Case Study: AI-Supported Analysis of Company Data
Features:
Large Datase
Virtual Assistants
Financial Data
This case study explores how company data can be collected on a large scale and systematically analyzed. It highlights the use of Large Language Models (LLMs) to search for specific information within this data. The goal is to identify interesting target companies for the client. The process includes the automatic collection of data from various sources, the cleaning and structuring of this data, and the application of LLMs to extract relevant patterns and details. Finally, the identified target companies are automatically contacted to optimize and accelerate the customer acquisition process.
Result
Unfortunately, we cannot freely provide data for this project. Please contact us if you would like a demonstration of this project.
Technical Setup
The system uses the Bundesanzeiger and the commercial register as main sources to identify and collect company data. An initial virtual assistant analyzes this data and stores it in a central database, while a second virtual assistant automatically contacts the identified target companies and provides all results in a second database for detailed evaluation.
Applications
The presented system, originally developed for the financial sector, offers versatile applications and can be applied to all kinds of specialized company data. It is capable of searching for and analyzing exactly the data the customer is looking for, making it suitable for various industries.
The core of the system is its flexibility and adaptability. It can be tailored to specific company data that conventional financial data services do not provide. This allows for the extraction of precisely the information the customer needs, enhancing operational efficiency across different industries. Whether financial data, legal information, or market research, the system is versatile and supports informed decision-making in a wide range of applications.