AI Social Content Production Pipeline

social · content · ai · scheduling · ecommerce

The problem

Every social post for an e-commerce brand needs a picture of a real product in a plausible setting and a caption that sounds like the brand. The picture normally means a photoshoot, or reusing the same three catalog shots until they are worn out. The caption means someone opening the product record, checking materials, colour and plating, and writing it by hand for each channel.

What was built

An n8n pipeline. Input is loose: product names, post type, notes, target channels. Output is a set of finished posts and a calendar entry for each one.

  • Product names are matched against the brand's real catalog, so the copy uses the actual materials, colours and finishes rather than a guess.
  • Images show the real product placed in different environments, generated per post instead of shot.
  • Captions are written per channel, against the brand's own guidelines and post type (showcase, UGC-style, how-to, promo).
  • Everything lands as a schedule entry: date, weekday, platform, caption, media URLs, preview link.
  • Posts are linked back to the product record they feature.

Visual examples

Stack

  • Orchestration: n8n
  • Catalog and calendar: Notion
  • Content and imagery: language and image-generation models

Credentials

Our Apollo.io node is published on npm as n8n-nodes-apollo (MIT, maintainer arturl95, 14 resources, source public). We hold n8n Verified Creator status.


Want something like this built?

Tell us what the workflow needs to do and we will tell you whether it is a fit, what it would take, and what it would cost.

Start a conversation