# Table of Contents

- [Introduction | Synthesise AI](#introduction-synthesise-ai)
- [Introduction | Synthesise AI](#introduction-synthesise-ai)
- [Supporters | Synthesise AI](#supporters-synthesise-ai)
- [Key Features | Synthesise AI](#key-features-synthesise-ai)
- [Products | Synthesise AI](#products-synthesise-ai)
- [Architecture | Synthesise AI](#architecture-synthesise-ai)
- [Roadmap | Synthesise AI](#roadmap-synthesise-ai)
- [Chatbots | Synthesise AI](#chatbots-synthesise-ai)
- [AI Agents | Synthesise AI](#ai-agents-synthesise-ai)
- [Tokenomics | Synthesise AI](#tokenomics-synthesise-ai)
- [Automations | Synthesise AI](#automations-synthesise-ai)
- [Holder Benefits | Synthesise AI](#holder-benefits-synthesise-ai)
- [Unknown](#unknown)
- [Unknown](#unknown)
- [Unknown](#unknown)
- [Introduction | Synthesise AI](#introduction-synthesise-ai)
- [Unknown](#unknown)
- [Unknown](#unknown)
- [Unknown](#unknown)
- [Unknown](#unknown)
- [Unknown](#unknown)
- [Unknown](#unknown)
- [Unknown](#unknown)
- [Unknown](#unknown)
- [Unknown](#unknown)
- [Architecture | Synthesise AI](#architecture-synthesise-ai)
- [Tokenomics | Synthesise AI](#tokenomics-synthesise-ai)
- [AI Agents | Synthesise AI](#ai-agents-synthesise-ai)

---

# Introduction | Synthesise AI

For the complete documentation index, see [llms.txt](https://synthesise-ai.gitbook.io/synthesise-ai/llms.txt)
. This page is also available as [Markdown](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/introduction.md)
.

#### What is Synthesise AI?[](https://synthesise-ai.gitbook.io/#what-is-synthesise-ai)

Synthesise AI is an intelligent platform designed for creators, entrepreneurs, and digital product builders who want to rapidly launch AI-enhanced offerings—without writing a line of code. It combines a composable AI agent framework, no-code automation builder, product validation intelligence, and a fully integrated monetization layer.

At its core, Synthesise AI turns the traditionally fragmented process of ideating, validating, launching, and scaling digital products into a unified flow—one that’s fast, intelligent, and modular.

* * *

#### Why It Matters[](https://synthesise-ai.gitbook.io/#why-it-matters)

The traditional digital product journey involves:

*   Guessing what the market wants
    
*   Piecing together product flows from multiple SaaS tools
    
*   Writing content manually
    
*   Hiring teams or contractors
    

With Synthesise AI, users can instead:

*   Validate product-market fit in minutes
    
*   Generate full product frameworks (charters, deliverables, email sequences, upsell flows)
    
*   Deploy smart AI agents that evolve with their audience
    
*   Automate the customer journey end-to-end
    

This isn’t just automation. It’s AI-native product development.

* * *

#### Platform Backing & Ecosystem[](https://synthesise-ai.gitbook.io/#platform-backing-and-ecosystem)

Synthesise AI is supported by a global innovation network including:

*   **Microsoft Founders Hub** – enterprise mentorship, cloud credits
    
*   **Monetise** – embedded payment infrastructure and revenue analytics
    
*   **Educate** – strategic alignment with online course creators and coaches
    

These partnerships ensure that Synthesise AI is not only technically robust, but built for scale, distribution, and monetization.

* * *

#### Core Philosophy[](https://synthesise-ai.gitbook.io/#core-philosophy)

The core vision behind Synthesise AI is to democratize high-leverage product creation. This means:

*   Letting knowledge workers monetize faster with intelligent templates
    
*   Reducing the learning curve of AI tools to near-zero
    
*   Enabling continuous iteration via live market feedback
    
*   Turning every user into a full-stack digital business—powered by autonomous software
    

As the internet becomes more agentic, we believe product creation itself must become a dialogue—between creator, data, customer, and AI.

* * *

#### Foundational Technology Stack[](https://synthesise-ai.gitbook.io/#foundational-technology-stack)

Synthesise AI is developed using:

*   **Rust** for core service logic and agent execution (speed, safety, concurrency)
    
*   **WASM** (WebAssembly) for deploying secure, lightweight chatbots and micro-agents on the edge
    
*   **PostgreSQL + Redis** for structured data and real-time flows
    
*   **OpenAI, Anthropic, and Local LLMs** as pluggable inference providers
    
*   **Kubernetes** for horizontal scaling across product deployments
    

Here’s a sample Rust interface that powers our chatbot sandboxing layer:

Copy

    use std::collections::HashMap;
    
    pub struct AIKernel {
        model: String,
        memory: HashMap<String, String>,
    }
    
    impl AIKernel {
        pub fn new(model: &str) -> Self {
            Self {
                model: model.to_string(),
                memory: HashMap::new(),
            }
        }
    
        pub fn ask(&mut self, input: &str) -> String {
            self.memory.insert("last_input".into(), input.into());
            format!("AI ({}): processing '{}'", self.model, input)
        }
    }

* * *

#### The Journey Ahead[](https://synthesise-ai.gitbook.io/#the-journey-ahead)

As Synthesise AI evolves, users can expect:

*   Agent marketplaces with user-generated templates
    
*   Revenue share baked into product flows
    
*   Community-curated intelligence that compounds with usage
    

In a future defined by personalized AI interfaces, Synthesise AI positions itself as the backbone for self-sustaining, ever-adaptive digital products.

[NextSupporters](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/supporters)

Last updated 1 year ago

---

# Introduction | Synthesise AI

For the complete documentation index, see [llms.txt](https://synthesise-ai.gitbook.io/synthesise-ai/llms.txt)
. This page is also available as [Markdown](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/introduction.md)
.

#### What is Synthesise AI?[](https://synthesise-ai.gitbook.io/synthesise-ai#what-is-synthesise-ai)

Synthesise AI is an intelligent platform designed for creators, entrepreneurs, and digital product builders who want to rapidly launch AI-enhanced offerings—without writing a line of code. It combines a composable AI agent framework, no-code automation builder, product validation intelligence, and a fully integrated monetization layer.

At its core, Synthesise AI turns the traditionally fragmented process of ideating, validating, launching, and scaling digital products into a unified flow—one that’s fast, intelligent, and modular.

* * *

#### Why It Matters[](https://synthesise-ai.gitbook.io/synthesise-ai#why-it-matters)

The traditional digital product journey involves:

*   Guessing what the market wants
    
*   Piecing together product flows from multiple SaaS tools
    
*   Writing content manually
    
*   Hiring teams or contractors
    

With Synthesise AI, users can instead:

*   Validate product-market fit in minutes
    
*   Generate full product frameworks (charters, deliverables, email sequences, upsell flows)
    
*   Deploy smart AI agents that evolve with their audience
    
*   Automate the customer journey end-to-end
    

This isn’t just automation. It’s AI-native product development.

* * *

#### Platform Backing & Ecosystem[](https://synthesise-ai.gitbook.io/synthesise-ai#platform-backing-and-ecosystem)

Synthesise AI is supported by a global innovation network including:

*   **Microsoft Founders Hub** – enterprise mentorship, cloud credits
    
*   **Monetise** – embedded payment infrastructure and revenue analytics
    
*   **Educate** – strategic alignment with online course creators and coaches
    

These partnerships ensure that Synthesise AI is not only technically robust, but built for scale, distribution, and monetization.

* * *

#### Core Philosophy[](https://synthesise-ai.gitbook.io/synthesise-ai#core-philosophy)

The core vision behind Synthesise AI is to democratize high-leverage product creation. This means:

*   Letting knowledge workers monetize faster with intelligent templates
    
*   Reducing the learning curve of AI tools to near-zero
    
*   Enabling continuous iteration via live market feedback
    
*   Turning every user into a full-stack digital business—powered by autonomous software
    

As the internet becomes more agentic, we believe product creation itself must become a dialogue—between creator, data, customer, and AI.

* * *

#### Foundational Technology Stack[](https://synthesise-ai.gitbook.io/synthesise-ai#foundational-technology-stack)

Synthesise AI is developed using:

*   **Rust** for core service logic and agent execution (speed, safety, concurrency)
    
*   **WASM** (WebAssembly) for deploying secure, lightweight chatbots and micro-agents on the edge
    
*   **PostgreSQL + Redis** for structured data and real-time flows
    
*   **OpenAI, Anthropic, and Local LLMs** as pluggable inference providers
    
*   **Kubernetes** for horizontal scaling across product deployments
    

Here’s a sample Rust interface that powers our chatbot sandboxing layer:

Copy

    use std::collections::HashMap;
    
    pub struct AIKernel {
        model: String,
        memory: HashMap<String, String>,
    }
    
    impl AIKernel {
        pub fn new(model: &str) -> Self {
            Self {
                model: model.to_string(),
                memory: HashMap::new(),
            }
        }
    
        pub fn ask(&mut self, input: &str) -> String {
            self.memory.insert("last_input".into(), input.into());
            format!("AI ({}): processing '{}'", self.model, input)
        }
    }

* * *

#### The Journey Ahead[](https://synthesise-ai.gitbook.io/synthesise-ai#the-journey-ahead)

As Synthesise AI evolves, users can expect:

*   Agent marketplaces with user-generated templates
    
*   Revenue share baked into product flows
    
*   Community-curated intelligence that compounds with usage
    

In a future defined by personalized AI interfaces, Synthesise AI positions itself as the backbone for self-sustaining, ever-adaptive digital products.

[NextSupporters](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/supporters)

Last updated 1 year ago

---

# Supporters | Synthesise AI

For the complete documentation index, see [llms.txt](https://synthesise-ai.gitbook.io/synthesise-ai/llms.txt)
. This page is also available as [Markdown](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/supporters.md)
.

Synthesise AI is supported by leading organizations that accelerate our mission to make intelligent digital product creation accessible, scalable, and profitable.

#### Microsoft Founders Hub[](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/supporters#microsoft-founders-hub)

As part of Microsoft’s startup support network, Synthesise AI benefits from cloud credits, developer tooling, and access to cutting-edge AI infrastructure via Azure and OpenAI. This support enhances our scalability and backend performance.

#### Monetise[](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/supporters#monetise)

Monetise powers Synthesise AI’s financial layer—enabling seamless monetization workflows, creator analytics, and dynamic checkout flows. With real-time revenue infrastructure, creators can convert ideas into income with zero code.

#### Educate[](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/supporters#educate)

Educate connects Synthesise AI to a global creator economy, offering knowledge entrepreneurs a channel to rapidly validate, package, and deliver learning-based digital products. This partnership strengthens our distribution model and educational footprint.

[PreviousIntroduction](https://synthesise-ai.gitbook.io/synthesise-ai)
[NextKey Features](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/key-features)

Last updated 1 year ago

---

# Key Features | Synthesise AI

For the complete documentation index, see [llms.txt](https://synthesise-ai.gitbook.io/synthesise-ai/llms.txt)
. This page is also available as [Markdown](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/key-features.md)
.

#### 1\. Intelligent Product Frameworks[](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/key-features#id-1.-intelligent-product-frameworks)

**1.1 Unique Value Zone (UVZ) Engine**

**Purpose** Automatically identifies the “intersection of knowledge and demand” by analyzing user expertise and mapping it against audience pain points using machine learning.

