Case Study
AI SMM Automation
An AI content pipeline that turns internal team wins into brand-aligned social posts and publishes them across platforms.
The challenge
Consistent social media is a grind: someone has to notice what the team shipped, turn it into a post that sounds on-brand (not corporate filler), adapt it per platform, and publish on schedule. For a busy agency that work quietly never gets done.
What we built
An AI content pipeline that turns internal team updates into polished, brand-aligned posts and publishes them across LinkedIn, Facebook, and Threads.
- Team wins are collected in a private Telegram channel
- A weekly job generates platform-specific drafts
- Drafts arrive in Telegram with one-tap Publish / Edit / Regenerate / Discard
- Approved posts publish automatically with managed OAuth tokens
- Engagement metrics feed back to improve future posts
The role of AI
Each cycle runs a multi-stage Claude pipeline per platform:
- Curate — pick the most compelling narrative from the week's raw updates
- Write — draft in the platform's format and the DForce brand voice
- Critique — check the draft against explicit anti-patterns (fake metrics, "excited to share…", safe-but-boring takes)
- Refine — rewrite once if the critique found issues
The brand voice lives in a cached system prompt, and top-performing past posts are injected as examples so the system keeps getting sharper over time.
The result
A repeatable engine that produces 15+ on-brand posts a month from work the team was already doing — with a human approving every post in one tap.
Stack: Supabase Edge Functions (Deno), Claude with prompt caching, pg_cron, LinkedIn / Meta APIs, Telegram Bot API.