The differentiator this cycle isn't what I do — it's how. I run AI and agentic tooling across the whole production stack, every day, so a single operator can work closer to a team's output without giving up the rigor of reading the actual code and data before making a call.
→ One operator, higher altitude.
Most people bolt AI onto a task or two. I wired it into how the work actually happens — connected across the systems where the real work lives, so the loop from "something's broken" to a validated fix runs at support speed instead of engineering-queue speed. The tools do the heavy lifting; the judgment about what's really wrong, what to fix, and what to tell the customer stays mine.
Diagnose against real production code and data, so an answer is grounded, not a hunch.
Take an issue all the way to a named mechanism before it ever reaches engineering.
Reproduce and confirm the fix, so what goes out is verified — not hopeful.
Deliver engineering a precise ticket, or resolve it directly when it's config or data.
Not one tool — a connected workflow spanning the systems where the work happens. That connection is the whole capability.
Read the connector codebases directly — trace the offending effect or throwing function.
Query live data to confirm what's cached, mapped, or missing — evidence, not theory.
Open the customer record and file the scoped engineering ticket in one motion.
Route the escalation to the right channel so the right people see it immediately.
Inspect the sync's actual behavior against the config that drives it.
Pull the commitments and context from customer calls back into the work.
Exercise endpoints directly for the systems where I have no direct UI access.
Code, data, tickets, comms and tests in a single workflow — diagnosis at support speed.
The capability isn't a claim — it's a whole customer operation re-architected around an AI hub, run by one person. The full story:
My daily driver, and the things I build with it when the workday ends.
The clearest proof of the method is what I build when no one's asking — self-directed, backtested, and genuinely useful.
A from-scratch NFL auction-pricing system — public-data ETL, a backtested model, and a Monte-Carlo decision tool, built solo with AI.
City-records screening, our own neighborhood grade, a Chrome extension and a shared pipeline. Designed by me, built with Claude, used every day.
Product & operations leadership at growth-stage B2B SaaS — where an AI-native operator can do the work of a much larger team.