Marketing engineering

I named it Jarvis, after the assistant in Iron Man — because that's the fantasy, isn't it: one calm voice that has already looked at everything and just tells you what matters. The mundane problem it actually solves is less cinematic. A brand's marketing data lives in a dozen tabs — Meta Ads in one, Google Ads in another, TikTok, Instagram, Search Console, Shopify — and nobody, least of all the person paying for all of it, ever sees the whole picture at once.

So Jarvis has one job, stated plainly: gather the fragmented numbers from every channel into a single place an owner can read. Not "go log into seven dashboards." One view that answers the questions an owner actually asks — how much did we spend across everything this week, which campaigns are working, which are bleeding, what should I hand to the agency, and is the agency actually delivering — with the SEO side, from Search Console, folded into the same picture. If a channel has data, Jarvis pulls it in.

That's the other half of what I've been calling marketing engineering. SoiTarot was the bet on making things with AI; Jarvis is the bet on making sense of the numbers that tell you whether any of it worked.

What it actually is

Not a dashboard. A dashboard makes you do the analysis — it just draws the chart and leaves you to read it. Jarvis is the opposite: it does the looking and hands you the conclusion. The design separates three concerns into three layers, with hard boundaries between them:

• Adapters — every data source (Meta, Google, TikTok, Shopify, Search Console) wrapped as its own MCP server.
• Skills — markdown-defined analyses that compose those adapters and apply an actual framework.
• Workflows — plain Python on a schedule that runs the skills and drops a written report in your inbox.

The first principle in the design doc is blunt about why:

"Every data source wrapped as an MCP server. Never call APIs directly from analyzers."

A concrete flow straight from the architecture: at 7am a workflow pulls Meta, TikTok, and Shopify, computes blended ROAS, flags anything that deviates more than 50% from its 7-day average, and emails the summary. The owner reads one page instead of opening seven tabs and doing the math by hand. The shape of that one page (with illustrative numbers):

Built in slices, proven by demos

The project runs on a development log of 10-day cycles, and two of its guiding principles keep it honest. Ship in slices — "every 2 weeks ships something useful, no 3-month invisible builds." And demo-driven — every feature lands with one side-by-side case showing the tool's read against the status quo, recorded in the repo's demo-cases/. The strategy doc even closes on the discipline it takes:

"Build something every week. Show no one. Ship at Day 90."

The capability ladder is deliberately staged — describe → recommend → act — and each rung has to earn the next. Right now Jarvis describes: it tells you what happened, sharper and faster than a manual scan. Only once the describe layer has a pile of evidence behind it does it get to recommend, and only then, for the narrow reversible actions, to act.

Where it's going

The newest slices push past read-only:

• An inbox that drafts its own replies — reading a support thread, checking the order, and writing the response as a draft, never sent. (its own write-up here.)
• Ad creative from inside the tool — wiring an image model to the ad platforms so you can spin up campaign variants for A/B tests without leaving the cockpit. (In development.)

The throughline is a single cockpit for everything an ecommerce brand's marketing actually touches — paid, organic, customer replies, creative — with a human still in the chair for the calls that matter.

For most of marketing's history the leverage was budget and headcount. That's ending. One person who can wrap a data source, write an analytical skill, and schedule it has more throughput than a small team did five years ago — and the ceiling stops being how many reports you can run and becomes whether you know which question is worth asking. I'm betting on the hyphen: not a marketer, not an engineer, but a marketing engineer. Jarvis is the artifact. The real output is becoming the kind of marketer who builds.

The two foundations under it, each its own piece: why I went MCP-first, and why I ship evidence, not features.