A decade leading global customer support, and the AI and automation I build on top of it.
You are not hiring a rehearsed pitch. You are hiring a person: driven, honest, harder on himself than any boss. I hear a problem and my first thought is how do I fix this now.
A decade in customer support and service taught me the thing no tool teaches. Operations are people first. Get that right, and the technology finally has something worth amplifying.
A 32-person support org across the US, EU/UK, China, and the Philippines. Hiring, structure, coverage, and culture across four time zones and eight-plus languages.
I ran CX as the thing the operation is built around, not a queue to clear. ~90% CSAT held steady through a full automation rollout, because service quality was the constraint I protected.
I get fired up when someone on the team levels up because of something I built. I lead by building trust, not carrying a big stick. The measure is whether the people around me grow.
When the highest-leverage work moved to building, I hired and trained a successor for the people-management side and handed it off cleanly. The team was stronger for it.
None of these were assigned. I saw the gap, learned what I needed, and shipped working systems that proved their value in production before they ever had a budget line.
Support capacity gated response speed and quality. We wanted to own the customer-facing voice end to end, with deeper knowledge-base integration and per-response quality scoring, instead of moving at a vendor's pace.
Built an autonomous support AI from scratch on the Claude API, wired into the existing ticketing system. Domain-specific knowledge layer, multi-step reasoning, quality scoring on every response. Shipped without formal headcount approval.
Touches every inbound ticket and fully resolves 40 to 50% autonomously (the ceiling is data-privacy policy, not capability). Nets roughly 8 to 12 FTE of created capacity at ~90% CSAT, about $500K a year. Successor CARA 3.0 is in development.
About 35K RMA records sat in a spreadsheet with no real-time view for engineering, product, or QC. Quality spikes surfaced weeks late, in monthly review.
Built a dashboard with incremental sync (135x faster than full pulls), multi-device trend overlays, and severity-scored spike alerts.
Quality data is now shared across engineering, product, and QC. Per-device drill-downs surface SKU patterns monthly reports miss, and spike alerts catch issues weeks earlier.
Fraudulent lookalike sites were impersonating the brand on similar domains. Nothing detected them; issues surfaced only when customers complained.
Python scanners for DNS-twist analysis and Certificate Transparency logs, plus a search crawler, feeding a dashboard with a documented takedown workflow.
Catches lookalike domains in CT logs within hours of registration. A reactive, complaint-driven problem became proactive defense.
A single inbound mailbox served 22 distinct audiences across 8-plus languages on a broken pipeline that mis-routed daily.
Built a classifier on Claude Haiku with a multi-rule routing table and overrides (support topics always go to global support, regardless of language). A dashboard turns human corrections into few-shot training.
The inbox triages itself with a self-improving feedback loop, and the support-topic policy holds across all 22 categories.
Zendesk got support to a working baseline. As we matured, we wanted a platform that fit our workflows exactly: custom routing, deeper CARA integration, and the freedom to ship UI on our own schedule.
Built a complete platform from scratch, 48 routes, 19 workers, 61 database migrations, a three-panel CARA simulator, Zendesk article import, and the full ticket lifecycle.
A working, production-ready platform, held on purpose pending an organizational chat-strategy call. Activatable the moment alignment lands. Knowing when to wait is part of shipping.
AI tools only work if the team beside them does. Without a documented framework and real training, even strong systems gather dust.
Co-authored an enterprise-wide AI Playbook with executive leadership, stood up an internal Claude interface, and ran intro training for ~20 staff with a full enablement kit.
The adoption framework is live across the company and Level 2 power-user training is queued. The pattern repeats: build the system, train the people, measure.
Click any system to open the full case study. Want more than the summary? Ask my AI anything about how these were built.
Reverse-chronological. Roughly the last twelve months of work that landed in production or made real progress.
Replaced a broken pipeline with a Claude Haiku classifier that routes one mailbox to 22 audiences and learns from human corrections.
Real-time hardware quality intelligence over a 35K-record dataset, with severity-scored spike alerts.
Stood up an internal Claude interface and ran hands-on training for ~20 staff with a full enablement kit.
Dual-server MCP architecture connecting Claude directly to internal Bitable, calendar, tasks, and docs.
Brand-protection monitor watching Certificate Transparency logs for fraudulent lookalike domains.
Feedback-intelligence platform over 12K+ support tickets, surfacing themes and sentiment in real time.
Continuous competitor monitoring, quiet daily output for sales, marketing, and product.
From-scratch Zendesk replacement, built and validated, held on purpose pending a chat-strategy call.
Next-gen agentic architecture that brings the AI stack fully in-house. Active development.
The original autonomous support AI, and the anchor of everything above.
Knowing systems is one thing. Knowing systems and people, so they actually work together, is the whole job. Three places it shows up.
CARA takes the repetitive 40 to 50% so the team spends its time on the hard, human half: judgment, relationships, the edge cases. The capacity it creates is not headcount removed, it is headcount freed to do work that actually needs a person. We held ~90% CSAT straight through the rollout.
A tool nobody adopts is a hobby. So the systems ship with the other half. I co-authored an AI Playbook with leadership and ran hands-on training. The build and the adoption both get my time, on purpose. That is why the systems actually stick.
When building became the higher-leverage work, I did not just walk. I hired and trained a successor for the people side, handed off cleanly, and moved into a full-time AI and Automation role. The measure of the work is whether the people around me are better for it, not whether I am indispensable.
Operations are people before they are systems. Get the team, the coverage, and the culture right, and the technology finally has something worth amplifying. Skip it, and you have just automated a mess.
Working systems beat decks. CARA, SENTINEL, the QC dashboard all ran in production before they had a budget. The fastest way to fund something is to make it quietly indispensable first.
Iron Man works because Tony Stark is inside it. AI amplifies what good people already do, and exposes what weak process was hiding. Without the human judgment inside, the technology is just code.
I can excel and lead. I can also burn out and have a bad day. Those are human things. I do my best work where people get that, and where I can be the one they count on to get it done.
I started at Apple support in 2014, advanced through Special Projects, and served as Team Lead Admin on the AMR Commitments Team. Nine years there taught me to treat customer experience not as a department, but as the discipline you build everything else around.
At COROS I came on to lead Customer Support in October 2023, building and running a 32-person global team across four regions. That is the half of my background people tend to skip past, and it is the half that makes the rest work.
It is one thing to know a system. It is another to know systems and people, and get them working together. That overlap is where I live.
By mid-2025 the highest-leverage thing I could do had shifted from managing people to building the systems that scale them. So I designed my way into it. I hired and trained a successor for the people side and moved into a full-time AI and Automation role. CARA, SENTINEL, RELAY, the QC dashboard: none were assigned, all got built and proven before they had a budget.
I have never been the guy who interviews easy or shows off well. What you get instead is honest, driven, and steady. Find the real problem, build the thing that solves it, bring the people with you, measure everything, move on.
I want roles where operational leadership and hands-on AI building are not two different people. Customer experience, support operations, or automation with real stakes. Also open to select consulting through Infinitus Digital.