3.3.5 Workflow Automation

Orbit Insight empowers users to automate complex workflows, significantly enhancing efficiency and productivity in data-driven tasks such as investment research. By leveraging high-quality, machine-readable data and advanced processing capabilities, Orbit Insight transforms how workflows are managed and executed.

1. Building Blocks of Workflow Automation

Orbit Insight’s workflow automation is built on three key pillars that ensure seamless and efficient operation:

1.1 Universal Metadata

All data within Orbit Insight is tagged with a universal metadata framework, ensuring that information is consistently organized and easily accessible across the platform. This standardized approach allows workflows to operate smoothly, as the necessary data can be quickly retrieved and processed based on its metadata. Whether sorting documents by entity, date, or document type, the universal metadata system ensures that all relevant information is accurately categorized and ready for automation.

1.2 High-Quality, Machine-Readable Data

A critical factor in workflow automation is the ability to work with data that is both high-quality and machine-readable. Orbit Insight’s robust data processing capabilities, including PDF pre-processing and metadata management, ensure that all data is in a format that can be easily interpreted by machines. This eliminates the need for manual intervention in data preparation, allowing workflows to run more efficiently and reducing the risk of errors.

1.3 Batch Calculation Platform

To handle large-scale data processing, Orbit Insight includes a batch calculation platform that supports extensive parallel processing. This platform is designed to run large-scale calculations efficiently, making it possible to automate complex workflows that involve significant data analysis. By executing calculations in parallel, Orbit Insight ensures that even the most data-intensive tasks are completed quickly and accurately.

2. Evolving from Manual to Automated Workflows

Historically, workflows, especially those related to investment research, were either performed manually or automated in very mechanical and rigid ways. These traditional methods were often time-consuming and limited in scope, requiring significant manual effort to maintain and adapt to changing needs.

2.1 The Shift from Manual Processes

Investment research and similar tasks have traditionally involved labor-intensive processes, where analysts manually sift through data, apply calculations, and compile results. This manual approach, while thorough, is inefficient and prone to human error, especially when dealing with large volumes of data.

2.2 Mechanical Automation Limitations

Early attempts at automation involved creating rigid, mechanical workflows that could handle repetitive tasks but lacked flexibility and intelligence. These systems were often difficult to set up and maintain, requiring specialized knowledge to create and modify workflows. Moreover, they struggled to adapt to more complex or nuanced tasks that required deeper analysis or contextual understanding.

2.3 Empowering Users with Intelligent LLMs

Orbit Insight revolutionizes workflow automation by integrating intelligent Large Language Models (LLMs) that allow users to set up their own logic and workflows with ease. These LLMs bring a new level of sophistication to automation, enabling the handling of more complex tasks that were previously impossible or impractical to automate.

• User-Defined Logic: With LLM-powered automation, users can define custom workflows tailored to their specific needs. This flexibility means that even highly specialized processes can be automated, reducing the manual effort required and allowing users to focus on more strategic tasks.

• Complex Workflow Automation: The integration of LLMs means that workflows can now handle more nuanced and complicated tasks, such as interpreting natural language queries, making contextual decisions, and dynamically adapting to new information. This capability allows for a more comprehensive and adaptive approach to automation, far beyond the capabilities of traditional methods.