“Sustain.AI” – The AI tool for analyzing sustainability reports

“Sustain.AI” – The AI tool for analyzing sustainability reports

Where does the AI application offer the greatest benefit?

Sustainability reports are an important part of a company’s information policy. They provide interested members of the public with information about the organization’s activities and performance regarding sustainable development. Since 2017, all listed companies with 500 or more employees have been required to publish such reports. In doing so, they follow the CSR (Corporate Social Responsibility) directive. The main aim of this directive is to increase transparency regarding the environmental and social aspects of companies in the EU. This includes information on environmental, social and employee issues as well as the protection of human rights and the fight against corruption.

Like annual reports, sustainability reports are also used as a basis for important purchasing or investment decisions. However, the necessary identification of all relevant criteria and information usually requires a great deal of time and effort. With the tightening of the CSR Directive implemented by the EU in 2023, which will extend reporting requirements to other aspects and a larger group of companies, this work becomes even more complex – especially if reports are evaluated manually.

The AI-based tool Sustain.AI makes this work easier. Thanks to machine text recognition, sustainability reports can now be analyzed in a highly efficient and structured way. The technology behind Sustain.AI is particularly aimed at auditors and controllers who can use the tool in their day-to-day work.

What are “Sustain.AI’s” quality features?

  • Time saving: The KI.NRW demonstrator allows for quick and efficient handling of sustainability reports and analysis of the required CSR criteria. With the help of AI language models, the text passages relevant to the respective criteria are filtered out. In this way, auditors can focus on the sections that are most relevant to the respective criterion.
  • Overview and context: With the integrated PDF viewer, the extracted text elements can be displayed in the report at any time. This allows users to immediately grasp the context of the passage.  
  • Integrated feedback and adaptability: Users can evaluate the system’s suggestions using the built-in feedback system. This helps us to further train the AI model and continuously improve its quality. It is also capable of learning new criteria.
“Sustainability is increasingly becoming the focus of public attention. With the AI-supported tool Sustain.AI, it is possible to efficiently analyze and browse sustainability reports.”
Maren Pielka
Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS

Which AI technology is used in the KI.NRW demonstrator?

Language models

Modern language models are neural networks which are trained to predict a word by the context in which it appears. In this way, they learn an accurate representation for all words in the text and is capable of semantically comparing and classifying text passages.

Image recognition

PDF is a widely used unstructured file format. This means that although headings and tables are visually recognizable to readers, there is no internal structure. To be able to work with PDFs automatically, intelligent image processing algorithms are required to extract this structure from the images and correctly classify objects such as tables and paragraphs.

What does the AI demonstrator show?

The AI-supported suggestion system is an intelligent, intuitive search engine. You can upload your own documents as well as view existing reports. The reports can be searched and analyzed using a stored checklist from the “Global Reporting Initiative”, a widely used reporting framework for sustainability reporting.

Request a non-binding consultation with our experts now!

Curious? Click here to go straight to the demonstrator!

Where can I find more information?

AI.map with entries around data analysis and forecasting

More AI providers, applications, and AI products “made in NRW” with the same AI focus can be found using the filter and search function of the AI.Map, which currently contains more than 1000 entries.

Study “Modern language technologies”

Find out where we encounter modern language technologies in everyday life and at work and what economic opportunities they offer.

Fraunhofer IAIS: Media Engineering

You can learn more about the topics of “Cognitive Business Optimization”, “Smart Coding and Learning”, and AI-based industrial image processing on the website of the IAIS Media Engineering department.

Contact the team of developers

Maren Pielka

Data Scientist and Team Leader Cognitive Text Analytics,
Business Area Cognitive Business Optimization,
Media Engineering Department

Fraunhofer IAIS
Schloss Birlinghoven
53757 Sankt Augustin

Phone +49 2241 14-2871

Send email

Lars Patrick Hillebrand

PhD Student / Research Assistant in Machine Learning,
Media Engineering Department

Fraunhofer IAIS
Schloss Birlinghoven
53757 Sankt Augustin

Phone +49 2241 14-1920

Send email

Image recognition and understanding

Intelligent document analysis “recognAIze”

With the intelligent document analysis of recognAIze, data from documents can be recognized and evaluated automatically.

