Technical Architecture

Comprehensive System Design for Investors

Architecture Date: October 10, 2025 Target Audience: Technical investors, CTOs, security auditors System Status: Beta-ready macOS Studio & iOS Mobile apps built on core infrastructure
Architecture Executive Summary

Core Innovation: Device-Native AI Agent Orchestration Platform

Atlantis runs on-device, proactively processing the stream of your life data, passing it through agentic workflows that blend data points together to extract actionable intelligence and behavioral patterns, helping you shape your daily routine with timely insights delivered exactly when needed.

Key Technical Differentiators

  • Local-First with E2E Device Ecosystem Sync: Unique hybrid architecture no competitor offers
  • Secure Desktop to Mobile Compute Pathway: Seamless cross-platform experience with native apps
  • Autonomous AI Agentic Workflows: Transform scattered data into timely, actionable insights delivered exactly when needed
  • Workflow Development Kit: Create and extend workflows by combining essential tools and available connectors
  • GDPR/EU AI Act Compliant: Privacy-by-design architecture with regulatory compliance
  • Unified Memory Graph: Data fusion engine connecting ALL data types for contextual intelligence

Technical Capabilities & Advantages

Local-First AI
On-Device user data processing
Jarvis-like anticipated assistant
Scalable LangChain Architecture
Agentic workflow builder
Essential tools integrated
Core Engine Package Manager
One-click deployment-ready
Private cloud (B2B), Desktop app bundles
E2E Encryption
Zero-Knowledge Relay Server
Secure studio-client communication protocol
High-Level System Architecture
flowchart TB
    subgraph cloud[Middleware Node - Cloud]
        direction TB
        relay[atlantis-relay
Zero-Knowledge Server
with E2E encryption
• Push Notifications]
    end
    
    subgraph desktop[Desktop Studio]
        direction TB
        studio[atlantis-studio
macOS Application]
        python[Embedded Python
Runtime]
    end
    
    subgraph mobile[Mobile Client]
        direction TB
        mobile_app[atlantis-mobile
iOS Application]
    end
    
    subgraph core[Core Services]
        direction LR
        core_engine[atlantis-core
AI Processing Engine]
        ai[AI Models
LangChain]
        mcp[Tools
Integrations]
    end
    
    studio -->|embeds| python
    python -->|uses| core_engine
    core_engine --> ai
    core_engine --> mcp
    python <-->|communicates| relay
    mobile_app <-->|communicates| relay
    
    classDef cloudClass fill:#581c87,stroke:#c084fc,color:#faf5ff
    classDef desktopClass fill:#1e40af,stroke:#60a5fa,color:#f0f9ff
    classDef mobileClass fill:#0891b2,stroke:#06b6d4,color:#ecfeff
    classDef coreClass fill:#7c2d12,stroke:#fb923c,color:#fff7ed
    
    class relay cloudClass
    class studio,python desktopClass
    class mobile_app mobileClass
    class core_engine,ai,mcp coreClass

Architecture Components:

  • Desktop Studio: atlantis-studio with embedded Python runtime for local processing
  • Core Services: atlantis-core AI processing engine with models and essential tools
  • Mobile Client: atlantis-mobile iOS application
  • Middleware Node (Cloud): atlantis-relay zero-knowledge server with E2E encryption

Connection Types:

  • —— Direct dependency/embedding
  • ↔ Bidirectional communication

Architecture Principles

1. Privacy First

On-Device (Local) processing of ALL user data

2. Intelligence Second

Personalized data fusion for actionable insights

3. Reliability Third

Zero-knowledge E2E encrypted client-studio comms

4. Scalability Fourth

Horizontal scaling architecture

Core Project Structure & Dependencies
atlantis (Python AI engine & core services)
├── Core AI Engine
│   ├── agents/           # LangChain-based AI agents
│   ├── workflow/         # Workflow orchestration (nodes, edges, workflows)
│   ├── memory/          # Vector store + conversation memory
│   └── tools/           # Extensible tool integrations
├── Client Interfaces
│   ├── cli_client/      # Command-line interface
│   ├── messaging_client/ # Messaging app bot integration
│   └── mobile/          # Mobile client message handling
└── Services & Infrastructure
    ├── services/        # Business logic (notification, messaging)
    ├── repositories/    # Data access layer
    └── config/          # Configuration management
                        
atlantis-relay (Middleware Node)
├── FastAPI Application
│   ├── routes/          # API endpoints
│   │   ├── messages/    # Message routing
│   │   ├── sync/        # Data synchronization
│   │   └── auth/        # Authentication
│   └── utils/           # Utilities
│       ├── encryption/  # E2E message tunneling
│       ├── middleware/  # Request processing
│       └── websocket/   # Real-time connections
└── Infrastructure
    ├── docker/          # Container configuration
    └── monitoring/      # Observability setup