**How it works**

*   Accepts a user input string describing skills, domain, or niche
    
*   Uses cosine similarity across a vectorized corpus of monetizable problems
    
*   Outputs a ranked UVZ score with tags and prompts
    

**Rust Example: UVZ Scoring Engine**

Copy

    pub fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
        let dot = a.iter().zip(b).map(|(x, y)| x * y).sum::<f32>();
        let norm_a = a.iter().map(|x| x * x).sum::<f32>().sqrt();
        let norm_b = b.iter().map(|y| y * y).sum::<f32>().sqrt();
        dot / (norm_a * norm_b)
    }

* * *

**1.2 Concept Validation Protocol**

**Purpose** Determines product-market fit by querying clusters of LLMs and using heuristics to simulate “early audience reactions.”

**Components**

*   Prompt multiplexing
    
*   GPT-ensemble confidence scoring
    
*   Competitive saturation indexing
    

**Workflow**

*   Feed idea into AI mesh
    
*   Aggregate scores, identify overlap with existing solutions
    
*   Deliver heatmap and go/no-go signal
    

* * *

**1.3 Product Charter Generator**

**Purpose** Transforms a validated idea into a blueprint with full chapters, milestones, and pricing logic.

**Submodules**

*   Deliverable Tree Engine
    
*   Persona & Journey Map
    
*   Pricing Engine (integrated with Monetise)
    

**Rust Struct**

Copy

    struct ProductCharter {
        title: String,
        modules: Vec<String>,
        pricing_model: String,
    }
    
    impl ProductCharter {
        fn render_summary(&self) -> String {
            format!("{} with {} modules, priced at {}", 
                self.title, 
                self.modules.len(), 
                self.pricing_model)
        }
    }

* * *

#### 2\. Smart Assistant Dashboard[](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/key-features#id-2.-smart-assistant-dashboard)

**2.1 Creation History + Modular Resume**

*   Timestamped logs for each flow
    
*   One-click resumption of archived builds
    
*   Clone + mutate workflows to speed up ideation
    

**Rust Struct**

Copy

    struct ProjectLog {
        name: String,
        created_at: String,
        status: String,
    }

* * *

**2.2 Visual Feedback Analytics**

*   Real-time graphs of engagement, user friction, flow completions
    
*   Uses Rust-based backend with frontend D3.js integration
    
*   Interactive anomaly detection and LLM feedback injection
    

**Metric Capture Example**

Copy

    struct Feedback {
        engagement_rate: f32,
        dropoff_point: usize,
        score: u8,
    }

* * *

#### 3\. Chatbot Engine (Autonomous AI Assistants)[](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/key-features#id-3.-chatbot-engine-autonomous-ai-assistants)

**3.1 Deployable, Context-Aware Product Bots**

*   Auto-trained on user’s product charter and value zone
    
*   Works on landing pages, checkout flows, dashboards
    
*   Supports structured (FAQ) and unstructured (follow-up) queries
    

**Rust Example**

Copy

    struct ChatBot {
        context: Vec<String>,
    }
    
    impl ChatBot {
        fn reply(&self, input: &str) -> String {
            format!("Based on context, responding to: {}", input)
        }
    }

* * *

#### 4\. Pre-trained Modular AI Agents[](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/key-features#id-4.-pre-trained-modular-ai-agents)

**4.1 Agent Categories**

*   **SEOAgent** – Metadata, semantic keywords, clustering
    
*   **LaunchCopyAgent** – CTA variants, emotional tones, urgency sliders
    
*   **InstructionalDesigner** – Course breakdowns, quizzes, and follow-ups
    

**Execution Snippet**

Copy

    trait Agent {
        fn act(&self, input: &str) -> String;
    }
    
    struct SEOAgent;
    
    impl Agent for SEOAgent {
        fn act(&self, input: &str) -> String {
            format!("Generating tags for '{}'", input)
        }
    }

* * *

#### 5\. Visual Automation Engine[](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/key-features#id-5.-visual-automation-engine)

**5.1 Event-Driven Workflow Composer**

*   Drag-and-drop builder (WASM-rendered frontend)
    
*   Events include:
    
    *   User purchase
        
    *   Abandonment
        
    *   Form submit
        
    *   Page engagement
        
    
*   Actions include:
    
    *   Email / SMS / Webhook
        
    *   Smart delay with optimization window
        
    *   Token issuance (NFT / access pass)
        
    

**Rust Enum**

Copy

    enum AutomationEvent {
        Purchased(String),
        AbandonedCart,
        CompletedQuiz,
    }

**Handler Function**

Copy

    fn automation_trigger(event: AutomationEvent) {
        match event {
            AutomationEvent::Purchased(p) => println!("Thanks for purchasing {}", p),
            AutomationEvent::AbandonedCart => println!("Reminder sent."),
            _ => println!("Unhandled event"),
        }
    }

[PreviousSupporters](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/supporters)
[NextProducts](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/products)

Last updated 1 year ago

---

# Products | Synthesise AI

For the complete documentation index, see [llms.txt](https://synthesise-ai.gitbook.io/synthesise-ai/llms.txt)
. This page is also available as [Markdown](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/products.md)
.

Synthesise AI is available in two primary subscription tiers. Each package is designed to match different levels of creative intensity, team size, and automation needs. The billing cycle is annual, with a usage-based credit system baked into runtime.

* * *

#### 1\. Starter Tier – **$1,499/year**[](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/products#id-1.-starter-tier-usd1-499-year)

**Who It's For**

Designed for solopreneurs, early-stage builders, or coaches launching their first digital products.

**What's Included**

*   **3 Creation Credits per Year** Each credit unlocks one complete build flow: from idea → UVZ → validation → automation → export
    
*   **Personalized Dashboard** Real-time usage, success score tracking, and flow history
    
*   **Product Charter Generation** One-click transformation from validated idea to modular blueprint
    
*   **Premium Support** Email-based support with 48hr SLA
    

**Enforcement Logic in Rust**

Copy

    struct Subscription {
        tier: String,
        credits_remaining: u8,
    }
    
    impl Subscription {
        fn consume_credit(&mut self) -> Result<(), &'static str> {
            if self.credits_remaining > 0 {
                self.credits_remaining -= 1;
                Ok(())
            } else {
                Err("No credits remaining.")
            }
        }
    }

* * *

#### 2\. Pro Tier – **$2,999/year**[](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/products#id-2.-pro-tier-usd2-999-year)

**Who It's For**

Ideal for scale-focused businesses, agencies, or educators building multiple monetizable flows each month.

**What's Included**

*   **Unlimited Build Credits** Create as many product flows as you like across multiple brands, niches, or formats.
    
*   **Priority Support** 24/7 access to Slack-based concierge and technical support
    
*   **Advanced Guidance Flows** Access exclusive agents and workflows (e.g. High Ticket Course Builder, Web3 Onboarding Agent)
    
*   **Multi-Agent Flow Composition** Combine AI agents into collaborative swarms that co-develop your product
    
*   **Early Feature Access** Beta-test upcoming tools, automation nodes, and LLM connectors
    

**Runtime Feature Access Logic**

Copy

    fn has_pro_features(tier: &str) -> bool {
        match tier {
            "Pro" => true,
            _ => false,
        }
    }

* * *

#### 3\. Credit System Explained[](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/products#id-3.-credit-system-explained)

Credits are the atomic unit of product development access on Synthesise AI. Each credit unlocks one **end-to-end product flow**:

Copy

    [ Ideation ] → [ Validation ] → [ Charter ] → [ Automation ] → [ Monetize ]

Even Pro users rely on credits—they’re just unmetered. This gives our runtime consistent entry/exit points for feature usage, cost estimation, and long-term metering.

* * *

#### 4\. In-Platform Purchases[](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/products#id-4.-in-platform-purchases)

While credits cover product generation, you can optionally buy:

*   Additional chatbot deployments
    
*   Premium templates
    
*   Revenue projection packs
    
*   Custom domain dashboards
    

All add-ons are powered by Stripe and tracked via a tokenized ledger internally.

[PreviousKey Features](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/key-features)
[NextArchitecture](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure/architecture)

Last updated 1 year ago

---

# Architecture | Synthesise AI

For the complete documentation index, see [llms.txt](https://synthesise-ai.gitbook.io/synthesise-ai/llms.txt)
. This page is also available as [Markdown](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure/architecture.md)
.

Synthesise AI is built on a composable, microservice-driven infrastructure written in Rust, deployed via Kubernetes, and optimized for low-latency AI orchestration. Its design prioritizes modularity, resilience, and high-throughput LLM interaction.

* * *

#### 1\. Core Flow Engine[](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure/architecture#id-1.-core-flow-engine)

**Purpose**

Responsible for coordinating execution across agents, prompts, memory layers, and UI responses. It ensures every user action in a product flow is context-aware and state-consistent.

**Internal Logic**

*   Pulls agent responses via async Rust tasks
    
*   Routes payloads between system modules (UVZ, charter, chatbots)
    
*   Writes flow data to Redis for short-term caching, Postgres for history
    

Copy

    struct FlowRequest {
        user_id: String,
        input: String,
    }
    
    fn handle_flow(request: FlowRequest) -> String {
        format!("Processing flow for {}: {}", request.user_id, request.input)
    }

* * *

#### 2\. Agent Runtime Layer[](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure/architecture#id-2.-agent-runtime-layer)

**Purpose**

Executes autonomous agent logic across multiple threads, often using pre-trained transformers (e.g., GPT-4, Claude, or local models).

**Capabilities**

*   Custom toolchains for SEO, structure generation, pricing logic
    
*   Multi-agent collaboration on a shared product state
    
*   Streaming response mode for real-time frontends
    

Copy

    trait Agent {
        fn act(&self, input: &str) -> String;
    }
    
    struct BlueprintAgent;
    
    impl Agent for BlueprintAgent {
        fn act(&self, input: &str) -> String {
            format!("Generated outline for '{}'", input)
        }
    }

* * *

#### 3\. State & Memory Layer[](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure/architecture#id-3.-state-and-memory-layer)

**Purpose**

Combines fast-access state (Redis) with persistent product memory (PostgreSQL). Every product flow generates a state tree, enabling rollback, simulation, and audit.

**Design Notes**

*   Each module (e.g., UVZ, Automation) has its own schema
    
*   Redis used for ephemeral chatbot context and rate-limiting
    
*   Database holds: charter drafts, agent logs, API results, user sessions
    

Copy

    struct ProductMemory {
        id: u64,
        key: String,
        value: String,
    }
    
    fn save_memory(mem: ProductMemory) {
        println!("Saved {}: {}", mem.key, mem.value);
    }

* * *

#### 4\. Monetise + Subscription Engine[](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure/architecture#id-4.-monetise--subscription-engine)

**Purpose**

Integrates real-time access control, usage tracking, and revenue metrics. Synthesise AI’s billing logic is tightly bound to the build credit system.

**Key Components**

*   Stripe webhook processor for payments, renewals, plan upgrades
    
*   Runtime gatekeeping that checks tier permissions and deducts credits
    

Copy

    struct User {
        tier: String,
        credits: u8,
    }
    
    fn use_credit(user: &mut User) -> Result<(), &'static str> {
        if user.credits > 0 {
            user.credits -= 1;
            Ok(())
        } else {
            Err("No credits left")
        }
    }

* * *

#### 5\. Automation + Event Router[](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure/architecture#id-5.-automation--event-router)

**Purpose**

Translates product actions (e.g. purchase, completion) into automated system behaviors using a rule-based event engine.

