Hey, I'm Chris
At LiveData, a perioperative platform used in 90+ hospitals, I own product strategy. And unlike most leaders at my level, I still build: RAG pipelines, agents, and the prototype behind our AI Advisor. What I care about most is AI that holds up in production, where being wrong is expensive. The path here ran through fly fishing rivers and ski shops, but that is a story for the About page.
Tinkering with AI agents, shipping side projects, and writing about the things that surprise me. My latest build partner is my 8-year-old son. We're making a soccer trading card site together because finding card values is unreasonably difficult.
Thoughts on AI, building software, and the things in between.
Healthcare AI went mainstream this summer, and the industry converged on the same roadmap. Every workflow on it has its highest-dollar instance in the perioperative suite, and nobody's roadmap names the room.
For a decade, medicine threw technology at clinical documentation and most of it failed. Then ambient AI scribes stuck. The lesson: the hard part was never the technology, it was trust.
Two months into running Product, the real shift wasn't the new work. It was that I no longer get to just have the best answer. I have to choose, and live with it.
A Builder is anyone whose default artifact is working software. Why it's agile's original promise finally kept, and what Product becomes when anyone can build.
A year ago I was passed over for the top product role at LiveData. This month I started it. The difference was a year of building.
I'm a product manager who doesn't write code. My son Harley is 8. Together we built pitchpackcards.com in a month using AI. Here's what he taught me about product.
You don't need to pave every road. You need to pave the ones your teams need to move fast on. And right now, with AI, that's most of them.
At mile 70 of the Vermont 100, everything hurts and nothing makes sense. That experience maps almost perfectly onto building and shipping AI products.
I bought a used Sprinter 2500 and turned it into a family adventure vehicle. It was the best product education I've had since guiding fly fishing trips.
How working in Colorado's outdoor industry taught me everything I needed to know about product leadership.
Things I've built, contributed to, or am currently tinkering with.
Built a RAG system that routes queries to department-specific context so Sales, CS, and Product each get answers grounded in their world, not a generic corpus. Designed two deployment tiers (developer-local and serverless) so both engineers and non-technical staff could use it. Includes an AI-powered feedback loop that triages corrections into the knowledge base automatically.
85+ hospital sites, each generating field reports as unstructured PDFs. Built an AI extraction layer that pulls structured data (issues, staffing gaps, compliance status) from documents field staff were already writing. No new process required. A health score algorithm surfaces which sites need attention without anyone reading 85 reports.
A sales tool that generates facility-specific ROI projections with a key design principle: all math is deterministic Python. The AI interprets and narrates, but never touches a number. Three parallel AI agents handle analysis, storytelling, and objection prep. A conversational what-if agent lets reps adjust assumptions live in meetings.
1.3M+ records from 10 federal sources (CMS, AHRQ, VA) unified into a single queryable schema. Built to put any hospital's surgical metrics in context: volumes, quality, costs, workforce. Idempotent, auditable, and designed so adding a new data source is one Python class, not an infrastructure project.
Real-time AI sales coaching for Google Meet. Captures audio, transcribes locally via Whisper (nothing leaves your machine), and delivers live coaching suggestions through a Chrome extension overlay. An MVP exploring what happens when AI can listen to a sales call and help in the moment.
Turn one transcript into 10+ pieces of on-brand content: LinkedIn posts, blog drafts, email sequences. The key decision was making brand voice a first-class input, not an afterthought. Generic AI content is the enemy of adoption. Built with tiered rate limiting and cost tracking from day one, not bolted on later.
Other places you can find me on the internet.