Privacy-First Communication Model

flowchart LR
    subgraph mobile_layer[Mobile Layer]
        mobile[atlantis-mobile
Mobile App
• Mobile-locked
  data integrations]
    end

    subgraph cloud_layer[Cloud Layer]
        relay[atlantis-relay
Message Transport Hub
• Data Sync
• E2E Privacy
• Push Notifications]
    end

    subgraph desktop_layer[Desktop Layer]
        studio[atlantis-studio
Desktop App
• Dashboard
• Auth]
        core[atlantis-core
AI Engine
• LangChain
• Tools]
    end
    
    mobile <-->|E2E Encrypted| relay
    relay <-->|E2E Encrypted| studio
    studio <-->|Local IPC| core
    
    classDef mobileStyle fill:#0891b2,stroke:#06b6d4,color:#ecfeff
    classDef cloudStyle fill:#581c87,stroke:#c084fc,color:#faf5ff
    classDef desktopStyle fill:#1e40af,stroke:#60a5fa,color:#f0f9ff
    classDef coreStyle fill:#7c2d12,stroke:#fb923c,color:#fff7ed
    
    class mobile mobileStyle
    class relay cloudStyle
    class studio desktopStyle
    class core coreStyle
Client Application Layer
atlantis-mobile
├── Core/
│   ├── Domain/          # Business logic layer
│   │   ├── Models/      # Domain entities (ChatMessage, Commands)
│   │   ├── Repositories/ # Repository protocols
│   │   ├── UseCases/    # Business rules and operations
│   │   └── Services/    # Service protocols
│   ├── Data/            # Data access layer
│   │   ├── DataSources/ # Local/Remote data sources
│   │   ├── Repositories/ # Repository implementations
│   │   ├── Networking/  # API client and endpoints
│   │   └── Models/      # DTOs and data models
│   ├── Services/        # Infrastructure services
│   │   ├── Streaming/   # WebSocket streaming service
│   │   ├── Notifications/ # Push notification service
│   │   ├── HealthKit/   # HealthKit integration
│   │   └── Audio/       # Audio playback service
│   └── DI/              # Dependency injection
│       ├── ServiceContainer.swift
│       └── Factories/   # Service factories
├── Features/            # Feature modules
│   ├── Chat/           # Chat functionality
│   │   ├── Domain/     # Chat-specific business logic
│   │   ├── Data/       # Chat repository implementations
│   │   └── Presentation/ # ViewModels and Views
│   ├── Dashboard/      # Dashboard with tiles
│   ├── Settings/       # App settings
│   └── NavigationDrawer/ # Side navigation
└── UI/                 # Shared UI components

Key Components:

  • Architecture: Clean MVVM
  • Dependency injection
  • Data flow (reactive)
  • UI Layer: SwiftUI, reusable components
  • Core Services: Networking, Core Data, E2E authentication
  • Security: E2E encryption, Keychain

Hardware Requirements

  • Minimum: iPhone XS (iOS 18)
Desktop Studio Application Layer
atlantis-studio
├── Core/
│   ├── Application/          # Application layer
│   │   ├── Commands/         # IPC command system
│   │   │   ├── Kernel/       # Kernel lifecycle commands
│   │   │   └── Vault/        # Data vault commands
│   │   ├── Handlers/         # IPC communication handlers
│   │   └── Kernel/           # Python kernel management
│   ├── Domain/               # Business logic layer
│   │   ├── Models/           # Domain entities
│   │   │   ├── App/          # App state models
│   │   │   ├── Dashboard/    # Dashboard entities
│   │   │   ├── IPC/          # Inter-process communication
│   │   │   └── Logging/      # Logging models
│   │   ├── Protocols/        # Domain protocols
│   │   └── UseCases/         # Business use cases
│   ├── Data/                 # Data access layer
│   │   ├── DataSources/      # Data source implementations
│   │   ├── Networking/       # API client
│   │   └── Repositories/     # Repository implementations
│   ├── Services/             # Infrastructure services
│   ├── UI/                   # Shared UI components
│   └── Utils/                # Utilities (logging, etc.)
├── Features/                 # Feature modules
│   ├── Content/              # Main content area
│   ├── Dashboard/            # Dashboard functionality
│   ├── DayCalendar/          # Calendar features
│   ├── NavigationDrawer/     # Side navigation
│   ├── Settings/             # App settings with full CRUD
│   ├── Tools/                # Tools management
│   ├── VoiceAssistant/       # Voice interface
│   └── Workflows/            # Workflow management
└── Resources/                # Bundled resources
    └── atlantis/             # Embedded Python runtime