**Architecture**

*   Event queue processed via background workers
    
*   Rules stored as JSON, parsed and compiled at runtime
    
*   Can call email APIs, trigger NFT minting, send Stripe webhooks, or assign rewards
    

Copy

    enum Trigger {
        OnPurchase(String),
        OnDropoff,
        OnSuccess,
    }
    
    fn dispatch(trigger: Trigger) {
        match trigger {
            Trigger::OnPurchase(p) => println!("Delivering product {}", p),
            Trigger::OnDropoff => println!("Reminder sequence triggered"),
            Trigger::OnSuccess => println!("Badge minted"),
        }
    }

[PreviousProducts](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/products)
[NextRoadmap](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure/roadmap)

Last updated 1 year ago

---

# Roadmap | Synthesise AI

For the complete documentation index, see [llms.txt](https://synthesise-ai.gitbook.io/synthesise-ai/llms.txt)
. This page is also available as [Markdown](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure/roadmap.md)
.

#### ✅ Q2 2024 — Research & Foundation[](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure/roadmap#q2-2024-research-and-foundation)

*   Initial product engine prototyped in Rust
    
*   UVZ scoring model and charter generator completed
    
*   Closed alpha with 10 early users across education, SaaS, and info product markets
    
*   Visual automation system scoped and tested internally
    

#### ✅ Q3 2024 — Agent Infrastructure[](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure/roadmap#q3-2024-agent-infrastructure)

*   MVP chatbot framework deployed with WASM runtime
    
*   Redis-powered session management and automation triggers
    
*   Charter-to-agent architecture stabilized for real-world use cases
    
*   Internal payments integrated for credits and access tiers
    

#### ✅ Q4 2024 — Private Beta[](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure/roadmap#q4-2024-private-beta)

*   Launch of private beta cohort
    
*   First 100+ flows completed using the platform
    
*   Stake-based referral system introduced
    
*   Developer tools and credit logic hardened
    

#### ✅ Q1 2025 — Internal Monetization Tools[](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure/roadmap#q1-2025-internal-monetization-tools)

*   Team collaboration system designed
    
*   Metrics layer built into dashboards
    
*   Agent performance logging and credit ledger launched
    
*   Partner template support tested with hand-picked creators
    

#### ⏳ Q2 2025 — Public Launch & Token Activation[](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure/roadmap#q2-2025-public-launch-and-token-activation)

*   Synthesise AI v1 opens to the public
    
*   Starter and Pro subscriptions go live
    
*   Tokenomics for $SYNAI released
    
*   $SYNAI token deployed on pump.fun (95% liquidity / 5% dev, 1-year lock)
    
*   Revenue-sharing staking module goes live
    
*   First product flows pinned to IPFS
    
*   AI agent marketplace structure rolled out
    

#### ⏳ Q3 2025 — Ecosystem Expansion[](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure/roadmap#q3-2025-ecosystem-expansion)

*   Multi-user live flow editing
    
*   On-chain credentialing with NFT certificates
    
*   Public developer API with WASM-wrapped agents
    
*   Localization for templates in multiple languages
    

#### ⏳ Q4 2025 — SDK and Integration Rollout[](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure/roadmap#q4-2025-sdk-and-integration-rollout)

*   SDKs released for Rust and TypeScript
    
*   Web3 authentication integrations added
    
*   Cross-chain export (Base, Arbitrum, Ethereum)
    
*   Templates available with token gating
    

#### ⏳ 2026 — DAO Governance & Marketplace[](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure/roadmap#id-2026-dao-governance-and-marketplace)

*   Agent plugin marketplace opens to developers
    
*   Community roadmap voting via token quorum
    
*   Ecosystem grants and template royalties enabled
    
*   Zapier, Slack, Shopify and Notion integrations
    
*   Global staker base with ongoing USDC revenue share
    

[PreviousArchitecture](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure/architecture)
[NextAI Agents](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/ai-agents)

Last updated 1 year ago

---

# Chatbots | Synthesise AI

For the complete documentation index, see [llms.txt](https://synthesise-ai.gitbook.io/synthesise-ai/llms.txt)
. This page is also available as [Markdown](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/chatbots.md)
.

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[](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/chatbots#id-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[](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/chatbots#id-2.-training-and-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**

Copy

    struct KnowledgeBase {
        product_id: String,
        items: Vec<String>,
    }
    
    impl KnowledgeBase {
        fn compile(&self) -> String {
            self.items.join(" || ")
        }
    }

**2.3 Embedding**

Copy

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

* * *

#### 3\. Runtime Architecture[](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/chatbots#id-3.-runtime-architecture)

**3.1 Chatbot Core Engine**

Copy

    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[](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/chatbots#id-4.-security-and-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
    

Copy

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

* * *

#### 5\. Use Cases by Product Type[](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/chatbots#id-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 Agents](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/ai-agents)
[NextAutomations](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/automations)

Last updated 1 year ago

---

# AI Agents | Synthesise AI

For the complete documentation index, see [llms.txt](https://synthesise-ai.gitbook.io/synthesise-ai/llms.txt)
. This page is also available as [Markdown](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/ai-agents.md)
.

AI Agents in Synthesise are autonomous modules trained to complete specific strategic, operational, or creative functions in the product development lifecycle. While chatbots focus on reactive conversation, agents proactively generate, analyze, and optimize.

These agents operate within flows, execute independently or as part of teams, and communicate via internal protocols.

* * *

#### 1\. Agent Architecture[](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/ai-agents#id-1.-agent-architecture)

Each agent is made up of:

*   **Purpose** – the specific outcome it’s optimized for (e.g., SEO, pricing, UX)
    
*   **Model** – the underlying LLM or ruleset (GPT-4, Claude, or fine-tuned local models)
    
*   **Memory** – local session context plus shared product knowledge
    
*   **Interface** – a unified `Agent` trait implemented across all agents
    

Copy

    pub trait Agent {
        fn name(&self) -> &str;
        fn execute(&self, input: &str) -> String;
    }

* * *

#### 2\. Available Agents[](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/ai-agents#id-2.-available-agents)

Agent

Description

**TutorAgent**

Generates educational outlines, assessments, and learning flows

**SEOAgent**

Produces search-optimized titles, meta tags, alt text

**UXAuditAgent**

Evaluates module structure and friction points

**MonetizationAgent**

Recommends pricing tiers, upsell logic, and access paths

Example:

Copy

    pub struct SEOAgent;
    
    impl Agent for SEOAgent {
        fn name(&self) -> &str {
            "SEOAgent"
        }
    
        fn execute(&self, input: &str) -> String {
            format!("SEO tags for '{}': future, disruption, AI", input)
        }
    }

* * *

#### 3\. Agent Lifecycle[](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/ai-agents#id-3.-agent-lifecycle)

1.  **Invocation** Triggered by user flow (e.g. creating a new module) or other agents
    
2.  **Prompt Assembly** Combines charter + user context + input for LLM inference
    
3.  **Execution** Outputs are streamed or completed depending on use case
    
4.  **Logging & Feedback** Outputs are saved to session memory and rated for accuracy
    
5.  **Replay/Mutation** Outputs can be forked or regenerated with new parameters
    

* * *

#### 4\. Multi-Agent Collaboration[](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/ai-agents#id-4.-multi-agent-collaboration)

Agents can interact in swarm-style flows using a shared product state. For example:

Copy

    let agents: Vec<Box<dyn Agent>> = vec![\
        Box::new(SEOAgent),\
        Box::new(MonetizationAgent),\
    ];
    
    for agent in agents {
        println!("{}: {}", agent.name(), agent.execute("digital product"));
    }

This enables compound outputs: SEOAgent might propose the headline, MonetizationAgent adjusts pricing based on that.

* * *

#### 5\. Custom Agent Creation (Pro Feature)[](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/ai-agents#id-5.-custom-agent-creation-pro-feature)

Pro users will be able to define their own agents with:

*   Input/output schema templates
    
*   Prompt libraries
    
*   Optional fine-tuned model integration
    

Early versions are compiled to WASM and run inside secure sandboxes.

[PreviousRoadmap](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure/roadmap)
[NextChatbots](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/chatbots)

Last updated 1 year ago

---

# Tokenomics | Synthesise AI

For the complete documentation index, see [llms.txt](https://synthesise-ai.gitbook.io/synthesise-ai/llms.txt)
. This page is also available as [Markdown](https://synthesise-ai.gitbook.io/synthesise-ai/usdsynai-information/tokenomics.md)
.

Synthesise AI is launching its native utility token, **$SYNAI**, to fuel platform interactions, governance, and economic alignment between builders, holders, and agents. The token is designed to extend beyond mere speculation—becoming an integral component of access control, feature unlocks, credit purchasing, and revenue distribution.

* * *

#### 1\. Launch Details[](https://synthesise-ai.gitbook.io/synthesise-ai/usdsynai-information/tokenomics#id-1.-launch-details)

*   **Token Name:** Synthesise AI Token
    
*   **Ticker:** $SYNAI
    
*   **Launch Platform:** pump.fun (Solana)
    
*   **Token Standard:** SPL (Solana Program Library)
    

**Supply Breakdown**

Allocation

Percentage

Notes

Liquidity Pool

95%

Immediately tradable

Developer Reserve

5%

Locked via time-based vesting (12m)

Copy

    struct Tokenomics {
        total_supply: u64,
        liquidity: u64,
        dev_reserve: u64,
    }
    
    fn generate_tokenomics() -> Tokenomics {
        Tokenomics {
            total_supply: 1_000_000_000,
            liquidity: 950_000_000,
            dev_reserve: 50_000_000,
        }
    }

* * *

#### 2\. Use Cases[](https://synthesise-ai.gitbook.io/synthesise-ai/usdsynai-information/tokenomics#id-2.-use-cases)

**2.1 Credit Purchase**

$SYNAI can be used to buy creation credits inside the platform at discounted rates.

**2.2 Access Unlock**

Exclusive flows, agents, and templates can be gated behind token-based thresholds.

**2.3 Staking & Revenue Share**

Stakers of $SYNAI receive a monthly share of Synthesise’s subscription revenue pool (paid in USDC).

**2.4 Voting & Governance**

Token holders vote on:

*   Product roadmap features
    
*   Token utility extensions
    
*   Partnership integrations
    
*   Grant distributions
    

* * *

#### 3\. Emission Policy[](https://synthesise-ai.gitbook.io/synthesise-ai/usdsynai-information/tokenomics#id-3.-emission-policy)

There is **no ongoing inflation**. The entire supply is minted at launch. All staking rewards and incentives are paid from real platform revenue, not token dilution.

* * *

#### 4\. Smart Contract Governance[](https://synthesise-ai.gitbook.io/synthesise-ai/usdsynai-information/tokenomics#id-4.-smart-contract-governance)

Governance rights are executed via a multi-signature DAO contract with quorum checks and on-chain vote execution:

Copy

    struct Proposal {
        id: u32,
        description: String,
        yes_votes: u64,
        no_votes: u64,
    }
    
    fn pass(proposal: &Proposal) -> bool {
        proposal.yes_votes > proposal.no_votes && proposal.yes_votes > 1_000_000
    }

* * *

#### 5\. Liquidity Considerations[](https://synthesise-ai.gitbook.io/synthesise-ai/usdsynai-information/tokenomics#id-5.-liquidity-considerations)

*   Liquidity is initially seeded via pump.fun
    
*   After bootstrapping, Synthesise deploys LPs on Raydium & Jupiter
    
*   Treasury incentives (grants, airdrops) are transparently tracked on-chain
    

[PreviousAutomations](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/automations)
[NextHolder Benefits](https://synthesise-ai.gitbook.io/synthesise-ai/usdsynai-information/holder-benefits)

Last updated 1 year ago

---

# Automations | Synthesise AI

For the complete documentation index, see [llms.txt](https://synthesise-ai.gitbook.io/synthesise-ai/llms.txt)
. This page is also available as [Markdown](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/automations.md)
.

