Anyone can publish once. I build the machine that does it every week.
Teams, workflow, standards, distribution, and measurement — assembled into an operation that holds its quality bar without me in the room.
Six parts of the machine.
Most content operations have two or three of these. The missing ones are why quality slips the moment volume goes up.
Team and roles
Who decides, who makes, who checks. Most quality failures are actually an undefined decision right.
Workflow and cadence
A pipeline with named stages and real deadlines, sized to what the team can hold every week — not on a good week.
The quality bar
Written standards for sourcing, expertise, and voice, so a piece can be judged against something other than taste.
Distribution and segmentation
The platform layer: who gets what, when, and why. Coverage is worthless if it lands in one undifferentiated list.
Analytics people act on
Numbers routed to the person who can change something, in language they already use. Dashboards nobody opens are decoration.
AI-assisted production
Machines on the parts that are mechanical, humans on judgment and verification. The line moves every quarter and has to be re-drawn deliberately.
No platform, no list, no team. Then a publishing operation.
A mandate to reach senior technology decision-makers, and nothing to do it with. No publishing platform, no subscriber list, no editorial team, no standards, no analytics.
Hired and managed four editors. Defined the coverage areas, cadence, and quality bar across AI, cybersecurity, cloud, and workforce transformation. Migrated Mailchimp → Iterable and built segmentation for an enterprise subscriber base. Wired the output into sales, CS, and enablement.
10,000+ subscribers in under a year against a 5,000 goal, 65%+ open rates, 6%+ clicks — produced by a documented operating model a team could run without me.
Run at three very different speeds.
A nightly comedy show, a global business newsroom, and a B2B intelligence program need the same discipline and completely different metabolisms.
Helped build a digital department that could turn a strong editorial voice into platform-native execution the same day — social, video, live experiences, books, and activations.
Emmy AwardStrategic newsroom team reversing a subscription plateau: a 209-page roadmap, a flagship newsletter relaunch, and bureau strategies driven by analytics.
3.7M subscribersA market intelligence operation for technical buyers, built from zero and positioned as independent coverage rather than content marketing.
10K+ in one yearWhat AI actually changes about editorial operations.
It collapses production cost and leaves judgment as the only scarce input. That inverts the org chart of a content team: fewer people producing drafts, more people deciding what is worth saying, verifying it, and owning the standard. The operations problem becomes quality control at volume, not volume.
I’d rather show than argue. I’ve built eight AI systems end-to-end — a context layer, an ops layer, and full editorial products with CMSes and reader apps. That’s the same architecture a company needs for market intelligence, tested on my own workflows first.
Publishing more
than you can hold?
Usually it’s not a headcount problem. It’s a missing workflow, an undefined quality bar, or analytics nobody acts on.