- Hard work is for AI – Automatic extraction of data from e-mails, messages, and documents_

Masses of unstructured data

With the expansion of digitalization, the amount data organizations store has increased significantly. Companies and government agencies have collected enormous amounts of data over the past few decades, often in unstructured formats such as emails or documents. Using this data efficiently requires converting it into a structured format. Human data entry is expensive and inefficient, especially with substantial amounts of data. Modern artificial intelligence (AI) models trained using machine learning (LLM) are revolutionizing this process and making automatic data extraction economical and practical.

In particular, the easy availability of user-friendly and powerful language models such as ChatGPT, Gemini or Mistral significantly reduces the time-to-market for innovative solutions. In the past, models with copious amounts of data had to be trained at great expense – today reliable results can be rapidly achieved with a fraction of the data.

Practical examples

cronn's AI experts have already developed solutions for many use cases to extract target data from a wide variety of data sources. The corresponding work processes were previously partially manual or were not economically feasible. With the latest machine learning models, our team has developed fully automated processes and integrated them into existing systems. These solutions can quickly bring a return on investment.

• Order entry from e-mails: We have built a solution for the logistics platform Orbit which uses a large language model to extract order data from several thousand e-mails per month and then enter it into the system. This allows for fewer staff or allows existing staff to focus on other tasks. To the customer testimonial.
• Our solution for North Data relies on a machine learning pipeline to extract metrics (revenue, total assets, number of employees) from millions of annual reports and transfer them to North Data's system. The solution is scalable through the use of an LLM and can be transferred to different languages. To the customer testimonial.
• Quick answers to compliance questions: We built a data protection-compliant chatbot for Toll Collect GmbH that answers employee questions about compliance issues via email. This reduces the workload for employees in the department, especially when it comes to frequently asked questions. To achieve this, an ML model was trained using knowledge from internal compliance documents, which continues to learn through communication with specialist staff. To the customer testimonial.

What are the benefits of automatic data extraction?

Automatic data extraction with AI from emails, messages, and documents offers several benefits:

• Time and cost savings
  Automated data extraction can significantly reduce the time and cost of manual data analysis and collection. Many processes are only made possible by automation.
• Improved accuracy
  Human data analysis is prone to errors, especially in routine, monotonous tasks. AI-powered data extraction can reduce errors and improve the accuracy of extracted information.
• Real-time analysis
  AI systems can analyze substantial amounts of text in real time, which can be especially useful in dynamic business areas where fast decision-making is important.
• Scalability
  AI systems can easily scale larger amounts of data. While manual data extraction is impractical for large amounts of data, an AI system tends to work better as the amount of data increases.
• Compliance and risk management
  Automatically extracting and analyzing data from corporate documents to monitor regulatory compliance and better manage risk.

What are the challenges of automatic data extraction with the help of AI?

Despite these advantages, there are also challenges to consider when implementing such systems:

• Evaluation of suitable technologies
• Preparation of data to be processed
• Constant review of extracted data
• Continuous monitoring and adaptation of the system to ensure functionality

cronn attends to AI projects throughout their entire life cycle and has already successfully tackled these challenges several times. Upon client request we can also further support projects with monitoring, maintenance, and further development. Through these so-called MLOps we ensure the quality and cost-effectiveness of our solutions.

Data security and protection are also important to us as part of our corporate DNA. We have the highest security standards and use concepts such as Zero Trust in our projects.

How can cronn help companies with automatic data extraction?

cronn has experience from hundreds of software development projects and is available to customers with advice and support. To enable automated data extraction, cronn proceeds as follows:

Our AI experts:

1. advise you on use, benefits, and costs
2. analyze your processes
3. conduct a workshop with you according to the AI Design Sprint™ method (upon request)
4. prepare your source data and create a reference data set (ground truth)
5. create a customized service that receives messages or documents and extracts the desired information
6. enable integration into your IT landscape and existing software – whether in the cloud or on-premises
7. take over the maintenance of the services to ensure long-term quality and security (MLOps)

You can find more information on cronn.ai, our website for our artificial intelligence offering.

Result

AI-powered data extraction from emails, messages, and documents saves time and unburdens your employees. It opens new analysis possibilities that were difficult to access with conventional methods. The advanced approach to text analytics can be useful in many cases, but it presents challenges in terms of technology selection, security, and quality assurance.

Our AI team at cronn can partner with you to consult, care for, and maintain – turning our experience from numerous successful projects and your business case into your perfect AI service.