docs/overview.md

Aurora AI Overview

This documentation provides an overview of Aurora AI, its architecture, and how it works.

Architecture

AuroraAI follows a modular, plugin-based architecture that enables extensibility and scalability:

graph TD
    A[User Interface] --> B[Core Engine]
    B --> C[Plugin Manager]
    C --> D[Knowledge Base Plugin]
    C --> E[API Integration Plugin]
    C --> F[Analytics Plugin]
    B --> G[Context Manager]
    G --> H[Session State]
    G --> I[Memory Store]

Supported Models

AuroraAI supports multiple AI model providers to suit different use cases and deployment requirements:

Provider	Model	Context Window	Best For
OpenAI	GPT-4 Turbo	128K tokens	Complex reasoning, code generation
OpenAI	GPT-3.5 Turbo	16K tokens	Fast responses, general queries
Anthropic	Claude 3 Opus	200K tokens	Long document analysis
Anthropic	Claude 3 Sonnet	200K tokens	Balanced performance
Local	Llama 2	4K tokens	Privacy-sensitive deployments

System Requirements

Component	Minimum	Recommended
CPU	4 cores	8+ cores
RAM	8 GB	16+ GB
Storage	10 GB	50+ GB (with local models)
Network	10 Mbps	100+ Mbps

Plugin Ecosystem

AuroraAI's functionality can be extended through plugins:

• Documentation Plugins: Confluence, SharePoint, Notion, GitBook
• Development Tools: GitHub, GitLab, Jira, Linear
• Communication: Slack, Microsoft Teams, Discord
• Data Sources: SQL databases, REST APIs, GraphQL endpoints

Security & Compliance

• Authentication: OAuth 2.0, SAML, API keys
• Encryption: AES-256 at rest, TLS 1.3 in transit
• Access Control: Role-based permissions (RBAC)
• Audit Logging: Complete activity tracking
• Compliance: SOC 2 Type II, GDPR, HIPAA-ready

Performance Metrics

Typical response times under standard load:

Operation	Avg Response Time	Throughput
Simple query	< 2 seconds	100 req/min
Document search	< 3 seconds	60 req/min
Code generation	3-5 seconds	40 req/min
Long document analysis	8-15 seconds	20 req/min

What Is AuroraAI?

AuroraAI is a lightweight, multimodal AI assistant designed to help teams write, search, analyze, and automate workflows across enterprise environments. It integrates seamlessly with documentation platforms, internal tools, and external APIs.

Key Features

• Natural language understanding for queries, tasks, and workflows.
• Plugin-based architecture for integrating knowledge bases, project systems, and developer tools.
• Context-aware responses optimized for technical writing and software development.
• Secure by design, with role-based access and encrypted local config.

Ideal Use Cases

• Generating and editing documentation.
• Summarizing tickets, requirements, and engineering discussions.
• Producing API specs, code snippets, troubleshooting trees, and templates.
• Providing real-time answers based on uploaded files or internal docs.