Synthesise AI features a powerful visual automation engine that allows users to chain together intelligent triggers and actions inside their product workflows. Automations are the bridge between user behavior and system response, enabling seamless personalization, monetization, and engagement—without writing code.

* * *

#### 1\. Automation Architecture[](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/automations#id-1.-automation-architecture)

**1.1 Core Design**

Automations are compiled into a lightweight rule engine that listens for defined events (e.g., purchase, quiz completion, drop-off) and responds with one or more predefined actions (e.g., send email, issue NFT, update state).

Each automation includes:

*   **Trigger** – the system event to listen for
    
*   **Condition** – optional logic (time delay, user segment)
    
*   **Action** – system behavior (send email, log event, mint token)
    

**1.2 Rust Struct Representation**

Copy

    enum Trigger {
        Purchase(String),
        DropOff,
        Completion,
    }
    
    enum Action {
        Email(String),
        Webhook(String),
        IssueToken(String),
    }
    
    struct Automation {
        trigger: Trigger,
        action: Action,
    }

* * *

#### 2\. Visual Builder[](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/automations#id-2.-visual-builder)

Users can build automations using a no-code canvas interface. Nodes represent events, conditions, and actions. Paths can be dragged to connect logic visually.

Common visual flows:

*   Purchase → Delay (24h) → Email: “Welcome Module”
    
*   Abandon Cart → Email: “Reminder” → Delay (6h) → SMS: “Still Interested?”
    
*   Module Complete → Issue Token → Unlock Bonus
    

The visual flow is compiled behind the scenes into a Rust automation object.

* * *

#### 3\. Event System[](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/automations#id-3.-event-system)

Synthesise’s automation engine is event-driven. It listens to an internal event bus across the application (e.g., purchases, interactions, completions) and matches these against active automation rules.

Example event consumption:

Copy

    fn dispatch(event: Trigger) -> Option<Action> {
        match event {
            Trigger::Purchase(product) => Some(Action::Email(format!("Thanks for purchasing {}", product))),
            Trigger::DropOff => Some(Action::Email("Reminder to complete your course".to_string())),
            _ => None,
        }
    }

* * *

#### 4\. Supported Actions[](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/automations#id-4.-supported-actions)

Action Type

Description

`Email()`

Sends templated email through integrated mail provider

`Webhook()`

Calls external system with product metadata

`IssueToken()`

Mints tokenized rewards or NFTs

`Delay()`

Defers next step by minutes/hours/days

`Split()`

Randomizes or segments flow paths

`Log()`

Saves output to analytics or CRM system

* * *

#### 5\. Advanced Use Cases[](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/automations#id-5.-advanced-use-cases)

**5.1 Dynamic Segmentation**

Users can build dynamic audience segments (e.g. “clicked but didn’t buy,” “watched 75%”) and create tailored follow-up flows per segment.

**5.2 Recurring Sequences**

Automations can be looped for recurring billing reminders, educational check-ins, or progressive onboarding modules.

**5.3 NFT Certificate Distribution**

Issue personalized, verifiable certificates for course completion:

Copy

    fn issue_certificate(user_id: &str) -> Action {
        Action::IssueToken(format!("certificate_for_{}", user_id))
    }

* * *

#### 6\. Deployment & Execution[](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/automations#id-6.-deployment-and-execution)

Automations are stored as JSON payloads in the database, compiled at runtime into action trees, and executed by a multi-threaded scheduler in Rust.

Copy

    fn run_automation(auto: Automation) {
        match auto.action {
            Action::Email(body) => println!("Sending email: {}", body),
            Action::IssueToken(token) => println!("Minting token: {}", token),
            _ => println!("Unhandled action"),
        }
    }

[PreviousChatbots](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/chatbots)
[NextTokenomics](https://synthesise-ai.gitbook.io/synthesise-ai/usdsynai-information/tokenomics)

Last updated 1 year ago

---

# Holder Benefits | Synthesise AI

For the complete documentation index, see [llms.txt](https://synthesise-ai.gitbook.io/synthesise-ai/llms.txt)
. This page is also available as [Markdown](https://synthesise-ai.gitbook.io/synthesise-ai/usdsynai-information/holder-benefits.md)
.

Holding $SYNAI isn’t just speculative—it’s a gateway to participating in the future of intelligent product creation. Synthesise AI rewards contributors, builders, and believers through revenue sharing, access benefits, and on-chain governance authority.

* * *

#### 1\. Revenue Share[](https://synthesise-ai.gitbook.io/synthesise-ai/usdsynai-information/holder-benefits#id-1.-revenue-share)

**Mechanism**

Token holders who stake $SYNAI in the Synthesise protocol contract receive a percentage of platform subscription revenue (paid in USDC) each month.

**Distribution Logic**

*   Rewards pool is funded by 10% of gross platform revenue
    
*   Distributed proportionally to staked $SYNAI
    
*   Snapshot-based system with a 7-day lock period
    

Copy

    struct Staker {
        wallet: String,
        amount: u64,
    }
    
    fn calculate_rewards(stakers: Vec<Staker>, total_rewards: u64) -> Vec<(String, u64)> {
        let total_staked: u64 = stakers.iter().map(|s| s.amount).sum();
        stakers.into_iter()
            .map(|s| (s.wallet, total_rewards * s.amount / total_staked))
            .collect()
    }

* * *

#### 2\. Early Access[](https://synthesise-ai.gitbook.io/synthesise-ai/usdsynai-information/holder-benefits#id-2.-early-access)

**Benefits**

*   First access to beta modules, experimental agent types, and automation templates
    
*   Closed-loop feedback program: stakers influence LLM tuning and template logic
    

**Examples**

*   Pre-release testing of Multi-Agent Negotiation Swarm
    
*   Early mint of Synthesise x Partner NFTs (token-gated access)
    

* * *

#### 3\. Platform Governance[](https://synthesise-ai.gitbook.io/synthesise-ai/usdsynai-information/holder-benefits#id-3.-platform-governance)

**Rights**

$SYNAI holders can vote on:

*   New feature priority
    
*   Integration roadmap
    
*   Token utility expansions
    
*   Treasury grant approvals
    

**Voting Logic**

One token = one vote. Votes are cast on-chain and executed via DAO smart contracts.

* * *

#### 4\. Builder Perks[](https://synthesise-ai.gitbook.io/synthesise-ai/usdsynai-information/holder-benefits#id-4.-builder-perks)

Holding Threshold

Reward Type

1,000 $SYNAI

Custom dashboard theme

10,000 $SYNAI

Free agent credit monthly

100,000 $SYNAI

Featured slot on marketplace homepage

250,000+ $SYNAI

Private access to team + L2 launch pad

* * *

#### 5\. Affiliate Boost[](https://synthesise-ai.gitbook.io/synthesise-ai/usdsynai-information/holder-benefits#id-5.-affiliate-boost)

Stakers unlock higher referral commissions via a tiered affiliate program. Commission % scales with staked amount, starting at 20% and reaching 40% at max tier.

[PreviousTokenomics](https://synthesise-ai.gitbook.io/synthesise-ai/usdsynai-information/tokenomics)

Last updated 1 year ago

---

# Unknown

\# Synthesise AI

## Synthesise AI

- \[Introduction\](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/introduction.md)
- \[Supporters\](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/supporters.md)
- \[Key Features\](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/key-features.md)
- \[Products\](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/products.md)
- \[Architecture\](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure/architecture.md)
- \[Roadmap\](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure/roadmap.md)
- \[AI Agents\](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/ai-agents.md)
- \[Chatbots\](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/chatbots.md)
- \[Automations\](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/automations.md)
- \[Tokenomics\](https://synthesise-ai.gitbook.io/synthesise-ai/usdsynai-information/tokenomics.md)
- \[Holder Benefits\](https://synthesise-ai.gitbook.io/synthesise-ai/usdsynai-information/holder-benefits.md)

---

# Unknown

\> For the complete documentation index, see \[llms.txt\](https://synthesise-ai.gitbook.io/synthesise-ai/llms.txt). Markdown versions of documentation pages are available by appending \`.md\` to page URLs; this page is available as \[Markdown\](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/introduction.md).

# Introduction

#### What is Synthesise AI?

Synthesise AI is an intelligent platform designed for creators, entrepreneurs, and digital product builders who want to rapidly launch AI-enhanced offerings—without writing a line of code. It combines a composable AI agent framework, no-code automation builder, product validation intelligence, and a fully integrated monetization layer.

At its core, Synthesise AI turns the traditionally fragmented process of ideating, validating, launching, and scaling digital products into a unified flow—one that’s fast, intelligent, and modular.

\*\*\*

#### Why It Matters

The traditional digital product journey involves:

\* Guessing what the market wants
\* Piecing together product flows from multiple SaaS tools
\* Writing content manually
\* Hiring teams or contractors

With Synthesise AI, users can instead:

\* Validate product-market fit in minutes
\* Generate full product frameworks (charters, deliverables, email sequences, upsell flows)
\* Deploy smart AI agents that evolve with their audience
\* Automate the customer journey end-to-end

This isn’t just automation. It’s AI-native product development.

\*\*\*

#### Platform Backing & Ecosystem

Synthesise AI is supported by a global innovation network including:

\* \*\*Microsoft Founders Hub\*\* – enterprise mentorship, cloud credits
\* \*\*Monetise\*\* – embedded payment infrastructure and revenue analytics
\* \*\*Educate\*\* – strategic alignment with online course creators and coaches

These partnerships ensure that Synthesise AI is not only technically robust, but built for scale, distribution, and monetization.

\*\*\*

#### Core Philosophy

The core vision behind Synthesise AI is to democratize high-leverage product creation. This means:

\* Letting knowledge workers monetize faster with intelligent templates
\* Reducing the learning curve of AI tools to near-zero
\* Enabling continuous iteration via live market feedback
\* Turning every user into a full-stack digital business—powered by autonomous software

As the internet becomes more agentic, we believe product creation itself must become a dialogue—between creator, data, customer, and AI.

\*\*\*

#### Foundational Technology Stack

Synthesise AI is developed using:

\* \*\*Rust\*\* for core service logic and agent execution (speed, safety, concurrency)
\* \*\*WASM\*\* (WebAssembly) for deploying secure, lightweight chatbots and micro-agents on the edge
\* \*\*PostgreSQL + Redis\*\* for structured data and real-time flows
\* \*\*OpenAI, Anthropic, and Local LLMs\*\* as pluggable inference providers
\* \*\*Kubernetes\*\* for horizontal scaling across product deployments

Here’s a sample Rust interface that powers our chatbot sandboxing layer:

\`\`\`rust
use std::collections::HashMap;

pub struct AIKernel {
    model: String,
    memory: HashMap<String, String>,
}

impl AIKernel {
    pub fn new(model: &str) -> Self {
        Self {
            model: model.to\_string(),
            memory: HashMap::new(),
        }
    }

    pub fn ask(&mut self, input: &str) -> String {
        self.memory.insert("last\_input".into(), input.into());
        format!("AI ({}): processing '{}'", self.model, input)
    }
}
\`\`\`

\*\*\*

#### The Journey Ahead

As Synthesise AI evolves, users can expect:

\* Agent marketplaces with user-generated templates
\* Revenue share baked into product flows
\* Community-curated intelligence that compounds with usage

In a future defined by personalized AI interfaces, Synthesise AI positions itself as the backbone for self-sustaining, ever-adaptive digital products.

