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. 9. Use Cases and Examples
  2. 9.1 Investment Research Use Cases

9.1.2 Analyse Investment Themes from Annual Reports

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Last updated 1 month ago

Identifying the technological focus of companies, especially in rapidly evolving industries, is a complex task. Each company often has specialized areas of focus, and with the continuous evolution of technology, new terms and innovations emerge regularly. Traditional methods of categorizing and tracking these technologies can quickly become outdated, leading to gaps in understanding and missed opportunities for analysis. To address this challenge, we leverage Large Language Models (LLMs) to systematically analyze public companies' annual reports and identify any innovative or disruptive technology mentioned. This innovative approach allows us to stay ahead of the curve, ensuring that we capture the most current and relevant information for analysis.

Step-by-Step Process

  1. Prompting with LLMs: We then use a specially crafted prompt with LLMs to search through these reports. The prompt we use is: "What are the innovative or disruptive technologies mentioned in the document? Please ignore the general terms and be focused on specific areas, and return these terms as comma separated without any explanations." This prompt guides the LLM to focus on identifying specific technological terms that are of particular interest, filtering out more general, less relevant terminology.

  2. Structuring Results: The LLM outputs the identified terms in a comma-separated format, which is then structured into usable data formats. This structured data forms the basis for further analysis and categorization.

  3. Classification: The next step involves classifying these terms into broader technological themes. This is done using additional prompts that guide the LLM to group related terms into categories such as "Artificial Intelligence," "Blockchain," "Quantum Computing," etc. This classification helps in organizing the data and making it easier to analyze.

  4. Analysis: Once the terms are classified, the data is ready for analysis. Analysts can use this information to assess which technologies are gaining traction across different industries, which companies are leading in certain areas, and how technological trends are evolving over time.

By utilizing LLMs in this innovative way, we are able to capture the fast-paced changes in technology that companies are adopting. This method not only provides a more accurate picture of the current technological landscape but also allows us to predict future trends by identifying emerging technologies early on.

The full result from S&P500 annual reports is available here.

Data Collection: We utilize annual reports directly from our . This knowledge base is an extensive repository of publicly filed documents from global exchanges, ensuring that we have comprehensive and up-to-date information at our fingertips. This direct access is a significant time-saver, allowing us to quickly gather the data needed for analysis.

global exchange filings knowledge base
387KB
Innovative Technology Terms from S&P500 Annual Reports.xlsx