In plain terms: this is an AI assistant that works whenever someone chats with it. It uses AI to understand and generate content, so it handles judgement-style work, not just moving data. It connects to OpenAI (LLM), Airtable and completes multi-step tasks for you automatically — the kind of work you'd otherwise hand to a virtual assistant.
30
Nodes
3
Integrations
33
Connections
Chat, Sub-workflow
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
OpenAI Chat Model· lmChatOpenAi
Window Buffer Memory· memoryBufferWindow
When chat message received· chatTrigger
Execute Workflow Trigger· executeWorkflowTrigger
Search records· toolWorkflow
Process data with code· toolWorkflow
Create map image· toolCode
Get list of bases· toolWorkflow
Get base schema· toolWorkflow
AI Agent· agent
Switch· switch
Get Bases· airtable
Get Base/Tables schema· airtable
If filter description exists· if
💼 Business analysis
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Business use case
Deploys an autonomous AI agent that can reason over instructions, call tools, and take multi-step actions on behalf of a team — replacing repetitive knowledge work. It orchestrates OpenAI (LLM), Airtable, HTTP / REST API so a single natural-language request becomes a completed task.
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Market need
Businesses are racing to put AI agents to work but lack the engineering to wire models to their real tools and data. This delivers that wiring as a ready-to-run asset.
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Market value
Custom AI-agent builds are quoted at $3,000–$15,000+ by agencies, with managed retainers on top. As a productised template it commands premium one-time or subscription pricing.