Althea AI Marketing

Small clinics can't staff a full marketing team, so campaigns are sporadic and inconsistent — and the WhatsApp and email follow-ups that actually convert rarely happen on time.

• ▸Claude-generated ad creative and copy produced at scale
• ▸n8n-orchestrated WhatsApp + email lifecycle campaigns triggered by customer behaviour
• ▸Predictive audience segmentation to target the right message to the right people
• ▸An always-on content pipeline feeding social, blog, and SEO

• ·AI-generated ad creatives + copy at scale
• ·Automated WhatsApp & email lifecycle campaigns
• ·Predictive audience segmentation
• ·Always-on content pipeline (social, blog, SEO)

The clinic is a Lebanese health & wellness practice with no marketing staff — the classic pattern where campaigns happen in bursts when someone finds time, then go quiet for weeks. The follow-ups that actually fill appointment slots (the WhatsApp message after an enquiry, the email before a lapsed patient drifts away) are exactly the ones that never happen on time when a human has to remember them. The engagement is an ongoing retainer: I built the engine and operate it.

n8n is the orchestration layer — every campaign is a visible, editable workflow rather than code only I can touch. Claude generates the ad creative and copy, which flows into the Meta Ads API for paid placement; lifecycle messaging runs over the WhatsApp Business API and Resend for email, triggered by patient behaviour instead of a calendar. Segmentation is predictive: patients are grouped by how they actually engage, so a re-activation message goes to people drifting away, not to everyone. The same pipeline continuously feeds social, blog, and SEO content, which is what makes the presence always-on rather than campaign-shaped.

The deliberate choice was to build the marketing engine as workflows a non-developer can inspect and adjust, with AI plugged into each step — not a black box. If the clinic ever grows into having a marketing person, they inherit a system they can operate, not a dependency on me.

Marketing that runs continuously without anyone babysitting it, so the clinic stays in front of patients consistently instead of in bursts.

Measurement note: this case study documents delivered functionality and architecture. Client-approved before/after KPIs, adoption data, and revenue impact are not measured or published here.