---

# Unknown

\> For the complete documentation index, see \[llms.txt\](https://synthesise-ai.gitbook.io/synthesise-ai/llms.txt). Markdown versions of documentation pages are available by appending \`.md\` to page URLs; this page is available as \[Markdown\](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/supporters.md).

# Supporters

Synthesise AI is supported by leading organizations that accelerate our mission to make intelligent digital product creation accessible, scalable, and profitable.

#### Microsoft Founders Hub

As part of Microsoft’s startup support network, Synthesise AI benefits from cloud credits, developer tooling, and access to cutting-edge AI infrastructure via Azure and OpenAI. This support enhances our scalability and backend performance.

#### Monetise

Monetise powers Synthesise AI’s financial layer—enabling seamless monetization workflows, creator analytics, and dynamic checkout flows. With real-time revenue infrastructure, creators can convert ideas into income with zero code.

#### Educate

Educate connects Synthesise AI to a global creator economy, offering knowledge entrepreneurs a channel to rapidly validate, package, and deliver learning-based digital products. This partnership strengthens our distribution model and educational footprint.

---

# Introduction | Synthesise AI

For the complete documentation index, see [llms.txt](https://synthesise-ai.gitbook.io/synthesise-ai/llms.txt)
. This page is also available as [Markdown](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/introduction.md)
.

#### What is Synthesise AI?[](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started#what-is-synthesise-ai)

Synthesise AI is an intelligent platform designed for creators, entrepreneurs, and digital product builders who want to rapidly launch AI-enhanced offerings—without writing a line of code. It combines a composable AI agent framework, no-code automation builder, product validation intelligence, and a fully integrated monetization layer.

At its core, Synthesise AI turns the traditionally fragmented process of ideating, validating, launching, and scaling digital products into a unified flow—one that’s fast, intelligent, and modular.

* * *

#### Why It Matters[](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started#why-it-matters)

The traditional digital product journey involves:

*   Guessing what the market wants
    
*   Piecing together product flows from multiple SaaS tools
    
*   Writing content manually
    
*   Hiring teams or contractors
    

With Synthesise AI, users can instead:

*   Validate product-market fit in minutes
    
*   Generate full product frameworks (charters, deliverables, email sequences, upsell flows)
    
*   Deploy smart AI agents that evolve with their audience
    
*   Automate the customer journey end-to-end
    

This isn’t just automation. It’s AI-native product development.

* * *

#### Platform Backing & Ecosystem[](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started#platform-backing-and-ecosystem)

Synthesise AI is supported by a global innovation network including:

*   **Microsoft Founders Hub** – enterprise mentorship, cloud credits
    
*   **Monetise** – embedded payment infrastructure and revenue analytics
    
*   **Educate** – strategic alignment with online course creators and coaches
    

These partnerships ensure that Synthesise AI is not only technically robust, but built for scale, distribution, and monetization.

* * *

#### Core Philosophy[](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started#core-philosophy)

The core vision behind Synthesise AI is to democratize high-leverage product creation. This means:

*   Letting knowledge workers monetize faster with intelligent templates
    
*   Reducing the learning curve of AI tools to near-zero
    
*   Enabling continuous iteration via live market feedback
    
*   Turning every user into a full-stack digital business—powered by autonomous software
    

As the internet becomes more agentic, we believe product creation itself must become a dialogue—between creator, data, customer, and AI.

* * *

#### Foundational Technology Stack[](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started#foundational-technology-stack)

Synthesise AI is developed using:

*   **Rust** for core service logic and agent execution (speed, safety, concurrency)
    
*   **WASM** (WebAssembly) for deploying secure, lightweight chatbots and micro-agents on the edge
    
*   **PostgreSQL + Redis** for structured data and real-time flows
    
*   **OpenAI, Anthropic, and Local LLMs** as pluggable inference providers
    
*   **Kubernetes** for horizontal scaling across product deployments
    

Here’s a sample Rust interface that powers our chatbot sandboxing layer:

Copy

    use std::collections::HashMap;
    
    pub struct AIKernel {
        model: String,
        memory: HashMap<String, String>,
    }
    
    impl AIKernel {
        pub fn new(model: &str) -> Self {
            Self {
                model: model.to_string(),
                memory: HashMap::new(),
            }
        }
    
        pub fn ask(&mut self, input: &str) -> String {
            self.memory.insert("last_input".into(), input.into());
            format!("AI ({}): processing '{}'", self.model, input)
        }
    }

* * *

#### The Journey Ahead[](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started#the-journey-ahead)

As Synthesise AI evolves, users can expect:

*   Agent marketplaces with user-generated templates
    
*   Revenue share baked into product flows
    
*   Community-curated intelligence that compounds with usage
    

In a future defined by personalized AI interfaces, Synthesise AI positions itself as the backbone for self-sustaining, ever-adaptive digital products.

[NextSupporters](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/supporters)

Last updated 1 year ago

---

# Unknown

\> For the complete documentation index, see \[llms.txt\](https://synthesise-ai.gitbook.io/synthesise-ai/llms.txt). Markdown versions of documentation pages are available by appending \`.md\` to page URLs; this page is available as \[Markdown\](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/key-features.md).

# Key Features

#### 1. Intelligent Product Frameworks

\*\*1.1 Unique Value Zone (UVZ) Engine\*\*

\*\*Purpose\*\*\\
Automatically identifies the “intersection of knowledge and demand” by analyzing user expertise and mapping it against audience pain points using machine learning.

\*\*How it works\*\*

\* Accepts a user input string describing skills, domain, or niche
\* Uses cosine similarity across a vectorized corpus of monetizable problems
\* Outputs a ranked UVZ score with tags and prompts

\*\*Rust Example: UVZ Scoring Engine\*\*

\`\`\`rust
pub fn cosine\_similarity(a: &\[f32\], b: &\[f32\]) -> f32 {
    let dot = a.iter().zip(b).map(|(x, y)| x \* y).sum::<f32>();
    let norm\_a = a.iter().map(|x| x \* x).sum::<f32>().sqrt();
    let norm\_b = b.iter().map(|y| y \* y).sum::<f32>().sqrt();
    dot / (norm\_a \* norm\_b)
}
\`\`\`

\*\*\*

\*\*1.2 Concept Validation Protocol\*\*

\*\*Purpose\*\*\\
Determines product-market fit by querying clusters of LLMs and using heuristics to simulate “early audience reactions.”

\*\*Components\*\*

\* Prompt multiplexing
\* GPT-ensemble confidence scoring
\* Competitive saturation indexing

\*\*Workflow\*\*

\* Feed idea into AI mesh
\* Aggregate scores, identify overlap with existing solutions
\* Deliver heatmap and go/no-go signal

\*\*\*

\*\*1.3 Product Charter Generator\*\*

\*\*Purpose\*\*\\
Transforms a validated idea into a blueprint with full chapters, milestones, and pricing logic.

\*\*Submodules\*\*

\* Deliverable Tree Engine
\* Persona & Journey Map
\* Pricing Engine (integrated with Monetise)

\*\*Rust Struct\*\*

\`\`\`rust
struct ProductCharter {
    title: String,
    modules: Vec<String>,
    pricing\_model: String,
}

impl ProductCharter {
    fn render\_summary(&self) -> String {
        format!("{} with {} modules, priced at {}", 
            self.title, 
            self.modules.len(), 
            self.pricing\_model)
    }
}
\`\`\`

\*\*\*

#### 2. Smart Assistant Dashboard

\*\*2.1 Creation History + Modular Resume\*\*

\* Timestamped logs for each flow
\* One-click resumption of archived builds
\* Clone + mutate workflows to speed up ideation

\*\*Rust Struct\*\*

\`\`\`rust
struct ProjectLog {
    name: String,
    created\_at: String,
    status: String,
}
\`\`\`

\*\*\*

\*\*2.2 Visual Feedback Analytics\*\*

\* Real-time graphs of engagement, user friction, flow completions
\* Uses Rust-based backend with frontend D3.js integration
\* Interactive anomaly detection and LLM feedback injection

\*\*Metric Capture Example\*\*

\`\`\`rust
struct Feedback {
    engagement\_rate: f32,
    dropoff\_point: usize,
    score: u8,
}
\`\`\`

\*\*\*

#### 3. Chatbot Engine (Autonomous AI Assistants)

\*\*3.1 Deployable, Context-Aware Product Bots\*\*

\* Auto-trained on user’s product charter and value zone
\* Works on landing pages, checkout flows, dashboards
\* Supports structured (FAQ) and unstructured (follow-up) queries

\*\*Rust Example\*\*

\`\`\`rust
struct ChatBot {
    context: Vec<String>,
}

impl ChatBot {
    fn reply(&self, input: &str) -> String {
        format!("Based on context, responding to: {}", input)
    }
}
\`\`\`

\*\*\*

#### 4. Pre-trained Modular AI Agents

\*\*4.1 Agent Categories\*\*

\* \*\*SEOAgent\*\* – Metadata, semantic keywords, clustering
\* \*\*LaunchCopyAgent\*\* – CTA variants, emotional tones, urgency sliders
\* \*\*InstructionalDesigner\*\* – Course breakdowns, quizzes, and follow-ups

\*\*Execution Snippet\*\*

\`\`\`rust
trait Agent {
    fn act(&self, input: &str) -> String;
}

struct SEOAgent;

impl Agent for SEOAgent {
    fn act(&self, input: &str) -> String {
        format!("Generating tags for '{}'", input)
    }
}
\`\`\`

\*\*\*

#### 5. Visual Automation Engine

\*\*5.1 Event-Driven Workflow Composer\*\*

\* Drag-and-drop builder (WASM-rendered frontend)
\* Events include:
  \* User purchase
  \* Abandonment
  \* Form submit
  \* Page engagement
\* Actions include:
  \* Email / SMS / Webhook
  \* Smart delay with optimization window
  \* Token issuance (NFT / access pass)

\*\*Rust Enum\*\*

\`\`\`rust
enum AutomationEvent {
    Purchased(String),
    AbandonedCart,
    CompletedQuiz,
}
\`\`\`

\*\*Handler Function\*\*

\`\`\`rust
fn automation\_trigger(event: AutomationEvent) {
    match event {
        AutomationEvent::Purchased(p) => println!("Thanks for purchasing {}", p),
        AutomationEvent::AbandonedCart => println!("Reminder sent."),
        \_ => println!("Unhandled event"),
    }
}
\`\`\`

---

# Unknown

\> For the complete documentation index, see \[llms.txt\](https://synthesise-ai.gitbook.io/synthesise-ai/llms.txt). Markdown versions of documentation pages are available by appending \`.md\` to page URLs; this page is available as \[Markdown\](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/products.md).

