Enabled AI across an enterprise
When Shopify's CEO mandated an AI-first operating model, I didn't wait for a playbook. I started small, proved value, and let adoption pull the work across the org. Each tool built on the last.
"While You Were Away" Agent
Six-Week Executive Reviews
Program Briefs Agent
MCP-Powered Dashboards
Performance Review Agent
Built AI for clients
After Shopify, I went independent. First real engagement: a skincare brand drowning in repetitive customer questions and manual clinic management.
Multi-Agent Customer Service System
Three connected AI systems: a customer chatbot grounded in website content, a clinician agent for licensed professionals, and an admin dashboard so non-technical staff manage both agents themselves.
The first version had staff using the OpenAI platform directly. It didn't stick. So I built the admin dashboard - brought the tool to the user instead of bringing the user to the tool. That principle has guided every build since.
Built my own products
Client work proved I could ship production AI for someone else. Next: building products I own, where I'm the user, the designer, and the operator.
reDiscover
Your streaming history has thousands of songs you've played dozens of times but haven't heard in months or years. reDiscover finds them and brings them back.
Built by a musician who wanted to solve his own problem. Hybrid data ingest (one-time privacy export + daily API sync), AI-powered music chat, monthly concert digests for artists you actually listen to. Designed the visual identity and built a cohesive design system across the product.
Signal
Scrapes openings across ATS platforms (Greenhouse, Lever, Ashby, Workday, and more), scores them against configurable criteria, discovers hiring contacts, and drafts personalized outreach.
Two-agent outreach drafting (drafter + formatter). Dead role detection with auto-close. Auto-detects ATS platform from job URLs.
Key decision: Two-LLM cost split. Sonnet for high-stakes scoring and outreach, Haiku for high-volume classification. ~90% cost reduction without sacrificing quality where it matters.
Went deeper
I'd shipped production AI across enterprise, client, and personal contexts. But I knew there were gaps. At Shopify, I'd consumed MCP servers (Looker, Salesforce, GitHub, Figma) but never built one. I'd built individual agents but never orchestrated multiple agents with genuinely different jobs working as a pipeline.
So I mapped the skills I wanted to develop and applied each one to real work. Not tutorials. Not demos. Real systems running in production right now.
Foundation Server
The gap: I'd consumed MCP servers at enterprise scale. Never designed the tool interface, handled auth, or built the serve-and-capture patterns from the other side.
A custom MCP server that serves professional context to any AI session - Claude, Cursor, any MCP-compatible client. But the real innovation is the capture loop: every AI session generates knowledge, that knowledge gets proposed back to a central source of truth, and nothing changes without review.
It's a PR workflow for AI-generated knowledge. Propose, review, merge. Most people's AI tools start from scratch every session. This system compounds.
KB Quality Agent
The gap: I'd built individual agents. Never orchestrated multiple agents with genuinely different architectures working as a pipeline.
My skincare client's chatbot was built on a point-in-time website scrape. Every time they updated their site, the knowledge base drifted. A three-agent pipeline that monitors, evaluates, and drafts updates for human review:
Same propose/review/approve pattern as the Foundation Server. Same architecture, completely different domain. The pattern proved portable.