processing.
Key features:
- Protocol handlers for Hikvision ISAPI, Dahua, and generic ONVIF
- Real-time event subscription (motion, line crossing, face detection)
- RTSP stream discovery and proxy configuration
- 24/7 health monitoring with automatic stream loss detection
Tech: Python, asyncio, aiohttp, RTSP, ONVIF, ISAPI, FFmpeg
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Agentic RAG Pipeline
Created an intelligent email triage system that classifies incoming emails and generates context-aware draft responses using retrieval-augmented generation. The LangChain agent uses tools to search a knowledge base, check customer history, and apply escalation rules.
Key features:
- 7-category classification with 4 priority levels
- Tool-augmented LLM agent for dynamic information retrieval
- Vector knowledge base storing policies, FAQs, and response templates
- Structured output with confidence scores and routing recommendations
Tech: Python, LangChain, GPT-4o, ChromaDB, OpenAI Embeddings, Pydantic
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Trademark Similarity Engine
Developed a visual similarity search system for trademark infringement detection using CLIP embeddings and PostgreSQL vector search. The system encodes trademark images into 512-dimensional semantic vectors and finds visually similar marks in milliseconds.
Key features:
- 87-94% accuracy on trademark similarity detection
- Sub-50ms query latency using pgvector IVFFlat indexing
- Scales to 1M+ trademarks with approximate nearest neighbor search
- Configurable similarity thresholds with automatic infringement flagging
Tech: Python, CLIP (ViT-B/32), pgvector, PostgreSQL, FastAPI, PyTorch, GCP
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Built an intelligent document extraction engine that automatically parses invoices using GPT-4 Vision. The system extracts 15+ field types (vendor info, line items, totals, dates) with 93.5% accuracy and includes confidence scoring for each field.
Key features:
- Multi-modal LLM processing for PDF, images, and scanned documents
- Cross-validation logic that catches extraction errors by comparing line totals vs subtotals
- Async batch processing for high-volume workflows
- Pydantic schemas for type-safe, structured output
Tech: Python, GPT-4 Vision, LangChain, Pydantic, FastAPI
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Built an intelligent document extraction engine that automatically parses invoices using GPT-4 Vision. The system extracts 15+ field types (vendor info, line items, totals, dates) with 93.5% accuracy and includes confidence scoring for each field.
Key features:
- Multi-modal LLM processing for PDF, images, and scanned documents
- Cross-validation logic that catches extraction errors by comparing line totals vs subtotals
- Async batch processing for high-volume workflows
- Pydantic schemas for type-safe, structured output
Tech: Python, GPT-4 Vision, LangChain, Pydantic, FastAPI
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