AI Content Platform: Blog-to-Social Media Repurposing Engine
An AI platform that takes a single blog post and generates 30 days of social media content across LinkedIn, Twitter, Instagram, and email newsletters.
Client: RepurposeAI (Solo founder)
Dashboard showing a blog post URL input at top, with generated content cards below organized by platform (LinkedIn, Twitter, Instagram, Email). Each card shows preview text with platform-specific formatting and a 'Copy' button.
The Challenge
Content marketers know the rule: one piece of pillar content should become 20+ distribution pieces. But the manual work of adapting a 2,000-word blog post into LinkedIn carousels, tweet threads, Instagram captions, and email snippets takes 10+ hours per post. The founder was a content strategist who did this for 15 clients and was drowning. ChatGPT produced generic output that needed heavy editing. She needed an AI that understood platform-specific formats, brand voice, and content strategy, not just summarization.
Our Approach
The key insight was that each platform needs fundamentally different content, not just reformatted text. A LinkedIn post needs a hook, story structure, and CTA. A tweet thread needs punchy standalone statements. An Instagram caption needs emotional language and hashtags. We built platform-specific prompt chains: the blog post was first analyzed for key themes, statistics, and quotes, then each platform generator used a specialized prompt with format constraints and examples. Brand voice was captured in a one-time onboarding quiz that generated a voice profile (tone, vocabulary, emoji usage, CTA style). The output was 30 pieces: 8 LinkedIn posts, 8 tweet threads, 8 Instagram captions, and 6 email subject lines with preview text. Users could edit any piece inline before copying or scheduling. We integrated with Buffer's API for direct scheduling.
What We Built
Delivery Timeline
Day 1-3: Foundation
Auth, database schema, blog post URL ingestion and text extraction pipeline.
Day 4-7: AI Engine
Blog analysis prompt, 4 platform-specific generators, brand voice profiling system.
Day 8-10: Content Dashboard
Generated content cards, platform previews, inline editor, copy-to-clipboard.
Day 11-12: Scheduling
Buffer API integration, content calendar view, direct posting.
Day 13: Quality + Hardening
Content quality checks, format validation, security audit.
Day 14: Launch
Production deployment, seed brand profiles, founder onboarding.
Tech Stack
Architecture
frontend
Next.js with platform-specific content preview components.
backend
Hono on Railway with BullMQ for async content generation.
auth
Better Auth with email magic links.
data
PostgreSQL for users, brand profiles, and generated content.
ai
Claude 3.5 Sonnet with chained prompts: analyze → extract → generate per platform.
Security
rbac
User-level isolation. Content only visible to the creator.
secrets
Buffer OAuth tokens encrypted. Railway environment variables.
monitoring
Sentry for errors. Usage tracking for AI cost management.
qa
Automated quality checks on generated content length and format compliance.
The Results
“I went from dreading content distribution to actually enjoying it. The AI gets my brand voice right 90% of the time. My clients think I hired a social media team.”
Key Takeaways
Platform-specific prompts are non-negotiable. A single 'summarize for social media' prompt produces garbage. Each platform needs its own format constraints, examples, and tone.
Brand voice profiling is the difference between generic AI output and content that sounds like the client. A 5-minute onboarding quiz saves hours of editing.
Chained prompts outperform single mega-prompts. Analyze first, then generate per platform. The analysis step catches nuances that direct generation misses.
Deliverables
FAQ
Frequently Asked Questions
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