Dylan McCapes

A production clinical decision-support platform I designed and built end-to-end — backend, web, mobile, and the retrieval brain in between.

An async FastAPI backend on PostgreSQL + pgvector, a React/TypeScript web client, and a React Native mobile app. Clinical answers come from a hybrid retrieval layer — BM25 + ANN embeddings over a unified medical knowledge graph that fuses SNOMED concepts, clinical guidelines, and a custom "Ethos of Health" patient-state model (baseline integrity, chronic-baseline mode, and per-diagnosis stability "stack levels").

Built HIPAA-forward from the start: encrypted logging, token-gated B2B routes, and auth on every sensitive endpoint. I owned it from architecture through deployment.

Point it at a codebase you've never seen and it tells you how the thing actually works — and where the bodies are buried.

A hybrid-RAG pipeline (ripgrep + embeddings) runs a probe → gap → report loop across ten dimensions of a system — data layer, APIs, UX, security, testing, and more — with timestamped human-in-the-loop governance gates, dual-written logs, and per-run metadata.

The real test: executives wanted to know whether two sister companies' shower-controller programs (Moen and Aqualisa) could be merged. Instead of months of manual manager review, FullMetalPacket analyzed every codebase and produced reports that spoke for themselves — the decision was made not to merge.

A code-intelligence tool tuned for Android Studio projects — index a repo, then actually talk to it.

The indexing pipeline goes skeleton → dependency graph → LLM enrichment → embeddings, then answers in two modes: an engineer-mode chat for digging into code, and a product/architect mode that writes phased design docs. It also exposes fast no-LLM paths — direct grep and semantic search — plus an Android-docs fetcher, dependency-graph summaries, and governance gates on the expensive phases.

It's the middle link in a lineage: the FullMetalPacket idea, sharpened for one ecosystem, on the way to ProScope.

"Vibe-coding" with guardrails. Describe a feature in plain English; ProScope turns it into phased plans, plain-English strategy docs, and reviewable git diffs — and never writes anything without your approval.

Every step ends at a Proceed / Question / Abort checkpoint, so a founder or product lead stays in control while the model does the heavy lifting. It runs fully locally on Ollama with GPU-probed model tiering (7B / 14B / 32B), is language-agnostic (Python, TypeScript, Go, Rust, Kotlin, Swift, and more), and offers two front ends over one backend: a pure-PyGame "mission control" GUI and a matching CLI.

It ships a deterministic end-to-end test harness that drives the full plan → strategy → implement → commit loop with a scripted stand-in for the model — so it runs in seconds with no Ollama and no network.

An education engagement for an e-commerce company: take a team from "we use AI tools" to "we understand the systems underneath them."

The running example is a 10-"book" advertising knowledge assistant that grows across the course — staged-reduction RAG, an MCP server exposing tools, an agentic loop, and a "score-keeping" (Bayesian) belief-update layer that decides what to trust. It ships a fully offline, pre-built HTML reader (markdown + visual explainers compiled by a Python builder) alongside runnable RAG / MCP / agent code.

I delivered a live 90-minute foundations session from it; it's scoped as a potential multi-lesson series.