Orbit Platform Documentation
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  • Welcome
  • 1. Introduction
    • 1.1 Orbit Platform Overview
    • 1.2 Key Features
    • 1.3 Target Audience
    • 1.4 Benefits of Using Orbit Platform
    • 1.5 Overview of this Documentation
  • 2. Quick Start
    • 2.1 Accessing Orbit Platform
    • 2.2 Navigating the User Interface
    • 2.3 Basic User Cases
      • 2.3.1 Conducting a Semantic Search
      • 2.3.2 Copilot Chat
      • 2.3.3 Browsing and Using Pre-Defined Bots
    • 2.4 Exploring the Bot Marketplace
    • 2.5 Understanding SaaS Features and Limitations
  • 3. Platform Overview
    • 3.1 Overview of Orbit Platform
    • 3.2 Orbit AI Studio
      • 3.2.1 Data Loaders
      • 3.2.2 Metadata Management
      • 3.2.3 PDF Pre-Processing
      • 3.2.4 LLM Integration
      • 3.2.5 Workflow Automation
    • 3.3 Custom Knowledge Base Creation
    • 3.4 Chat and Search Capabilities
    • 3.5 Bot Marketplace
      • 3.5.1 Overview of the Bot Marketplace
      • 3.5.2 Creating and Managing Bots
      • 3.5.3 Automating Manual Tasks with Bots
  • 3.6 Data Connectors
  • 4. User Guide
    • 4.1 General User Interface
      • 4.1.1 Portfolio Management
      • 4.1.2 Concept Management
      • 4.1.3 Share
    • 4.2 Semantic Search and Chat
    • 4.3 Features on Single Document
    • 4.4 Create Your Knowledge Base
  • 5. Orbit Knowledge Bases
    • 5.1 Introduction
  • 5.2 Global Exchange Filings
  • 5.3 China Earnings Transcripts
  • 5.4 Global Sustainability Reports
  • 5.5 Global Regulation Documents
  • 5.6 Global Earnings Transcripts
  • 5.7 Listed Companies Official Documents
  • 5.8 Private Companies Official Documents
  • 5.9 Google News
  • 5.10 China Bond Documents
  • 6. Off-the-Shelf Bots
    • 6.1 Data Transformer
    • 6.2 Filings Insight Extractor
    • 6.3 Portfolio News Tracker
    • 6.4 Summary Composer
    • 6.5 Financial Statement Navigator
    • 6.6 Earning Call Calendar
    • 6.7 News Flow Tracker
  • 6.8 SmartMonitor Bot
  • 7. Pricing
    • 7.1 Product Options
    • 7.2 SaaS Pricing Structure
  • 7.3 Product Selection Guide
  • 8. Enterprise Deployment
    • 8.1 Deployment Options
    • 8.2 Security and Compliance
    • 8.3 Scaling and Performance
    • 8.4 Integration with Existing Systems
  • 9. Use Cases and Examples
    • 9.1 Investment Research Use Cases
      • 9.1.1 Generate a Research Report with Copilot Chat
      • 9.1.2 Analyse Investment Themes from Annual Reports
    • 9.2 Sustainability Use Cases
      • 9.2.1 Generate an ESG Report with Copilot Chat
      • 9.2.2 Orbit vs Claude vs Perplexity
    • 9.3 Service Provider Use Cases
    • 9.4 Case Studies: Success Stories
  • 10. FAQ and Troubleshooting
    • 10.1 Common Questions
    • 10.2 Contacting Support
  • 11. Appendices
    • 11.1 Glossary of Terms
    • 11.2 Whitepapers
      • Advancing News Analytics for Financial Decision Making
    • 11.3 Release Notes
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  • 1. Intelligent Web Scraping System
  • 2. APIs for Client Data Integration
  • 3. Microsoft Graph API Integration
  1. 3. Platform Overview
  2. 3.2 Orbit AI Studio

3.2.1 Data Loaders

The effectiveness of Orbit Insight relies heavily on its ability to efficiently source, process, and integrate large volumes of data. To achieve this, Orbit has developed a suite of sophisticated data loaders that ensure high-quality data input, enabling the platform to deliver accurate and actionable insights.

