AI Deployment Practice

Most AI rollouts fail in the same place. The tools get installed, three people try them for a week, and everything quietly goes back to how it was.

The problem was never the software. It's that nobody mapped the work first.

I build AI deployments that teams are still using in month six.

Why I Can Do This

I didn't learn this from a certification.

I run a multi-product studio on a context architecture I designed and built myself — a persistent memory layer that carries project state across every session, every model, and every tool I use. It is the reason one person can operate a portfolio this size. It has been in production for months.

That system is also where I learned what actually makes AI stick inside a working business: not the model, not the integrations, but whether the context and the workflows were structured correctly before anyone typed a prompt.

I'll build yours the same way I built mine.

Who This Is For

Teams of roughly five to ten people who have bought AI tooling and haven't seen the return.

You'll recognize yourself if seats are provisioned but usage is concentrated in one or two enthusiasts. If your core systems aren't in anyone's supported connector list. If nobody has written down what data may and may not go into these tools. If someone built a few useful things and then left, and now nobody else can maintain them. If your AI spend went up and you can't point at what it bought.

Smaller teams and solo operators are welcome — start at Foundations below.

What I Actually Do

Map the work before touching a tool. Structured interviews across two functions to find where the hours go. Most engagements surface two or three workflows nobody had thought to name.

Build workflows against your real work. Not demos. Your documents, your systems, your edge cases — validated with the people who will actually use them.

Write the data rules. What goes in, what never does, who approves exceptions. One page, signed off before we build anything.

Leave you a skills library you own. Packaged, documented, and editable by your team. Not locked in my head or my account.

Train twice, two weeks apart. The first session teaches the build. The second addresses the friction the first session couldn't predict. The second one is where adoption is won or lost.

Engagements

Foundations — $2,500, about a week. Setup, supported connectors, three production workflows, data rules, one training session, and a recorded walkthrough you keep. For smaller teams and solo operators.

Standard — $6,500, two to three weeks. Everything above, plus workflow discovery across two functions, up to eight production workflows, a client-owned skills library, one custom connector if your stack needs it, governance and permissions setup, two training sessions, and thirty days of support after handoff. This is the one most teams need.

Custom Integrations — from $4,500. Production MCP server work for the systems nobody else supports: OAuth, remote transport, complete documentation, working examples, and a handoff runbook.

AI Spend Audit — $3,500, about a week. Your production workloads tested against multiple model candidates, with cost and fallback projections and a routing architecture that answers your security team's actual question. Advisory only — you decide what to change.

Ongoing — from $1,500 per month. New workflows as you find them, skills library maintenance, monthly usage review, and migration work when platforms change underneath you. Offered after delivery, never as an opener.

How It Goes

A thirty-minute call, where I ask about your stack and where the work piles up. No deck.

A proposal within forty-eight hours: fixed scope, fixed fee, named deliverables, and a date.

Then the build. You see working output in the first week, not at the end.

Then handoff — training, documentation, and the library. You own all of it.

Things I'll Tell You Before You Ask

I'm not vendor-locked. Claude, Cowork, and the MCP ecosystem are where most of the current demand sits, and it's what I use daily. But the deployment work is the same work regardless of whose model is underneath. If a cheaper model does your job just as well, I'll say so.

Installation gets easier every release. The vendors compress setup friction constantly, and if all you need is a toggle flipped, you don't need me. What doesn't get easier is deciding what to build, keeping it maintained, and getting a team to change how it works. That is what you're paying for.

Macrolific is a boutique studio and I'm the one doing the work. You'll talk to me on the call, and I'll be the one in your systems. There is no handoff to someone junior, because there isn't one.