# Products

Synthesise AI is available in two primary subscription tiers. Each package is designed to match different levels of creative intensity, team size, and automation needs. The billing cycle is annual, with a usage-based credit system baked into runtime.

\*\*\*

#### 1. Starter Tier – \*\*$1,499/year\*\*

\*\*Who It's For\*\*

Designed for solopreneurs, early-stage builders, or coaches launching their first digital products.

\*\*What's Included\*\*

\* \*\*3 Creation Credits per Year\*\*\\
  Each credit unlocks one complete build flow: from idea → UVZ → validation → automation → export
\* \*\*Personalized Dashboard\*\*\\
  Real-time usage, success score tracking, and flow history
\* \*\*Product Charter Generation\*\*\\
  One-click transformation from validated idea to modular blueprint
\* \*\*Premium Support\*\*\\
  Email-based support with 48hr SLA

\*\*Enforcement Logic in Rust\*\*

\`\`\`rust
struct Subscription {
    tier: String,
    credits\_remaining: u8,
}

impl Subscription {
    fn consume\_credit(&mut self) -> Result<(), &'static str> {
        if self.credits\_remaining > 0 {
            self.credits\_remaining -= 1;
            Ok(())
        } else {
            Err("No credits remaining.")
        }
    }
}
\`\`\`

\*\*\*

#### 2. Pro Tier – \*\*$2,999/year\*\*

\*\*Who It's For\*\*

Ideal for scale-focused businesses, agencies, or educators building multiple monetizable flows each month.

\*\*What's Included\*\*

\* \*\*Unlimited Build Credits\*\*\\
  Create as many product flows as you like across multiple brands, niches, or formats.
\* \*\*Priority Support\*\*\\
  24/7 access to Slack-based concierge and technical support
\* \*\*Advanced Guidance Flows\*\*\\
  Access exclusive agents and workflows (e.g. High Ticket Course Builder, Web3 Onboarding Agent)
\* \*\*Multi-Agent Flow Composition\*\*\\
  Combine AI agents into collaborative swarms that co-develop your product
\* \*\*Early Feature Access\*\*\\
  Beta-test upcoming tools, automation nodes, and LLM connectors

\*\*Runtime Feature Access Logic\*\*

\`\`\`rust
fn has\_pro\_features(tier: &str) -> bool {
    match tier {
        "Pro" => true,
        \_ => false,
    }
}
\`\`\`

\*\*\*

#### 3. Credit System Explained

Credits are the atomic unit of product development access on Synthesise AI.\\
Each credit unlocks one \*\*end-to-end product flow\*\*:

\`\`\`plaintext
\[ Ideation \] → \[ Validation \] → \[ Charter \] → \[ Automation \] → \[ Monetize \]
\`\`\`

Even Pro users rely on credits—they’re just unmetered. This gives our runtime consistent entry/exit points for feature usage, cost estimation, and long-term metering.

\*\*\*

#### 4. In-Platform Purchases

While credits cover product generation, you can optionally buy:

\* Additional chatbot deployments
\* Premium templates
\* Revenue projection packs
\* Custom domain dashboards

All add-ons are powered by Stripe and tracked via a tokenized ledger internally.

---

# Unknown

\> For the complete documentation index, see \[llms.txt\](https://synthesise-ai.gitbook.io/synthesise-ai/llms.txt). Markdown versions of documentation pages are available by appending \`.md\` to page URLs; this page is available as \[Markdown\](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure/architecture.md).

# Architecture

Synthesise AI is built on a composable, microservice-driven infrastructure written in Rust, deployed via Kubernetes, and optimized for low-latency AI orchestration. Its design prioritizes modularity, resilience, and high-throughput LLM interaction.

\*\*\*

#### 1. Core Flow Engine

\*\*Purpose\*\*

Responsible for coordinating execution across agents, prompts, memory layers, and UI responses. It ensures every user action in a product flow is context-aware and state-consistent.

\*\*Internal Logic\*\*

\* Pulls agent responses via async Rust tasks
\* Routes payloads between system modules (UVZ, charter, chatbots)
\* Writes flow data to Redis for short-term caching, Postgres for history

\`\`\`rust
struct FlowRequest {
    user\_id: String,
    input: String,
}

fn handle\_flow(request: FlowRequest) -> String {
    format!("Processing flow for {}: {}", request.user\_id, request.input)
}
\`\`\`

\*\*\*

#### 2. Agent Runtime Layer

\*\*Purpose\*\*

Executes autonomous agent logic across multiple threads, often using pre-trained transformers (e.g., GPT-4, Claude, or local models).

\*\*Capabilities\*\*

\* Custom toolchains for SEO, structure generation, pricing logic
\* Multi-agent collaboration on a shared product state
\* Streaming response mode for real-time frontends

\`\`\`rust
trait Agent {
    fn act(&self, input: &str) -> String;
}

struct BlueprintAgent;

impl Agent for BlueprintAgent {
    fn act(&self, input: &str) -> String {
        format!("Generated outline for '{}'", input)
    }
}
\`\`\`

\*\*\*

#### 3. State & Memory Layer

\*\*Purpose\*\*

Combines fast-access state (Redis) with persistent product memory (PostgreSQL). Every product flow generates a state tree, enabling rollback, simulation, and audit.

\*\*Design Notes\*\*

\* Each module (e.g., UVZ, Automation) has its own schema
\* Redis used for ephemeral chatbot context and rate-limiting
\* Database holds: charter drafts, agent logs, API results, user sessions

\`\`\`rust
struct ProductMemory {
    id: u64,
    key: String,
    value: String,
}

fn save\_memory(mem: ProductMemory) {
    println!("Saved {}: {}", mem.key, mem.value);
}
\`\`\`

\*\*\*

#### 4. Monetise + Subscription Engine

\*\*Purpose\*\*

Integrates real-time access control, usage tracking, and revenue metrics. Synthesise AI’s billing logic is tightly bound to the build credit system.

\*\*Key Components\*\*

\* Stripe webhook processor for payments, renewals, plan upgrades
\* Runtime gatekeeping that checks tier permissions and deducts credits

\`\`\`rust
struct User {
    tier: String,
    credits: u8,
}

fn use\_credit(user: &mut User) -> Result<(), &'static str> {
    if user.credits > 0 {
        user.credits -= 1;
        Ok(())
    } else {
        Err("No credits left")
    }
}
\`\`\`

\*\*\*

#### 5. Automation + Event Router

\*\*Purpose\*\*

Translates product actions (e.g. purchase, completion) into automated system behaviors using a rule-based event engine.

\*\*Architecture\*\*

\* Event queue processed via background workers
\* Rules stored as JSON, parsed and compiled at runtime
\* Can call email APIs, trigger NFT minting, send Stripe webhooks, or assign rewards

\`\`\`rust
enum Trigger {
    OnPurchase(String),
    OnDropoff,
    OnSuccess,
}

fn dispatch(trigger: Trigger) {
    match trigger {
        Trigger::OnPurchase(p) => println!("Delivering product {}", p),
        Trigger::OnDropoff => println!("Reminder sequence triggered"),
        Trigger::OnSuccess => println!("Badge minted"),
    }
}
\`\`\`

---

# Unknown

\> For the complete documentation index, see \[llms.txt\](https://synthesise-ai.gitbook.io/synthesise-ai/llms.txt). Markdown versions of documentation pages are available by appending \`.md\` to page URLs; this page is available as \[Markdown\](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure/roadmap.md).

# Roadmap

#### ✅ Q2 2024 — Research & Foundation

\* Initial product engine prototyped in Rust
\* UVZ scoring model and charter generator completed
\* Closed alpha with 10 early users across education, SaaS, and info product markets
\* Visual automation system scoped and tested internally

#### ✅ Q3 2024 — Agent Infrastructure

\* MVP chatbot framework deployed with WASM runtime
\* Redis-powered session management and automation triggers
\* Charter-to-agent architecture stabilized for real-world use cases
\* Internal payments integrated for credits and access tiers

#### ✅ Q4 2024 — Private Beta

\* Launch of private beta cohort
\* First 100+ flows completed using the platform
\* Stake-based referral system introduced
\* Developer tools and credit logic hardened

#### ✅ Q1 2025 — Internal Monetization Tools

\* Team collaboration system designed
\* Metrics layer built into dashboards
\* Agent performance logging and credit ledger launched
\* Partner template support tested with hand-picked creators

#### ⏳ Q2 2025 — Public Launch & Token Activation

\* Synthesise AI v1 opens to the public
\* Starter and Pro subscriptions go live
\* Tokenomics for $SYNAI released
\* $SYNAI token deployed on pump.fun (95% liquidity / 5% dev, 1-year lock)
\* Revenue-sharing staking module goes live
\* First product flows pinned to IPFS
\* AI agent marketplace structure rolled out

#### ⏳ Q3 2025 — Ecosystem Expansion

\* Multi-user live flow editing
\* On-chain credentialing with NFT certificates
\* Public developer API with WASM-wrapped agents
\* Localization for templates in multiple languages

#### ⏳ Q4 2025 — SDK and Integration Rollout

\* SDKs released for Rust and TypeScript
\* Web3 authentication integrations added
\* Cross-chain export (Base, Arbitrum, Ethereum)
\* Templates available with token gating

#### ⏳ 2026 — DAO Governance & Marketplace

\* Agent plugin marketplace opens to developers
\* Community roadmap voting via token quorum
\* Ecosystem grants and template royalties enabled
\* Zapier, Slack, Shopify and Notion integrations
\* Global staker base with ongoing USDC revenue share

---

# Unknown

\> For the complete documentation index, see \[llms.txt\](https://synthesise-ai.gitbook.io/synthesise-ai/llms.txt). Markdown versions of documentation pages are available by appending \`.md\` to page URLs; this page is available as \[Markdown\](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/automations.md).

# Automations

Synthesise AI features a powerful visual automation engine that allows users to chain together intelligent triggers and actions inside their product workflows. Automations are the bridge between user behavior and system response, enabling seamless personalization, monetization, and engagement—without writing code.

\*\*\*

#### 1. Automation Architecture

\*\*1.1 Core Design\*\*

Automations are compiled into a lightweight rule engine that listens for defined events (e.g., purchase, quiz completion, drop-off) and responds with one or more predefined actions (e.g., send email, issue NFT, update state).

Each automation includes:

\* \*\*Trigger\*\* – the system event to listen for
\* \*\*Condition\*\* – optional logic (time delay, user segment)
\* \*\*Action\*\* – system behavior (send email, log event, mint token)

\*\*1.2 Rust Struct Representation\*\*

\`\`\`rust
enum Trigger {
    Purchase(String),
    DropOff,
    Completion,
}

enum Action {
    Email(String),
    Webhook(String),
    IssueToken(String),
}

struct Automation {
    trigger: Trigger,
    action: Action,
}
\`\`\`

\*\*\*

#### 2. Visual Builder

Users can build automations using a no-code canvas interface. Nodes represent events, conditions, and actions. Paths can be dragged to connect logic visually.

Common visual flows:

\* Purchase → Delay (24h) → Email: “Welcome Module”
\* Abandon Cart → Email: “Reminder” → Delay (6h) → SMS: “Still Interested?”
\* Module Complete → Issue Token → Unlock Bonus

The visual flow is compiled behind the scenes into a Rust automation object.

\*\*\*

#### 3. Event System

Synthesise’s automation engine is event-driven. It listens to an internal event bus across the application (e.g., purchases, interactions, completions) and matches these against active automation rules.

