CASE STUDY — RAG SYSTEM

Agentic
Content Pipeline

Manual content production from brief to publish consumed 6+ hours per campaign with no review checkpoints or retry logic.

n8nOpenAIClaudeSlack

APPROACH

How it was built.

Built retrieval-augmented generation pipeline with document ingestion, chunking strategies, Pinecone and Weaviate vector storage, citation extraction, and webhook chat interface.

RESULTS

Measured outcomes.

Verified Metrics

80% time saved per campaign cycle
Zero manual handoff steps remaining
Full retry and error monitoring deployed

Pipeline Architecture

Pipeline Flow

Brief Input → LLM Agent → Review / Retry → Slack / CMS

Stack: n8n · OpenAI · Claude · Slack · JSON Mapping

TECHNOLOGY

Built with.

n8n AI Agent nodes, OpenAI GPT-4o, Anthropic Claude, Slack webhooks, custom JSON data mapping, error/retry handling, and process monitoring.