Synthesise AI
  • Getting Started
    • 📘Introduction
    • 🤝Supporters
    • 🔎Key Features
    • 💳Products
  • Platform Infrastructure
    • 🏗️Architecture
    • 🗺️Roadmap
  • Core Modules
    • 🧠AI Agents
    • 🤖Chatbots
    • ⚙️Automations
  • $SYNAI INFORMATION
    • 🪙Tokenomics
    • 🎁Holder Benefits
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  1. Core Modules

Chatbots

Every product built on Synthesise AI can deploy its own intelligent chatbot—trained on the specific charter, audience, and monetization logic of the digital product it’s attached to. These bots act as always-on assistants for sales, onboarding, support, and conversion optimization.


1. Capabilities

  • Context-Aware Responses Chatbots are initialized with the product's UVZ, feature set, and audience pain points. They handle follow-up questions, clarifications, and custom queries.

  • Multi-Mode Support Bots can operate in:

    • FAQ mode

    • Objection handling mode

    • Content guidance mode

    • Post-purchase interaction mode

  • Dynamic Memory Short-term session memory is held in Redis, allowing continuity across multi-turn conversations. Context resets after session timeout or upon completion trigger.


2. Training & Deployment Pipeline

2.1 Data Ingestion

Each chatbot is trained on:

  • Product Charter content

  • Module breakdowns

  • FAQ JSON (auto-generated from flows)

  • Monetization triggers

2.2 Compilation Process

struct KnowledgeBase {
    product_id: String,
    items: Vec<String>,
}

impl KnowledgeBase {
    fn compile(&self) -> String {
        self.items.join(" || ")
    }
}

2.3 Embedding

<script src="https://cdn.synthai.chat/embed.js"></script>
<div id="synth-chatbot" data-product-id="abc123"></div>

3. Runtime Architecture

3.1 Chatbot Core Engine

struct ChatbotSession {
    id: String,
    memory: Vec<String>,
}

impl ChatbotSession {
    fn receive(&mut self, input: &str) -> String {
        self.memory.push(input.to_string());
        format!("Responding to: {}", input)
    }
}

3.2 Session Control

  • Memory auto-prunes after 5 turns or 15 minutes of inactivity

  • LLM call is parameterized with product ID + session stack


4. Security & Sandbox Layer

  • All LLM output is filtered via prompt validators before being returned to user

  • Chatbot memory is sandboxed per user and never shared across sessions

fn sanitize_input(input: &str) -> String {
    input.replace("prompt injection", "[blocked]")
}

5. Use Cases by Product Type

Product Type
Chatbot Use Case

Courses

Lesson unlock guidance, quiz help

SaaS Flows

Pricing negotiation, feature explainers

Templates

Personalization suggestions

Coaching

Schedule follow-ups, overcome objections

PreviousAI AgentsNextAutomations

Last updated 13 days ago

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