Automation library · AI assistants

Segment players and predict churn with OpenAI

Started by another system calling it, and works across in-memory vectors and n8n data table. It puts OpenAI behind a retrieval step, so answers are drawn from documents you supply rather than from the model alone. 23 steps: large, and worth reading before you trust it with anything live.

LargeCalls a model: OpenAIRetrieval

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23 stepsLarge: read it before trusting it with anything live
0Branches: places the flow splits
0Loops or waits
2Services it connects to

The flow

Starts it 2Model 19Logic 1Service 1

The steps in the order they run, from what starts it.

  1. 1Gameplay Logs WebhookWebhookStarts it
  2. 2Segmentation ModelLm Chat Open AiModel
  3. 3Segmentation Output ParserOutput Parser StructuredModel
  4. 4Player Behavior EmbeddingsEmbeddings Open AiModel
  5. 5Metrics CalculatorTool CalculatorModel
  6. 6Statistical Analysis ToolTool CodeModel
  7. 7Prediction ModelLm Chat Open AiModel
  8. 8Prediction Output ParserOutput Parser StructuredModel
  9. 9Reward Simulation ModelLm Chat Open AiModel
  10. 10Reward Simulation Output ParserOutput Parser StructuredModel
  11. 11Pricing Simulation ModelLm Chat Open AiModel
  12. 12Pricing Simulation Output ParserOutput Parser StructuredModel
  13. 13Testing Roadmap ModelLm Chat Open AiModel
  14. 14Testing Roadmap Output ParserOutput Parser StructuredModel
  15. 15Player Segmentation AgentAgentModel
  16. 16Player Behavior Vector StoreVector Store In MemoryModel
  17. 17Behavioral Prediction AgentAgent ToolModel
  18. 18Reward Redesign Simulation AgentAgent ToolModel
  19. 19Pricing Adjustment Simulation AgentAgent ToolModel
  20. 20A/B Testing Roadmap AgentAgent ToolModel
  21. 21Prepare Analytics ResultsSetLogic
  22. 22Store Analytics ResultsData TableService
  23. 23Return Analysis ResultsRespond To WebhookStarts it

How it's built

Starts on
Another system calling it
Steps
23
Size
Large: read it before trusting it with anything live
Connects to
in-memory vectors, n8n data table
Model
OpenAI
Retrieval
Answers are drawn from documents you supply, not from the model alone

Questions

What does it need to run?

An n8n account, plus your own in-memory vectors account, and a key for OpenAI. You connect your own accounts after importing it; none are included.

What starts it?

It starts on another system calling it.

Does it use AI?

Yes. It calls OpenAI. Its answers draw on documents you give it, not on the model alone. Model calls are billed by the provider, not by n8n.

How long does it take to set up?

Take your time. At 23 steps it is large: read it before trusting it with anything live.

How do I import it?

Copy the workflow, open a new workflow in n8n and paste. The steps appear on the canvas, ready for your own accounts.

Before you run it

Nothing here has been tested by us. It is somebody's working automation, read and described from its own steps. Treat the connections it wants as a list of accounts it will touch, and read the 23 steps before you point it at anything that matters.