In plain terms: this turns your documents into a smart Q&A brain. It reads and remembers your content, then answers questions accurately using OpenAI (LLM), OpenAI Embeddings — great for support and internal knowledge.
10
Nodes
2
Integrations
9
Connections
Manual
Trigger
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The real node graph exported from n8n — triggers (green), AI nodes (violet), services and logic — laid out as they run.
⚙️ How it works
When chat message received· manualChatTrigger
OpenAI Chat Model· lmChatOpenAi
Window Buffer Memory· memoryBufferWindow
OpenAI Chat Model1· lmChatOpenAi
Embeddings OpenAI· embeddingsOpenAi
Wikipedia· toolWikipedia
Calculator· toolCalculator
Nike Agent· agent
Vector Store Tool· toolVectorStore
Pinecone Vector Store· vectorStorePinecone
💼 Business analysis
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Business use case
Builds a retrieval-augmented knowledge system: content is embedded into a vector store and served back as grounded, citeable answers. Ideal for support deflection, internal knowledge bases, and AI assistants that must stay factual.
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Market need
Generic chatbots hallucinate; companies need answers grounded in their own documents. RAG is the proven pattern, and a working pipeline is scarce and valuable.
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Market value
RAG assistant implementations typically run $5,000–$20,000 bespoke. Packaged, it underpins support-deflection tools that sell for $99–$999/mo.