Example event consumption:

\`\`\`rust
fn dispatch(event: Trigger) -> Option<Action> {
    match event {
        Trigger::Purchase(product) => Some(Action::Email(format!("Thanks for purchasing {}", product))),
        Trigger::DropOff => Some(Action::Email("Reminder to complete your course".to\_string())),
        \_ => None,
    }
}
\`\`\`

\*\*\*

#### 4. Supported Actions

| Action Type    | Description                                            |
| -------------- | ------------------------------------------------------ |
| \`Email()\`      | Sends templated email through integrated mail provider |
| \`Webhook()\`    | Calls external system with product metadata            |
| \`IssueToken()\` | Mints tokenized rewards or NFTs                        |
| \`Delay()\`      | Defers next step by minutes/hours/days                 |
| \`Split()\`      | Randomizes or segments flow paths                      |
| \`Log()\`        | Saves output to analytics or CRM system                |

\*\*\*

#### 5. Advanced Use Cases

\*\*5.1 Dynamic Segmentation\*\*

Users can build dynamic audience segments (e.g. “clicked but didn’t buy,” “watched 75%”) and create tailored follow-up flows per segment.

\*\*5.2 Recurring Sequences\*\*

Automations can be looped for recurring billing reminders, educational check-ins, or progressive onboarding modules.

\*\*5.3 NFT Certificate Distribution\*\*

Issue personalized, verifiable certificates for course completion:

\`\`\`rust
fn issue\_certificate(user\_id: &str) -> Action {
    Action::IssueToken(format!("certificate\_for\_{}", user\_id))
}
\`\`\`

\*\*\*

#### 6. Deployment & Execution

Automations are stored as JSON payloads in the database, compiled at runtime into action trees, and executed by a multi-threaded scheduler in Rust.

\`\`\`rust
fn run\_automation(auto: Automation) {
    match auto.action {
        Action::Email(body) => println!("Sending email: {}", body),
        Action::IssueToken(token) => println!("Minting token: {}", token),
        \_ => println!("Unhandled action"),
    }
}
\`\`\`

---

# Unknown

\> For the complete documentation index, see \[llms.txt\](https://synthesise-ai.gitbook.io/synthesise-ai/llms.txt). Markdown versions of documentation pages are available by appending \`.md\` to page URLs; this page is available as \[Markdown\](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/chatbots.md).

# 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\*\*

\`\`\`rust
struct KnowledgeBase {
    product\_id: String,
    items: Vec<String>,
}

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

\*\*2.3 Embedding\*\*

\`\`\`html
<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\*\*

\`\`\`rust
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

\`\`\`rust
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 |

---

# Unknown

\> For the complete documentation index, see \[llms.txt\](https://synthesise-ai.gitbook.io/synthesise-ai/llms.txt). Markdown versions of documentation pages are available by appending \`.md\` to page URLs; this page is available as \[Markdown\](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/ai-agents.md).

# AI Agents

AI Agents in Synthesise are autonomous modules trained to complete specific strategic, operational, or creative functions in the product development lifecycle. While chatbots focus on reactive conversation, agents proactively generate, analyze, and optimize.

These agents operate within flows, execute independently or as part of teams, and communicate via internal protocols.

\*\*\*

#### 1. Agent Architecture

Each agent is made up of:

\* \*\*Purpose\*\* – the specific outcome it’s optimized for (e.g., SEO, pricing, UX)
\* \*\*Model\*\* – the underlying LLM or ruleset (GPT-4, Claude, or fine-tuned local models)
\* \*\*Memory\*\* – local session context plus shared product knowledge
\* \*\*Interface\*\* – a unified \`Agent\` trait implemented across all agents

\`\`\`rust
pub trait Agent {
    fn name(&self) -> &str;
    fn execute(&self, input: &str) -> String;
}
\`\`\`

\*\*\*

#### 2. Available Agents

| Agent                 | Description                                                     |
| --------------------- | --------------------------------------------------------------- |
| \*\*TutorAgent\*\*        | Generates educational outlines, assessments, and learning flows |
| \*\*SEOAgent\*\*          | Produces search-optimized titles, meta tags, alt text           |
| \*\*UXAuditAgent\*\*      | Evaluates module structure and friction points                  |
| \*\*MonetizationAgent\*\* | Recommends pricing tiers, upsell logic, and access paths        |

Example:

\`\`\`rust
pub struct SEOAgent;

impl Agent for SEOAgent {
    fn name(&self) -> &str {
        "SEOAgent"
    }

    fn execute(&self, input: &str) -> String {
        format!("SEO tags for '{}': future, disruption, AI", input)
    }
}
\`\`\`

\*\*\*

#### 3. Agent Lifecycle

1. \*\*Invocation\*\*\\
   Triggered by user flow (e.g. creating a new module) or other agents
2. \*\*Prompt Assembly\*\*\\
   Combines charter + user context + input for LLM inference
3. \*\*Execution\*\*\\
   Outputs are streamed or completed depending on use case
4. \*\*Logging & Feedback\*\*\\
   Outputs are saved to session memory and rated for accuracy
5. \*\*Replay/Mutation\*\*\\
   Outputs can be forked or regenerated with new parameters

\*\*\*

#### 4. Multi-Agent Collaboration

Agents can interact in swarm-style flows using a shared product state. For example:

\`\`\`rust
let agents: Vec<Box<dyn Agent>> = vec!\[\
    Box::new(SEOAgent),\
    Box::new(MonetizationAgent),\
\];

for agent in agents {
    println!("{}: {}", agent.name(), agent.execute("digital product"));
}
\`\`\`

This enables compound outputs: SEOAgent might propose the headline, MonetizationAgent adjusts pricing based on that.

\*\*\*

#### 5. Custom Agent Creation (Pro Feature)

Pro users will be able to define their own agents with:

\* Input/output schema templates
\* Prompt libraries
\* Optional fine-tuned model integration

Early versions are compiled to WASM and run inside secure sandboxes.

---

# Unknown

\> For the complete documentation index, see \[llms.txt\](https://synthesise-ai.gitbook.io/synthesise-ai/llms.txt). Markdown versions of documentation pages are available by appending \`.md\` to page URLs; this page is available as \[Markdown\](https://synthesise-ai.gitbook.io/synthesise-ai/usdsynai-information/holder-benefits.md).

# Holder Benefits

Holding $SYNAI isn’t just speculative—it’s a gateway to participating in the future of intelligent product creation. Synthesise AI rewards contributors, builders, and believers through revenue sharing, access benefits, and on-chain governance authority.

\*\*\*

#### 1. Revenue Share

\*\*Mechanism\*\*

Token holders who stake $SYNAI in the Synthesise protocol contract receive a percentage of platform subscription revenue (paid in USDC) each month.

\*\*Distribution Logic\*\*

\* Rewards pool is funded by 10% of gross platform revenue
\* Distributed proportionally to staked $SYNAI
\* Snapshot-based system with a 7-day lock period

\`\`\`rust
struct Staker {
    wallet: String,
    amount: u64,
}

fn calculate\_rewards(stakers: Vec<Staker>, total\_rewards: u64) -> Vec<(String, u64)> {
    let total\_staked: u64 = stakers.iter().map(|s| s.amount).sum();
    stakers.into\_iter()
        .map(|s| (s.wallet, total\_rewards \* s.amount / total\_staked))
        .collect()
}
\`\`\`

\*\*\*

#### 2. Early Access

\*\*Benefits\*\*

\* First access to beta modules, experimental agent types, and automation templates
\* Closed-loop feedback program: stakers influence LLM tuning and template logic

\*\*Examples\*\*

\* Pre-release testing of Multi-Agent Negotiation Swarm
\* Early mint of Synthesise x Partner NFTs (token-gated access)

\*\*\*

#### 3. Platform Governance

\*\*Rights\*\*

$SYNAI holders can vote on:

\* New feature priority
\* Integration roadmap
\* Token utility expansions
\* Treasury grant approvals

\*\*Voting Logic\*\*

One token = one vote. Votes are cast on-chain and executed via DAO smart contracts.

\*\*\*

#### 4. Builder Perks

| Holding Threshold | Reward Type                            |
| ----------------- | -------------------------------------- |
| 1,000 $SYNAI      | Custom dashboard theme                 |
| 10,000 $SYNAI     | Free agent credit monthly              |
| 100,000 $SYNAI    | Featured slot on marketplace homepage  |
| 250,000+ $SYNAI   | Private access to team + L2 launch pad |

\*\*\*

#### 5. Affiliate Boost

Stakers unlock higher referral commissions via a tiered affiliate program. Commission % scales with staked amount, starting at 20% and reaching 40% at max tier.

---

# Unknown

\> For the complete documentation index, see \[llms.txt\](https://synthesise-ai.gitbook.io/synthesise-ai/llms.txt). Markdown versions of documentation pages are available by appending \`.md\` to page URLs; this page is available as \[Markdown\](https://synthesise-ai.gitbook.io/synthesise-ai/usdsynai-information/tokenomics.md).

# Tokenomics

Synthesise AI is launching its native utility token, \*\*$SYNAI\*\*, to fuel platform interactions, governance, and economic alignment between builders, holders, and agents. The token is designed to extend beyond mere speculation—becoming an integral component of access control, feature unlocks, credit purchasing, and revenue distribution.

\*\*\*

#### 1. Launch Details

\* \*\*Token Name:\*\* Synthesise AI Token
\* \*\*Ticker:\*\* $SYNAI
\* \*\*Launch Platform:\*\* pump.fun (Solana)
\* \*\*Token Standard:\*\* SPL (Solana Program Library)

\*\*Supply Breakdown\*\*

| Allocation        | Percentage | Notes                               |
| ----------------- | ---------- | ----------------------------------- |
| Liquidity Pool    | 95%        | Immediately tradable                |
| Developer Reserve | 5%         | Locked via time-based vesting (12m) |

\`\`\`rust
struct Tokenomics {
    total\_supply: u64,
    liquidity: u64,
    dev\_reserve: u64,
}

fn generate\_tokenomics() -> Tokenomics {
    Tokenomics {
        total\_supply: 1\_000\_000\_000,
        liquidity: 950\_000\_000,
        dev\_reserve: 50\_000\_000,
    }
}
\`\`\`

\*\*\*

#### 2. Use Cases

\*\*2.1 Credit Purchase\*\*

$SYNAI can be used to buy creation credits inside the platform at discounted rates.

\*\*2.2 Access Unlock\*\*

Exclusive flows, agents, and templates can be gated behind token-based thresholds.

\*\*2.3 Staking & Revenue Share\*\*

Stakers of $SYNAI receive a monthly share of Synthesise’s subscription revenue pool (paid in USDC).

\*\*2.4 Voting & Governance\*\*

Token holders vote on:

\* Product roadmap features
\* Token utility extensions
\* Partnership integrations
\* Grant distributions

\*\*\*

#### 3. Emission Policy

There is \*\*no ongoing inflation\*\*. The entire supply is minted at launch. All staking rewards and incentives are paid from real platform revenue, not token dilution.

