I run businesses with AI.
I came to your site as a design reference for a creative web build I’ve been working on, and found this role instead. I run the operating layer of a seven-figure professional services firm essentially alone, on systems I designed and built, with AI at every layer. This is the job I already do, at the place I’d rather do it.
Three minutes of proof in the video; the evidence below: the firm I operate, the small business I turned into a growth lab, the systems I’ve shipped, and the operating system I’d build at LCA in my first 90 days.
Exhibit A — The day job
A law firm is an agency with different fonts.
A remote corporate law firm runs on the same physics as a design agency. Client work delivered on deadline. Engagements scoped and staffed. Dozens of concurrent matters, from one-off projects to multi-year relationships. Hundreds of invoices a month, run calm. I operate one at a ratio most operators wouldn’t attempt: seventeen producers, one operating layer, built and run by me with AI doing the work of a department. That ratio isn’t heroics; it’s architecture. Building scalable process is my core competency. At DBM, when something works twice it becomes a playbook, and when it works three times it becomes a system.
The model is LCA’s pod model in miniature. Each attorney is a business within the business, with their own clients, book, and P&L. That flips the org chart. The playbooks, the SOPs, and the DataHub (the firm-wide workflow platform our outside CTO engineered, with me as his integral partner) aren’t back-office support. They’re the product. I translate business needs and workflow-optimization opportunities into the features he builds, and as AI coding tools have matured, I increasingly carry pieces of the build myself. My internal customers are seventeen entrepreneurs, and what we ship them is the operating system they run on.
The growth engine is mine too. I personally recruited every attorney behind the expansion, built the systems that gave the firm capacity to absorb it, and meet biweekly with each attorney on growing their book: headcount expansion × revenue-per-attorney expansion. The governance credential is hard-earned. I deployed AI inside attorney-client privilege, a stricter environment than any design agency will ever face.
*Excluding the founding partner’s own book, the honest proxy for my impact. I fence my own numbers; you should want an operator who does.
That’s the discipline underneath everything above: every call answered, every quote followed up, every invoice on time, a standing care rhythm with all seventeen. Four years serving attorneys and NYC building owners, the two least forgiving client bases in America.
Exhibit B — The builder proof
The small-business growth lab.
A traditional small business plus AI leverage: the exact thesis Greg writes about, executed end to end. It started with no website worth the name, no paid acquisition, no field-service software, no review engine. I built the whole growth machine myself while running the firm above. An organic engine around NYC compliance work, an ad campaign engineered with AI, and the operations layer to deliver what the demand engine catches.
Exhibit C — Selected builds
Systems I’ve shipped.
A sample of the operating infrastructure I’ve built or driven. Each one replaced a manual process, a vendor, or a guess.
The stack: systems I run daily
The crosswalk
What you’re asking for. What I’ve done.
The job description, synthesized. Each ask against the closest thing I’ve already built or run.
The actual pitch
My first 90 days at Late Checkout
Mapped to the role: delivery operations, resourcing, client experience, risk visibility, AI governance. Built as systems that scale, not organigrams that suffocate, and built for where you’re going: AI as the team’s sidekick today, agents running more of the operation tomorrow, with governance fit for clients like Dropbox, Grammarly, and Salesforce.
Map the machine
Shadow every delivery pod. Interview every lead. Trace three client engagements end to end: close to kickoff to delivery to invoice. Audit the tool stack and where the data actually lives. No process changes yet; operators who “fix” things in week two break things they don’t understand.
Ship → A complete operating map, a risk & health register, and 3–5 zero-friction quick wins.
Systematize delivery
Standardize the delivery lifecycle: rituals, playbooks, definitions of done. Build the resourcing and capacity-forecasting model so staffing decisions stop being vibes. Design client onboarding as a product, because first impressions are an operations problem wearing a design costume.
Ship → V1 operating cadence, capacity forecast model, onboarding system.
Automate & govern
Layer AI into the operating system itself: a delivery-health agent reading project signals before humans feel the problem, meeting→task→status automation, staffing forecasts from utilization history, an AI onboarding concierge, and the internal agent library. Stand up AI governance covering what we automate, what stays human, and how client data is protected, so the agency practices what it sells.
Ship → Live ops dashboards, automated reporting, AI governance framework v1.
Why here, specifically
Why Late Checkout
I’ve followed Greg’s work for years, long before this role existed. The honest version of how I got here: I was pulling the LCA site as a design reference for a creative build of my own, saw this role, and couldn’t not apply. I believe in the model, I think Greg understands where the world is going and what it takes to win in the AI era, and I’d like to build the operating system underneath that.
The thesis I’ve bet my career on is the one LCA embodies: a small team with taste, AI leverage, and real systems can out-build organizations ten times its size. I’ve proven it three times: inside a law firm, inside a 58-year-old family trade business, and as a solo founder. I’m not job hunting, and I’m not optimizing for comp or title. This is the one role I’d rearrange things for.
Based in Connecticut (EST), fully remote-ready, available now.