What I do
Your operating layer is mostly machine work. I take it over, rebuild it AI-native, and run it. Ten to twenty hours a week, real ownership.
Reporting, enrichment, pipeline hygiene, competitive intel, support triage: rebuilt as agent workflows under written rules, quality gates, spending limits, and silent-failure tripwires. People keep the judgment calls.
It starts with an audit. If we continue, I run the function while I build the machine. What you keep is the point: an operating system with my judgment encoded, still working and learning after I leave.
Your stack, your models. I build inside the tools you already run, on whichever model provider you choose. Sales, marketing, support, operations, finance.
Thirteen years, five countries
I've built and led GTM and operations teams in New York, Toronto, Amsterdam, Barcelona, and Rio de Janeiro.
A new kind of company: Flip Education ran this way
Flip Education · flipeducation.ai
An AI co-teacher used in 18 countries and 11 languages. Two people built and ran it: a pedagogy expert who has trained over a thousand teachers, and me on everything else.
Launch took two weeks. Real reliability took two months. By staffing math, a year of work for 15 to 20 people. Day to day, 72 scheduled agents ran it: marketing manager, data analyst, support triage, cost accountant, competitive radar. One written charter, 50 quality gates, continuous evals, a daily cost ledger.
None of it is education-specific. The operating system transfers. The tooling is open source if you want to see how I work.
What an AI-run company looks like
Grouped the way a company is, ordered by impact: agents that ran Flip, tooling that's open source, and patterns I build for clients. If a function is mostly machine work, it can live here.
Marketing
Writes, localizes, and ships. 960+ articles, 215K+ URLs indexed. Ran at Flip.
Every page, every email, eleven languages, overnight. Growth without the translation bill. Ran at Flip.
Watches rankings and page health daily; flags regressions before traffic feels them. Ran at Flip.
Reads the market weekly: competitor launches, pricing moves, positioning shifts. Ran at Flip.
Onboarding, nurture, and win-back emails drafted from what users actually do.
Sales & revenue
Sources and qualifies prospects against your ICP, with a relationship gate so nobody you know gets a template. Ran at Flip.
Every inbound scored, enriched, and on the right desk in minutes, not Mondays.
Keeps the CRM honest: stale deals chased, fields enforced, forecasts reconciled.
Meetings become structured records and follow-ups while you drive to the next one.
First-draft proposals and quotes from the call transcript and your price book.
Tracks a 595-firm pipeline: who was pitched, what was said, what's due next. Ran at Flip.
Support
Reads every ticket, answers the known, routes the new, escalates the risky. Ran at Flip.
Reads support tone and product signals; names the accounts that go quiet before they leave.
Turns recurring questions into help-center pages so the queue shrinks itself.
Leadership & chief of staff
Compiles the numbers, the anomalies, and the questions worth a founder's hour. Ran at Flip.
The fleet's week, distilled to one email: what shipped, what broke, what needs you. Ran at Flip.
Metrics, narrative, and appendix drafted from the systems that already know the answers.
Tracks commitments against targets and asks the uncomfortable question on schedule.
Finance & operations
Polite, persistent, and never forgets. Receivables age less.
Finds the SaaS you pay for and stopped using. Usually pays for itself in week one.
A daily ledger of what every agent and API spends. Pre-spend gates stop surprises. Ran at Flip.
Job posts drafted, applicants screened against the bar, interviews scheduled.
Quality & reliability
Screenshots every key page on every deploy, in every language, and diffs what changed. Ran at Flip.
Every pipeline watched for the failure that makes no noise: outputs that stop changing. Ran at Flip.
Guard scripts between agents and their riskiest actions: secrets, spend, sends, scope, slop, silence. Open source: agent-gates.
Model evaluationWhich model runs each job, decided on evidence: rubric judging, cost/quality Pareto reports. Open source: pareto-evals.
Audits the knowledge base for contradictions, staleness, and entries that no longer earn their place. Ran at Flip.
Memory & knowledge
Reads the company's daily exhaust: agent reports, session transcripts, founder notes. Maintains the living wiki. Institutional memory as an agent. Ran at Flip.
Decisions and owners extracted from every call, filed where the next search finds them.
Deferred verification that survives the session that deferred it. “Check this after launch” actually gets checked.
A new hire asks the wiki before they ask a person. Ramp time drops.
Four ways in
Not sure where AI fits? I spend two to three weeks inside your operation. You leave with a function-by-function map, the reliability and cost gaps, and a sequenced plan with its economics.
My time: ~15 hours a week while it runsYou know what you need. I pressure-test the plan, then build and run it as your fractional COO.
My time: 10–15 hours a week, ongoingYour team executes. I direct from above on a fixed cadence, against a plan we agree on.
My time: 3–5 hours a weekStrategy on tap: AI operations, GTM, revenue. Bring the problem; we work it live.
Typically 2–8 hours a monthPrice bands move with team size and system count. Not sure which way fits? One email sorts it.
Email me