\*\*\*

#### 4. Smart Contract Governance

Governance rights are executed via a multi-signature DAO contract with quorum checks and on-chain vote execution:

\`\`\`rust
struct Proposal {
    id: u32,
    description: String,
    yes\_votes: u64,
    no\_votes: u64,
}

fn pass(proposal: &Proposal) -> bool {
    proposal.yes\_votes > proposal.no\_votes && proposal.yes\_votes > 1\_000\_000
}
\`\`\`

\*\*\*

#### 5. Liquidity Considerations

\* Liquidity is initially seeded via pump.fun
\* After bootstrapping, Synthesise deploys LPs on Raydium & Jupiter
\* Treasury incentives (grants, airdrops) are transparently tracked on-chain

---

# Architecture | Synthesise AI

For the complete documentation index, see [llms.txt](https://synthesise-ai.gitbook.io/synthesise-ai/llms.txt)
. This page is also available as [Markdown](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure/architecture.md)
.

Synthesise AI is built on a composable, microservice-driven infrastructure written in Rust, deployed via Kubernetes, and optimized for low-latency AI orchestration. Its design prioritizes modularity, resilience, and high-throughput LLM interaction.

* * *

#### 1\. Core Flow Engine[](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure#id-1.-core-flow-engine)

**Purpose**

Responsible for coordinating execution across agents, prompts, memory layers, and UI responses. It ensures every user action in a product flow is context-aware and state-consistent.

**Internal Logic**

*   Pulls agent responses via async Rust tasks
    
*   Routes payloads between system modules (UVZ, charter, chatbots)
    
*   Writes flow data to Redis for short-term caching, Postgres for history
    

Copy

    struct FlowRequest {
        user_id: String,
        input: String,
    }
    
    fn handle_flow(request: FlowRequest) -> String {
        format!("Processing flow for {}: {}", request.user_id, request.input)
    }

* * *

#### 2\. Agent Runtime Layer[](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure#id-2.-agent-runtime-layer)

**Purpose**

Executes autonomous agent logic across multiple threads, often using pre-trained transformers (e.g., GPT-4, Claude, or local models).

**Capabilities**

*   Custom toolchains for SEO, structure generation, pricing logic
    
*   Multi-agent collaboration on a shared product state
    
*   Streaming response mode for real-time frontends
    

Copy

    trait Agent {
        fn act(&self, input: &str) -> String;
    }
    
    struct BlueprintAgent;
    
    impl Agent for BlueprintAgent {
        fn act(&self, input: &str) -> String {
            format!("Generated outline for '{}'", input)
        }
    }

* * *

#### 3\. State & Memory Layer[](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure#id-3.-state-and-memory-layer)

**Purpose**

Combines fast-access state (Redis) with persistent product memory (PostgreSQL). Every product flow generates a state tree, enabling rollback, simulation, and audit.

**Design Notes**

*   Each module (e.g., UVZ, Automation) has its own schema
    
*   Redis used for ephemeral chatbot context and rate-limiting
    
*   Database holds: charter drafts, agent logs, API results, user sessions
    

Copy

    struct ProductMemory {
        id: u64,
        key: String,
        value: String,
    }
    
    fn save_memory(mem: ProductMemory) {
        println!("Saved {}: {}", mem.key, mem.value);
    }

* * *

#### 4\. Monetise + Subscription Engine[](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure#id-4.-monetise--subscription-engine)

**Purpose**

Integrates real-time access control, usage tracking, and revenue metrics. Synthesise AI’s billing logic is tightly bound to the build credit system.

**Key Components**

*   Stripe webhook processor for payments, renewals, plan upgrades
    
*   Runtime gatekeeping that checks tier permissions and deducts credits
    

Copy

    struct User {
        tier: String,
        credits: u8,
    }
    
    fn use_credit(user: &mut User) -> Result<(), &'static str> {
        if user.credits > 0 {
            user.credits -= 1;
            Ok(())
        } else {
            Err("No credits left")
        }
    }

* * *

#### 5\. Automation + Event Router[](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure#id-5.-automation--event-router)

**Purpose**

Translates product actions (e.g. purchase, completion) into automated system behaviors using a rule-based event engine.

**Architecture**

*   Event queue processed via background workers
    
*   Rules stored as JSON, parsed and compiled at runtime
    
*   Can call email APIs, trigger NFT minting, send Stripe webhooks, or assign rewards
    

Copy

    enum Trigger {
        OnPurchase(String),
        OnDropoff,
        OnSuccess,
    }
    
    fn dispatch(trigger: Trigger) {
        match trigger {
            Trigger::OnPurchase(p) => println!("Delivering product {}", p),
            Trigger::OnDropoff => println!("Reminder sequence triggered"),
            Trigger::OnSuccess => println!("Badge minted"),
        }
    }

[PreviousProducts](https://synthesise-ai.gitbook.io/synthesise-ai/getting-started/products)
[NextRoadmap](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure/roadmap)

Last updated 1 year ago

---

# Tokenomics | Synthesise AI

For the complete documentation index, see [llms.txt](https://synthesise-ai.gitbook.io/synthesise-ai/llms.txt)
. This page is also available as [Markdown](https://synthesise-ai.gitbook.io/synthesise-ai/usdsynai-information/tokenomics.md)
.

Synthesise AI is launching its native utility token, **$SYNAI**, to fuel platform interactions, governance, and economic alignment between builders, holders, and agents. The token is designed to extend beyond mere speculation—becoming an integral component of access control, feature unlocks, credit purchasing, and revenue distribution.

* * *

#### 1\. Launch Details[](https://synthesise-ai.gitbook.io/synthesise-ai/usdsynai-information#id-1.-launch-details)

*   **Token Name:** Synthesise AI Token
    
*   **Ticker:** $SYNAI
    
*   **Launch Platform:** pump.fun (Solana)
    
*   **Token Standard:** SPL (Solana Program Library)
    

**Supply Breakdown**

Allocation

Percentage

Notes

Liquidity Pool

95%

Immediately tradable

Developer Reserve

5%

Locked via time-based vesting (12m)

Copy

    struct Tokenomics {
        total_supply: u64,
        liquidity: u64,
        dev_reserve: u64,
    }
    
    fn generate_tokenomics() -> Tokenomics {
        Tokenomics {
            total_supply: 1_000_000_000,
            liquidity: 950_000_000,
            dev_reserve: 50_000_000,
        }
    }

* * *

#### 2\. Use Cases[](https://synthesise-ai.gitbook.io/synthesise-ai/usdsynai-information#id-2.-use-cases)

**2.1 Credit Purchase**

$SYNAI can be used to buy creation credits inside the platform at discounted rates.

**2.2 Access Unlock**

Exclusive flows, agents, and templates can be gated behind token-based thresholds.

**2.3 Staking & Revenue Share**

Stakers of $SYNAI receive a monthly share of Synthesise’s subscription revenue pool (paid in USDC).

**2.4 Voting & Governance**

Token holders vote on:

*   Product roadmap features
    
*   Token utility extensions
    
*   Partnership integrations
    
*   Grant distributions
    

* * *

#### 3\. Emission Policy[](https://synthesise-ai.gitbook.io/synthesise-ai/usdsynai-information#id-3.-emission-policy)

There is **no ongoing inflation**. The entire supply is minted at launch. All staking rewards and incentives are paid from real platform revenue, not token dilution.

* * *

#### 4\. Smart Contract Governance[](https://synthesise-ai.gitbook.io/synthesise-ai/usdsynai-information#id-4.-smart-contract-governance)

Governance rights are executed via a multi-signature DAO contract with quorum checks and on-chain vote execution:

Copy

    struct Proposal {
        id: u32,
        description: String,
        yes_votes: u64,
        no_votes: u64,
    }
    
    fn pass(proposal: &Proposal) -> bool {
        proposal.yes_votes > proposal.no_votes && proposal.yes_votes > 1_000_000
    }

* * *

#### 5\. Liquidity Considerations[](https://synthesise-ai.gitbook.io/synthesise-ai/usdsynai-information#id-5.-liquidity-considerations)

*   Liquidity is initially seeded via pump.fun
    
*   After bootstrapping, Synthesise deploys LPs on Raydium & Jupiter
    
*   Treasury incentives (grants, airdrops) are transparently tracked on-chain
    

[PreviousAutomations](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/automations)
[NextHolder Benefits](https://synthesise-ai.gitbook.io/synthesise-ai/usdsynai-information/holder-benefits)

Last updated 1 year ago

---

# AI Agents | Synthesise AI

For the complete documentation index, see [llms.txt](https://synthesise-ai.gitbook.io/synthesise-ai/llms.txt)
. This page is also available as [Markdown](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/ai-agents.md)
.

AI Agents in Synthesise are autonomous modules trained to complete specific strategic, operational, or creative functions in the product development lifecycle. While chatbots focus on reactive conversation, agents proactively generate, analyze, and optimize.

These agents operate within flows, execute independently or as part of teams, and communicate via internal protocols.

* * *

#### 1\. Agent Architecture[](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules#id-1.-agent-architecture)

Each agent is made up of:

*   **Purpose** – the specific outcome it’s optimized for (e.g., SEO, pricing, UX)
    
*   **Model** – the underlying LLM or ruleset (GPT-4, Claude, or fine-tuned local models)
    
*   **Memory** – local session context plus shared product knowledge
    
*   **Interface** – a unified `Agent` trait implemented across all agents
    

Copy

    pub trait Agent {
        fn name(&self) -> &str;
        fn execute(&self, input: &str) -> String;
    }

* * *

#### 2\. Available Agents[](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules#id-2.-available-agents)

Agent

Description

**TutorAgent**

Generates educational outlines, assessments, and learning flows

**SEOAgent**

Produces search-optimized titles, meta tags, alt text

**UXAuditAgent**

Evaluates module structure and friction points

**MonetizationAgent**

Recommends pricing tiers, upsell logic, and access paths

Example:

Copy

    pub struct SEOAgent;
    
    impl Agent for SEOAgent {
        fn name(&self) -> &str {
            "SEOAgent"
        }
    
        fn execute(&self, input: &str) -> String {
            format!("SEO tags for '{}': future, disruption, AI", input)
        }
    }

* * *

#### 3\. Agent Lifecycle[](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules#id-3.-agent-lifecycle)

1.  **Invocation** Triggered by user flow (e.g. creating a new module) or other agents
    
2.  **Prompt Assembly** Combines charter + user context + input for LLM inference
    
3.  **Execution** Outputs are streamed or completed depending on use case
    
4.  **Logging & Feedback** Outputs are saved to session memory and rated for accuracy
    
5.  **Replay/Mutation** Outputs can be forked or regenerated with new parameters
    

* * *

#### 4\. Multi-Agent Collaboration[](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules#id-4.-multi-agent-collaboration)

Agents can interact in swarm-style flows using a shared product state. For example:

Copy

    let agents: Vec<Box<dyn Agent>> = vec![\
        Box::new(SEOAgent),\
        Box::new(MonetizationAgent),\
    ];
    
    for agent in agents {
        println!("{}: {}", agent.name(), agent.execute("digital product"));
    }

This enables compound outputs: SEOAgent might propose the headline, MonetizationAgent adjusts pricing based on that.

* * *

#### 5\. Custom Agent Creation (Pro Feature)[](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules#id-5.-custom-agent-creation-pro-feature)

Pro users will be able to define their own agents with:

*   Input/output schema templates
    
*   Prompt libraries
    
*   Optional fine-tuned model integration
    

Early versions are compiled to WASM and run inside secure sandboxes.

[PreviousRoadmap](https://synthesise-ai.gitbook.io/synthesise-ai/platform-infrastructure/roadmap)
[NextChatbots](https://synthesise-ai.gitbook.io/synthesise-ai/core-modules/chatbots)

Last updated 1 year ago

---