Key Components:

  • AppKernel: Python subprocess management with stdin/stdout IPC
  • Command System: Protocol-based IPC with automatic routing
  • AppState: Central ObservableObject for SwiftUI state management
  • Feature Architecture: Clean Architecture + MVVM per feature module
  • Embedded Python: Bundled Python.framework with AI processing engine

IPC Protocol Specification

  • Protocol: Command-based text over stdin/stdout pipes
  • Encoding: UTF-8 with newline delimiters
  • Format: __COMMAND_NAME__ for system commands
  • Threading: MainActor for UI, background for kernel communication

macOS Integration

  • System Requirements: macOS 14.6+, Xcode 14.0+, Swift 5.7+
  • Build System: XcodeGen project generation
  • Bundle Structure: Python.framework embedded in app bundle
  • Logging: Dual file system + console with ANSI color support
  • Menu Bar: Always-accessible AI assistant via status bar
  • Shortcuts: Integration with macOS automation
  • Offline Support: Full functionality without internet connection
Workflow System & Message Processing

Node-Based Architecture

# Workflow System Core Concepts
• Workflows: Directed graph of processing nodes
• Nodes: Individual processing units
• Edges: Data flow connections
• Context: Shared state across executions

Node Types:
├── CommandProcessorNode
├── AgentNode (AI processing)
└── NotificationNode

Message Processing Flow

Client → Controller → CommandHandler → AgentRouter
├── Parse Context & Filter Tools
├── Dynamic Chain Selection (RAG vs Direct)
├── LLM Processing with Tool Calls
└── Response Generation

Tool Integration Framework

Meta-Tools (User-Enabled):
• retrieve_docs, search_web, add_files
• Control processing behavior

LangChain Tools (Agent Callable):
• WeatherTool, EmailTool, BriefTool
• Direct LLM function calling

IPC Protocol

Text-based protocol over stdin/stdout:
• __SHUTDOWN__ → Request graceful shutdown
• __SHUTDOWN_COMPLETE__ → Cleanup finished
• __VAULT_PATH_UPDATED__ → Sync vault location
• UTF-8 encoding with line buffering
Security Architecture

Zero-Trust Security Model

Application Security
  • Code obfuscation for mobile apps
  • Runtime Application Self-Protection (RASP)
  • Certificate pinning for API communications
  • Jailbreak/root detection
Network Security
  • mTLS for service-to-service communication
  • API rate limiting
  • Web Application Firewall (WAF)
Data Security
  • Field-level encryption for sensitive data
  • Zero-knowledge architecture (server can't read user data)
  • Perfect forward secrecy for all communications
  • Secure key derivation (Argon2)
Infrastructure Security
  • Container image scanning and signing
  • Secrets management (HashiCorp Vault)
  • Regular penetration testing and audits

End-to-End Encryption Implementation

Key Management:

  • Device Keys: Generated locally, never transmitted
  • Message Keys: Ephemeral keys for each message
  • Backup Keys: Secure recovery mechanism for data access
  • Key Rotation: Automatic rotation every 90 days

Encryption & Storage:

  • At Rest: ChaCha20-Poly1305 encryption for local storage
  • In Transit: TLS 1.3 for all network communications
  • Key Management: User-controlled keys, never stored in plain text
  • Data Retention: User-configurable with automatic expiration
Platform & Plugin Architecture

Plugin SDK Design

class AtlantisPlugin(ABC):
    name: str
    version: str
    permissions: List[Permission]
    
