scalematic.
Implementation GuideAgentsn8nClaude 3 min read · v1.0 · July 2026

The Content RAG Agent

Build a retrieval-augmented content agent: collect your knowledge base, embed it in Pinecone, ingest via n8n, wire up a RAG agent with live social-listening signals, and generate on-brand written and video drafts from Slack.

Executive summary

What problem does this solve?

Generic AI writes generic content because it has no memory of your voice, your playbooks, or what already worked. Without a retrieval layer over your own material, every draft starts from scratch and sounds like nobody.

Business outcome

  • An agent grounded in your own SOPs, posts, and calls
  • On-brand written and video drafts generated on demand
  • A knowledge base that improves as winners are added back
Revenue maturityLvl 6–9

Advanced content infrastructure. Build after the Content OS is running.

Implementation effort
40implementation hours
People required
FounderMarketingSalesRevOpsDeveloper

An n8n + Pinecone build — comfort with workflow tools required.

DifficultyAdvanced
Business impactHigh
Time to install1–2 weeks
Automation70%
MaintenanceMedium
OwnerContent Engineering
Required software
ApifyErgoGoogle DrivePineconeOpenAIn8nClaudeJunglerTweet HunterClaySlackOrdinalbeehiiv
Required integrations
Google DrivePineconen8nSlackOrdinalTweet Hunterbeehiiv
Architecture

How the system fits together

The Content RAG Agent — system architecture 1 AI steps
100%
01 · Collect
02 · Embed
03 · Ingest
04 · Configure
05 · Signals
06 · Generate

Hover a node for detail, or tap a tool below to see where it runs.

Highlight tool
The problem it solves

Generic AI writes generic content because it has no memory of your voice, your playbooks, or what already worked. Without a retrieval layer over your own material, every draft starts from scratch and sounds like nobody.

Expected outcomes
  • An agent grounded in your own SOPs, posts, and calls
  • On-brand written and video drafts generated on demand
  • A knowledge base that improves as winners are added back
Who it's for
  • Content teams adopting AI
  • Founder brands
  • GTM engineers
Before you start
Human-in-the-Loop Content Operating System
Implementation

9 steps, start to finish

Gather all internal content into one structured folder.

  • Pull LinkedIn posts from target creators using Apify
  • Include SOPs, playbooks, and copywriting
  • Include blogs, podcasts, previous posts
  • Include sales call recordings via Ergo
  • Save all files to a Google Drive folder

Prompt library

Copy-ready prompts

Tuned for Claude, GPT, Gemini, and Grok. Copy and run.

RAG content agent system message
You are a content agent for [BRAND]. On EVERY request, first query the connected vector store to retrieve the most relevant internal material — SOPs, past high-performing posts, and call excerpts. Ground every draft in that retrieved material and the brand voice it encodes. Never write generic business language. If retrieval returns nothing relevant, say so rather than inventing. Output the draft plus the sources you retrieved.
FAQ

Common questions

Related resources

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Automated LinkedIn-to-Blog Publishing

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Install this system

Build it yourself, or have it installed

The documentation above is complete — everything you need is on this page. The only question is whether you want to spend the time.

Do it yourself

Free · 1–2 weeks

Have ScaleMatic install it

Done with your team

Follow the documentation
Complete implementation
Configure every tool yourself
Tool configuration included
Troubleshoot issues yourself
Tested and supported setup
Train your team on it
Team training and SOPs included
Time investment: several hours or days
Guided implementation

We diagnose the constraint first — if this system isn’t what you need, we’ll say so.