1. Intelligent Web Scraping System

Orbit Insight is equipped with an advanced web scraping system that is specifically designed to handle the complexities of sourcing data from a vast number of websites. This system is not just about collecting data—it is built to manage large-scale operations with a focus on data quality and accuracy.

  • High-Volume Data Collection: The web scraping system is capable of sourcing documents from a wide range of websites, efficiently handling large volumes of data. This allows Orbit to build comprehensive knowledge bases that are continually updated with the latest information.

  • Accurate Metadata Capture: One of the critical challenges in web scraping is capturing metadata accurately. Orbit’s system addresses this by implementing advanced algorithms that ensure metadata is not only captured but also accurately classified. This is essential for maintaining the integrity and usability of the data within Orbit Insight.

  • Document Classification: To further enhance the accuracy of the data, the system includes robust document classification processes. These processes categorize and tag documents appropriately, ensuring that users can retrieve the most relevant data quickly and efficiently.

  • Data Quality Focus: The web scraping system is designed with a strong emphasis on data quality. This means achieving high levels of coverage (sourcing from as many relevant websites as possible), completeness (ensuring all necessary data points are captured), and low latency (minimizing the time between data being published on the web and being available within Orbit Insight).

[Placeholder for Diagram] Include a diagram or flowchart illustrating the web scraping process, highlighting the steps from data collection to metadata classification.

2. APIs for Client Data Integration

In addition to sourcing data from public websites, Orbit Insight can integrate seamlessly with a client’s internal systems. This is achieved through the development of custom APIs, designed to centralize and manage internal data effectively.

  • Centralized Data Management: By developing APIs tailored to a client’s specific needs, Orbit Insight can centralize data from various internal sources. This ensures that all relevant data is easily accessible and managed within a single platform, enhancing the overall efficiency of data processing and analysis.

  • Custom API Development: Orbit’s team works closely with clients to understand their internal systems and data requirements, enabling the development of APIs that integrate smoothly with existing infrastructures. This customization ensures that data flows seamlessly into Orbit Insight, where it can be analyzed alongside external data sources.

  • Enhanced Data Security: When integrating with internal systems, data security is a top priority. Orbit’s APIs are designed with robust security measures to protect sensitive information, ensuring that client data is handled with the highest levels of confidentiality and integrity.

[Placeholder for API Integration Example] Include a visual representation or example of how a custom API integrates client data with Orbit Insight.

3. Microsoft Graph API Integration

To further enhance its data integration capabilities, Orbit Insight has incorporated Microsoft Graph APIs, allowing the platform to easily consume and process data from a client’s Microsoft environment.

  • Seamless Microsoft Data Integration: With Microsoft Graph API integration, Orbit Insight can access and analyze data from Microsoft services such as Office 365, SharePoint, OneDrive, and Outlook. This capability allows clients to bring in relevant documents, emails, and other data types directly into Orbit Insight, where it can be processed and analyzed alongside other data sources.

  • Efficient Data Consumption: The integration with Microsoft Graph APIs is designed to be efficient and user-friendly, making it easy for clients to connect their Microsoft data with Orbit Insight. This reduces the complexity of data integration, enabling users to quickly start leveraging their Microsoft data within the platform.

  • Real-Time Data Access: The integration allows for real-time data access and updates, ensuring that the most current information is always available for analysis. This is particularly valuable for dynamic environments where data is frequently updated, such as financial services.

[Placeholder for Microsoft Graph API Integration Screenshot] Include a screenshot or diagram showing how Microsoft Graph API integrates with Orbit Insight, illustrating the flow of data from Microsoft services into the platform.

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Last updated 8 months ago