L2 - FinCare AI — Customer Support & Sales Intelligence Example

What this example shows

This example focuses on memory orchestration, not just retrieval.

Specifically, it demonstrates how to:

• Share the same memory cluster across multiple agents
• Combine MemoryModel based customer support with autonomous insight extraction
• Persist structured commercial signals during conversations
• Separate who talks from who observes

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Architecture overview

The system is composed of three main components, all connected to the same MemoryModel cluster:

1. Customer Support Agent

• Answers user questions using Retrieval-Augmented Generation (RAG)
• Retrieves information from product documentation and user profiles
• Stores long-term user preferences and contextual data

2. Spy Node (SalesInsight)

• Runs silently in parallel with the conversation
• Never answers the user
• Observes messages and extracts structured sales signals, such as:
  • implied needs
  • sentiment
  • potential upsell opportunities
• Writes structured data directly into memory

3. Operator Dashboard

• Reads only the structured memories generated by the Spy Node
• Displays real-time sales insights to a human operator
• Scores leads based on estimated conversion probability

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Memory nodes used

The example uses multiple memory nodes, defined explicitly by the developer:

• AppKnowledge
  Stores official documentation and product manuals ingested via batch upload.
• UserProfile
  Stores persistent user-level information such as preferences, constraints, and past interactions.
• SalesInsight
  A structured node designed to capture commercial signals inferred from conversations.

All nodes are defined in nodes.txt and created explicitly when configuring the memory cluster.

This highlights a core concept of MemoryModel:
you decide what is memory-worthy, not the system.

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Running the example

The full source code is available as an open-source project:

👉 FinCare AI — GitHub repository
https://github.com/MemoryModelRepo/CustomareCare-Upselling-finance-app-public.git

To run the example locally:

1. Create a MemoryModel cluster
2. Define the nodes using nodes.txt
3. Ingest documentation into the memory layer
4. Run the customer and operator applications in parallel

Detailed setup instructions are available in the project README.