MKTG: 120 hours a week on AI agents | Svolta case study
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Case study · MKTG

MKTG runs research, copywriting and reporting on AI agents, saving around 120 hours a week

MKTG, a marketing agency, was burning senior hours on in-depth research, copywriting and client reporting. We built a set of AI agents that now run that work, with the team reviewing output instead of producing it from scratch. The measured saving is around 120 hours a week across the agency.

~120 hrs/wk
production hours saved across the agency
3
workflow families on agents: research, copywriting, reporting

The constraint

Agency work has a production problem: the billable output (research decks, copy, client reports) is senior-hours-intensive, repetitive in structure, and always due. Scaling meant hiring, and hiring into work that is 'the same deliverable again, for a different client' is expensive and slow. The work also could not drop in quality; clients read every word.

How we approached it

We mapped the recurring deliverables first: where the hours actually went across research, copywriting and reporting. Then we built a series of AI agents against those workflows: research agents that assemble the in-depth groundwork, copywriting agents that produce first drafts in the agency's voice, and reporting agents that turn campaign data into client-ready reporting. The team's job moved from producing every deliverable to directing and reviewing agent output.

What changed

Agents now run research, copywriting and reporting production, saving the agency around 120 hours a week. The team reviews and ships instead of drafting from a blank page. MKTG has agreed to be named as a client; further detail on the engagement is available on a call.

The constraint

The trap in agency economics is that the fix for too much production is more people, and that fix arrives slowly. A senior hire takes months to find and longer to sound like the agency, while the deliverables keep landing on the same desks. The people best placed to grow the business end up spending their weeks producing for it instead.

What made this workload worth building against was its shape. The content changes with every client, but the structure of the work barely moves, and work that repeats in structure while varying in content is the kind agents handle well, provided a person stays on the judgment. MKTG’s production line was almost entirely made of it.

The approach

The build order came out of the map, not the other way around. Looking at deliverables rather than job titles showed where the hours concentrated, which of the three workflows carried the most weight, and what a good result looked like for each one.

We built one agent family per workflow rather than a single general assistant, because the three workflows fail differently. Research fails on depth and sourcing. Copy fails on voice. Reporting fails on the numbers. A bounded agent with one job can be reviewed against a clear standard; a do-everything assistant cannot.

Every family ends at the same review point. The agents changed who does the first pass on a deliverable. They did not change who is accountable for the last one.

The outcome

Around 120 hours a week saved, measured across the agency, and the shape of the week changed with the number. Deliverables now start from agent output instead of a blank page, and the senior hours that were hardest to hire for are the ones that came back.

MKTG agreed to be named as a client, so we can publish the agency and the number together. What sits behind them is better discussed than written; book a call if you want the detail.

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