AI & Intelligent Products
I build AI-powered Apple products — I don't train the models.
My work sits between research and production: taking a validated model or a well-scoped LLM feature and turning it into something reliable, fast, and honest about what it does with a user's data.
At Magnosco, I work alongside scientists building computer vision solutions for skin cancer diagnostics — integrating multiple CoreML models into a production iOS application, building the OpenCV pipelines that feed them, and translating PyTorch model logic into efficient Swift. I am not an AI researcher, and I don't train foundation models. What I specialize in is the harder-than-it-looks work of bringing AI technologies into a production-quality Apple product — reliably, on-device, and with privacy treated as a starting constraint rather than an afterthought.
Where I work
From research prototype to production pipeline
CoreML
Deploying, versioning, and optimizing trained models on-device — managing compute unit selection, quantization, and the lifecycle of multiple production models at once.
OpenCV
Building preprocessing and feature-extraction pipelines that turn raw camera or sensor input into consistent, model-ready data in real time.
Medical Imaging
Working directly with imaging scientists to turn validated research into a pipeline that holds up to the accuracy and consistency bar a clinical tool requires.
Image Processing
Color space normalization, orientation correction, and region-of-interest extraction — the unglamorous steps that determine whether a model's output is trustworthy.
Feature Extraction
Structuring the path from raw pixels to the specific signal a model was trained on, tested independently from the model itself.
Edge AI
Designing inference to run entirely on-device — for latency, for reliability without a network connection, and because it keeps sensitive data off a server entirely.
On-device Inference
Profiling models for real-world thermal and battery budgets, not just accuracy — the difference between a demo and a feature people use all day.
PyTorch Collaboration
Translating a research team's PyTorch model logic into efficient, numerically faithful Swift and CoreML implementations, with tooling to catch drift early.
LLM Integration
Scoping language-model features narrowly around specific tasks, favoring on-device foundation models where possible, and being explicit with users about what data a prompt contains.
Prompt Architecture
Treating prompt templates as product surface, not implementation detail — versioned, reviewed, and designed with constrained output formats and sensible fallbacks for when a model response doesn't parse.
Claude API Integration
Wiring Claude into narrowly-scoped product features via the Messages API — structured prompts, minimal payloads, and no server-side retention of conversation history by default.
Privacy-first AI
Treating every AI feature's data flow with the same scrutiny as any other privacy-sensitive code path — minimal payloads, no server-side retention by default, honest UI copy.
Case study
See it in a real product
The full story of Magnosco's diagnostic imaging pipeline — device integration, CoreML, and mTLS networking.