Aplysia3: Understanding How the chatbot answers Guest Questions

When a guest sends a message, the Aplysia 3 chatbot uses a multi-step decision-making process to determine the best response. This process combines topic logic with document retrieval to ensure flexibility, accuracy, and control.

Step 1: Topic Matching

The first stage in Aplysia3’s response process is Topic Matching. The system evaluates the guest's message to determine if it corresponds to any pre-configured Topics. These Topics are created and trained by property or brand administrators to address frequent or critical guest inquiries.

  • If a matching Topic is found and set to "Replace AI-generated Answer", Aplysia3 immediately responds using the predefined content. This can include text, images, and interactive buttons. No further document search is required in this case.

  • If a matching Topic is set to "Support AI-generated Answer", Aplysia3 proceeds to retrieve supporting text from property documents. The final response blends AI-generated content with the visuals and actions defined in the Topic Response.

  • If a matching Topic exists but lacks configured content, it is ignored, and the system continues to the next step.

  • If no matching Topic is found, Aplysia3 automatically advances to document retrieval.

This stage allows for full control over certain interactions while still enabling AI flexibility when needed.


Step 2: Document Retrieval

When a message is not fully answered by a Topic, Aplysia3 activates its core AI capability: Retrieval-Augmented Generation (RAG).

  • The chatbot searches across the Property Knowledge Document and the Company Knowledge Document for up-to-date, structured information.

  • Only documents explicitly marked as “Generating live answers” are used in this process.

  • RAG enables Aplysia3 to extract relevant content in real time and synthesize it into context-aware, accurate responses that are tailored to the user’s question.

This step ensures Aplysia3 can address a wide variety of inquiries, even those not anticipated in pre-configured Topics, while staying grounded in verified, structured data.


Step 3: Fallback Behaviour

If neither a matching Topic nor relevant document content is available, Aplysia3 moves into fallback mode.

Depending on configuration, this may involve:

  • Displaying a generic fallback message

  • Transferring the conversation to a human agent

  • Offering general support options

This ensures the guest always receives a response or a path to further help, even if the system cannot resolve the issue independently.


Additional Decision-Making Features

While following the main three-step process, Aplysia3 also runs parallel checks to enhance interaction quality:

  • Triggering conversation flows such as “Book a Room” or other custom journeys.

  • Suggesting relevant Topics based on the guest’s current intent, using its Proximity Feature.

  • Escalating to a human agent when the complexity or tone of the conversation indicates that further support is required.


Why This Matters

This layered architecture allows Aplysia3 to offer:

  • High accuracy for property-specific and branded topics.

  • Dynamic, up-to-date responses via real-time document integration.

  • Flexibility and control through configurable Topic overrides.