Shipped with
Building AI decision-support across MixShift’s Amazon analytics platform — onboarding ops, eval infrastructure, and the operator-grade interfaces that turn the model output into something seller and advertising teams open every day.The case study lands when the engagement wraps.
See the rest of the workLong engagements, inside your team, on your tools — not a vendor who emails PDFs. I join your Slack, your standups, your codebase, and your on-call rotation if it matters. I stay long enough to make the systems ship, and long enough to hand them off cleanly.
Every AI system your team uses has to be operable by humans. A decade of design engineering — Disney, Apple, and a long list of startups before that — taught me that information architecture matters as much as model selection. Dashboards, trace viewers, eval tools your team will actually open.
Six-month engagements end with your team owning the system. Code, prompts, infrastructure, runbooks — yours. No lock-in, no retainer-for-life. If the engagement extends, it extends because it's delivering. If it doesn't, you're not stuck.
Why thinking in components — not pages — is the foundation of every maintainable interface. And why the same discipline matters even more for AI systems.
Founder LessonsThe fastest way to kill a product isn't shipping a bad feature — it's shipping five mediocre ones. A case for severe scope discipline, and why AI systems make the trade even starker.
Founder LessonsUX engineering sits between design and development. The mindset is constant questioning — why this button, why this path, why this interaction — and the willingness to dig into both code and craft to answer.