Applied AI with an educator's edge. I design, ship, and validate real systems: agentic pipelines, payment integrity, AI authoring tools. Then I make them usable.
Every authoring job gets its own tool, with a hard gate that won't let an LLM grade its own work.
Production AI systems, hardened software, and the systems thinking that connects them.
A founder's billing prototype had to start taking real money — and a stale test-mode ID had already silently charged a card $138 with no record of it anywhere. I locked the billing data down at the database, made the webhooks heal their own bad data, and wrote 142 tests from zero.
Label a GitHub issue, and a locked-down, automated Claude Code run opens a draft pull request and a preview. The workflow handles git, not the AI. Trust-gated and hardened against prompt injection. I debugged it across a live run of 4 pull requests, then honestly parked it behind a product pivot.
A personal wiki the LLM writes once and keeps current, instead of rebuilding an answer every time you ask. It summarises, cross-links, and flags contradictions across 100 linked pages (none written by hand) drawn from 66 sources. A deliberate alternative to query-time RAG.
Worked out how a user could cheat a SaaS billing system from the browser — give themselves a paid tier, inflate their own credits — and shut it down at the database. Every protected field is locked, and each edit is checked against what actually changed. Proven with 59 allow/deny tests against the real rules in the Firebase emulator.
AbletonMCP, an open-source MCP bridge, could write MIDI clips but never read or truly modify one — so an AI agent had to regenerate each clip from memory, blind to manual edits. I designed and shipped two MCP tools that close the read → modify → write loop, with a 13-test suite. Submitted upstream as PR #106 (+612 / −0).
A three-step agent pipeline that reads an API release, works out which docs it affects, and drafts the updates. A human reviews them before they reach developers.
An LLM that scores submissions against a clear rubric, with checks that catch judgments it can't back up. It runs at a fraction of the cost and time of grading by hand.
A diagnose-and-fix campaign on a ~110K-record address dataset. A 5.8% per-run success rate looked like a broken geocoder — but the real problem was the shape of the addresses. I wrote the normalizer that recovered 47% of the failing backlog and lifted whole-dataset coverage to 98.3%.
A 5-person program that ended the duplicate docs hurting search and AI retrieval. One source of truth per concept, with a reusable 0-4 classifier deciding what stays. ~2,660 lines consolidated across 8 domains, with one site retired.
A five-stage serverless pipeline behind the in-app report-a-bug button: a locked-down screenshot upload, a private GitHub issue, a signature-verified webhook when the bug is fixed, and a two-pass cleanup of old data. Shipped to all users, with about 1.3 lines of test for every line of code.
The CI orchestration that keeps a fragile ~40-job scraping fleet reliable. One failing job can't sink the rest, large-file commits stay intact even when the main branch moves mid-run, and failures are sorted into infra-vs-bug so only the right ones auto-retry.
A synthetic user that reviews documentation the way a senior developer would, catching gaps before real developers hit them.
Owned a B2B learning path end to end — built with subject-matter experts, written for the Functional Architect, with every API example checked against the RAML source. Module 2 passed the automated critique gate at 88/100.
One content structure behind 9 learning paths, 30+ modules, and 200+ pages of technical material — built to scale to thousands of developers.
A 20-hour, hands-on course that takes enterprise developers from zero to production-ready, API first.
A conversational AI that mentors through Socratic, in-character dialogue. An experiment in how far one well-designed prompt system can carry the whole experience.
Co-founded an education company that taught high-schoolers to respond to complex social issues through visual storytelling and hands-on learning. Where the systems thinking started.
The stack I design, ship, and validate with.
I don't just call models. I build with the full agentic toolchain. What sets the work apart is that I actually run the stack, not just the API.
I design, build, and ship production AI systems: agentic CI pipelines, billing systems that don't lose money, AI authoring tools. Then I make them clear to the people and models that depend on them. Building something well and explaining it clearly don't always come from the same person, and I work at both.
As a technical co-founder at storipro, I shipped the production billing layer, hardened the data-security model, and built an autonomous issue-to-PR pipeline. At commercetools, I architected an orchestrator-agent authoring system with an eval harness and drove a single-source-of-truth documentation program across the platform. On my own time I keep building. An LLM-maintained knowledge base, an MCP control layer for Ableton Live.
I came up as an educator, a decade of it across three continents, and that's exactly why my systems land. The real craft of the classroom is earning enough trust that people actually engage. I build the same way, for the people and models that have to use the output.
If you're building applied-AI systems, or you need someone who can architect them, ship them, and make them land, I'd love to connect.