Seeing what others miss

I build AI systems that work with images, video, and other visual data as well as text. I came to this through digital design and image processing, so I start every computer vision project already knowing how the pixels were made.

What I build

• Computer vision pipelines: Object detection, image segmentation, visual inspection, and real-time video analysis built on OpenCV, TensorFlow, and PyTorch
• Image processing systems: Automated enhancement, transformation, classification, and metadata extraction at scale
• OCR & document intelligence: Extract structured data from scanned documents, invoices, receipts, and forms
• Visual quality control: Automated defect detection, anomaly identification, and compliance checking for manufacturing and logistics
• LLM-powered features: Search, summarization, classification, and generation built on OpenAI, Anthropic, or open-source models
• Custom model training: Fine-tuned models on your domain data when off-the-shelf isn’t enough

How it works

Every AI integration starts with the problem, and the technology comes later. For vision projects, I evaluate image quality, lighting conditions, edge cases, and data volume before selecting an approach. I handle the full pipeline: data collection, preprocessing, augmentation, model training, integration, and monitoring in production.

My background in digital design means I know color spaces, resolution, compression artifacts, and visual perception, and those details decide whether a system that reads images works.

Use cases

• Manufacturing quality control that catches defects too small or fast for a person on the line
• Medical image analysis that assists radiologists with anomaly detection
• Retail inventory management using real-time shelf and product recognition
• Automated document processing that replaces hours of manual data entry
• Security and surveillance systems with intelligent motion and object detection
• E-commerce product image classification, tagging, and visual search

Related reading

• When Not to Use AI: how I decide whether a problem actually needs machine learning
• Less Is More: Why I Reach for Simpler ML Models First: the case for the simplest model that works
• Ethics in AI: The Right Way Is the Hard Way: where I draw the line on training data and generative models