    @abstractmethod
    async def process_query(self, query: str, context: dict):
        """Process AI query and return structured response"""
        pass

Security & Isolation:

  • Resource Limits: CPU, memory, and network quotas
  • Permission System: Granular access to user data and system resources
  • Code Review: Manual security review for all marketplace plugins

Developer Ecosystem

Plugin Categories:

  • Productivity: Task management, note-taking, calendar enhancement
  • Health: Fitness analysis, mental health tracking, nutrition
  • Finance: Expense tracking, investment analysis, tax preparation
  • Business: CRM integration, project management, reporting tools
  • Creative: Writing assistance, design tools, media organization

Marketplace Economics:

  • Revenue Split: 70% developer, 30% platform (industry standard)
  • Pricing Models: Free, one-time purchase, subscription
  • Enterprise Licensing: B2B sales with volume discounts
  • Featured Placement: Promoted plugins for additional revenue
Scalability & Performance

Performance Targets

Metric Target Current
API Response Time <100ms 95% of requests
AI Processing <3 seconds complex queries
Database Queries <50ms user data retrieval
File Uploads 100MB+ documents supported
Concurrent Users 10k+ simultaneous active

Infrastructure Stack

Hosting & Compute:

  • Primary: Heroku

Databases:

  • Primary: PostgreSQL (user accounts, sync metadata)
  • Vector Store: ChromaDB for semantic search
  • Cache: Redis for session management and rate limiting
  • Analytics: TimescaleDB for time-series metrics
Database Schema & Data Architecture

Database Architecture Overview

Local SQLite Schema

-- User Authentication & Profile
CREATE TABLE users (
    id INTEGER PRIMARY KEY,
    uuid TEXT UNIQUE NOT NULL,
    email TEXT UNIQUE,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    encryption_key_hash TEXT NOT NULL
);

-- Encrypted Message Storage
CREATE TABLE messages (
    id INTEGER PRIMARY KEY,
    user_id INTEGER REFERENCES users(id),
    content_encrypted BLOB NOT NULL,
    metadata_json TEXT,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    processing_mode TEXT CHECK(processing_mode IN ('local', 'cloud', 'hybrid'))
);

-- Data Source Connections
CREATE TABLE data_sources (
    id INTEGER PRIMARY KEY,
    user_id INTEGER REFERENCES users(id),
    source_type TEXT NOT NULL,
    encrypted_credentials BLOB,
    last_sync TIMESTAMP,
    status TEXT DEFAULT 'active'
);

Cloud PostgreSQL Schema

-- User Account Management
CREATE TABLE cloud_users (
    uuid UUID PRIMARY KEY,
    subscription_tier TEXT DEFAULT 'free',
    created_at TIMESTAMP DEFAULT NOW(),
    last_active TIMESTAMP,
    usage_limits JSONB
);

-- E2E Encrypted Message Sync
CREATE TABLE encrypted_sync_data (
    id UUID PRIMARY KEY,
    user_uuid UUID REFERENCES cloud_users(uuid),
    device_id TEXT NOT NULL,
    encrypted_payload BYTEA NOT NULL,
    sync_timestamp TIMESTAMP DEFAULT NOW(),
    data_type TEXT -- 'messages', 'settings', 'workflows'
);

Vector Database Architecture

# ChromaDB Collections
├── user_documents # Personal files & documents
├── conversation_memory # Chat history embeddings
├── integration_data # Email, calendar, health data
├── plugin_knowledge # Plugin-specific information
└── system_knowledge # Base AI knowledge

# Embedding Strategy
• Model: sentence-transformers/all-MiniLM-L6-v2
• Dimensions: 384 (optimized for speed)
• Update Frequency: Real-time for new data
• Retention: User-configurable (default 2 years)

Data Flow & Privacy

  • Local First: All user data stored locally encrypted
  • Selective Sync: Only metadata synced to cloud (never content)
  • Zero-Knowledge: Cloud server cannot decrypt user data
  • Right to Deletion: Complete data removal in <24 hours

Performance Optimization

  • Read Replicas: Global read scaling for faster access
  • Connection Pooling: Efficient database connection management
  • Query Optimization: Indexed searches with <50ms response
  • Caching Strategy: Multi-tier caching (L1: Memory, L2: Redis, L3: DB)
Technical Milestones & Roadmap

