80+ clients. Fifteen posts a month each. One person reviewing.
Three hours a day on Google Business Profile content became two hours a week. The agency stopped producing and started doing strategy.
80+
Profiles
15
Posts / client / mo
3 hrs
Daily → weekly
1
Reviewer
Three hours a day. Generic posts. One bottleneck.
A digital marketing agency managed Google Business Profiles for over 80 clients. Each client needed 15 posts a month. Each post needed to reflect that specific business, in that specific market, at that specific time of year. A plumber in Tampa in December gets different content than an accountant in Phoenix in April.
The SEO person responsible was spending three hours a day on GBP work alone. Quality was inconsistent. Posts were generic. The people who should have been doing strategy were trapped in production.
Per-client intelligence. One dashboard. Scheduled output.
An AI content system using n8n and Airtable. Built around the insight that generic prompts produce generic content. The difference is the research underneath each post. Three layers, one dashboard.
Research per business
The system knows each business: their type, their market, their promotions, what's seasonally relevant. A post for the plumber doesn't read like a post for the accountant, because the research behind each is different.
Single review surface
One Airtable base. One view per client. Review, edit, and approve from a single screen. Batch approval for the ones ready. Individual editing for the ones that need work.
Auto-publish · GBP
Approved posts go live on schedule. No manual intervention. Across 80+ profiles, with seasonal calibration baked in.
The team stopped producing. Started strategising.
- 80+profiles managed through one system.
- 15posts per month per company, generated and published.
- 3 hrs/day → 2 hrs/wkon GBP content. The math speaks.
- →the team stopped producing and started doing the strategic work they were hired for.
05 / The lesson
Generic in, generic out. Specific in, specific out.
The model didn't change. The research did. A generic prompt with a business name produces generic content. A prompt with three layers of context produces content a human would be proud to post.
The first version pulled business type and location. Posts were technically correct but flat. When we rebuilt the research layer to include seasonal trends, recent reviews, and competitor activity, the posts started reading like someone who actually knew the business.
This was our first automation project. The proof of concept that showed AI content systems could replace manual production without the output getting worse.
Next step