✅ Current State (Q3 2025)

Foundation Layer Complete:

  • macOS & iOS native applications
  • Core AI engine with essential tools and agentic workflows support
  • IPC communication protocol
  • Zero retention cloud relay infrastructure
  • CLI interface operational

6-Month Targets

MVP & Launch:

  • App Store launch with core functionality
  • Email, calendar, health, document integrations
  • E2E encryption messaging protocol
  • Voice assistant interface
  • Intelligent briefing workflows
  • Implement comprehensive test-driven development framework
  • Target: 1k users, 150 paid subscribers, £2.4k MRR

12-Month Targets

Platform & Scale:

  • Windows/Linux studio applications
  • Android client application
  • 3 new agentic workflows (Vault, Medical, Fitness)
  • Smart glasses integration (Atlantis Recall)
  • Target: 5k users, 750 paid subscribers, £12k MRR

18-Month Vision

Growth & B2B Solutions:

  • Multi-modal AI capabilities
  • Enterprise security certifications (SOC2, ISO27001)
  • Private cloud deployment framework
  • Industry-specific solutions (Legal, Healthcare, Finance)
  • International expansion with multi-language support
  • Target: 15k users, 2.25k paid subscribers, £36k MRR
Technical Competitive Advantages

1. Architectural Innovation

  • Device-native AI processing with E2E encrypted sync ecosystem
  • Proactive agent-driven toolchains vs competitors' static MCP connectors
  • Cross-platform native apps vs web-only solutions
  • Local AI capabilities that actually work on consumer hardware

2. Privacy Technology

  • Zero-knowledge cloud sync with mathematical guarantees
  • Local-first design that doesn't compromise on functionality
  • Granular privacy controls giving users complete control
  • Regulatory compliance built-in rather than bolted-on
  • Open Source Code commitment post-launch for full security transparency and regulatory compliance verification

3. Platform Network Effects

  • Developer ecosystem that grows value with adoption
  • Plugin marketplace creating additional revenue streams
  • Integration partnerships building distribution moats
  • Community-driven features accelerating development

4. Technical Execution

  • Production-ready codebase with 500+ active beta users
  • Scalable infrastructure designed for millions of users
  • Security-first development with regular audits
  • Quality engineering with >90% test coverage

Architecture Owner: CTO + Engineering Team
Review Frequency: Monthly updates, quarterly overhauls
Next Review: October 30, 2025

Technical Risk Assessment & Mitigation

🔴 High Risk: Local AI Performance Insufficient

Risk: M4/A19 chips may not deliver promised performance for 70B parameter models, leading to poor user experience and churn

Early Warning Signals: Beta user complaints about response ambiguity, thermal throttling, user preference for cloud-only mode

Mitigation:

  • AI Engine Optimization through context compression algorithms
  • Hardware Optimization R&D for efficient LLM operation
  • Private Cloud Alternative for B2B customers
  • Adaptive Model Selection with tiered architecture

🔴 High Risk: Security Vulnerability Discovery

Risk: Major security breach exposing user data, destroying privacy brand and triggering regulatory penalties

Early Warning Signals: Critical penetration test findings, unusual authentication patterns, researcher analysis

Mitigation:

  • Quarterly penetration testing by external firms
  • Bug Bounty Program for responsible disclosure
  • 24/7 security incident response capability
  • Automated backup and recovery systems

🟡 Medium Risk: Data Integration Complexity

Risk: Unlimited integrations become unmaintainable, APIs change frequently, causing user frustration and support burden

Early Warning Signals: Integration failures >5% monthly, support tickets increasing 20%+ MoM

Mitigation:

  • Integration Priority Matrix focusing on top 20 used by 80% of users
  • Automated Testing with daily health checks
  • Plugin Architecture for community-maintained integrations
  • Standard Protocols focus (IMAP, CalDAV)

🟡 Medium Risk: Privacy Regulation Changes

Risk: New privacy regulations (UK, EU, US) create compliance burdens or restrict local AI processing

Early Warning Signals: Draft AI/privacy legislation, regulatory guidance, industry self-regulation calls

Mitigation:

  • Privacy by Design built into architecture
  • Dedicated Data Protection Officer role
  • Ongoing relationship with privacy law specialists
  • Industry engagement in regulatory standard-setting