Where does the AI application offer the greatest benefit?

The manual review of receipts, invoices and other documents, their digital capture and provision is associated with a high expenditure of time and money in many companies as well as administrative institutions. The solution is provided by intelligent document analysis systems based on optical character recognition (OCR), which, like “recognAIze”, enable fast, simple and automated analysis as well as blind processing of all types of documents. Thanks to artificial intelligence, documents are automatically captured, read, assigned and further processed. Damaged originals, low-quality scans of documents and, in particular, confidential documents are processed without further human intervention and according to high data protection standards.

What are the quality indicators in these types of AI applications?

  • The basis of document analysis is the input data that needs to be analyzed. Since the documents are usually captured in varying image quality, automated image enhancement is very important in the AI application.
  • AI-based optical character recognition (OCR) using artificial neural networks ensures that not only individual text characters are recognized and processed, but also text passages and the structure of a document (e.g. headers or footnotes).
  • Through layout analysis, the AI application can also identify tables within a document and interpret the contents to process invoices automatically in accounting, for example.
  • Particularly in the case of sensitive information, the AI used must be secure and all data must be processed in a DSGVO-compliant manner on German servers or on-premise at the customer’s premises.
  • In the future, handwriting recognition (ICR) will also play a role in the applications in order to open up additional fields of application and achieve a complete transfer of content.

What AI technology powers demonstrator of KI.NRW?

Deep Learning OCR

Optical Character Recognition (OCR) combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), two current approaches in the field of Artificial Intelligence, to develop characters from pixels. It extracts the text from the images and generates a structured XML file for each document with position data of the recognized words and page ranges.

Image enhancement

For the best possible quality of the results, negative influencing factors such as insufficient exposure of the scanned document or curvature or distortion in the image must be equalized. The image enhancement algorithms perform grayscale conversion and binarization for this purpose. In addition, procedures are used to remove curvature and other disturbing factors.

Layout recognition

The layout recognition identifies the structure of text and helps to divide the recognized characters into columns, text sections or headings and to determine a reading order. In this way, table structures can also be recognized and output again as such, e.g. as a csv file. The output format is provided with appropriate metadata.

“Thanks to the methods used for image enhancement, layout and
character recognition, even poor quality documents can be evaluated.”
Dr. Nicolas Flores-Herr
Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS

What does the AI demonstrator show?

The KI.NRW demonstrator “recognAIze” brings AI-supported document analysis to life. Via the application, your own photographed or scanned documents can be uploaded to the system, where they are available for testing the intelligent document analysis. The step by step animations guide you through the AI technologies used in the demonstrator.

Optimize documents

Often, photographed or scanned documents have a fluctuating image quality, and are sometimes bumpy, torn or dirty. Image enhancement processes ensure that even old or damaged documents can be processed. The “recognAIze” demonstrator vividly guides you through the range of optimization options that are essential for high-quality document analysis.

Recognize characters and structures

The accuracy and speed of the OCR engine for intelligent character recognition from “recognAIze” is higher than that of leading market players. Without templates and manual post-processing, the demonstrator recognizes document layouts, e.g. sender information or dates. Even complex text content such as text-around-image elements are reliably recognized by the application.

Understand tables

Tables pose a particular challenge because they can be structured differently from document to document. AI methods are responsible for subdividing table contents according to information types and interpreting the segments individually.

Classify content

The demonstrator “recognAIze” determines the properties of the document, evaluates the individual elements and thereby enables a whole range of subsequent processing. For example, the intelligent classification makes blind processing of confidential documents possible in the first place. This means that information can be aggregated or used pseudonymously without a human being having access to the documents. In this way, sensitive, personal data can be protected better.

Create interfaces

AI-supported document analysis is often at the beginning of a process chain, whether in accounting or in archives. To enable further processing steps, the KI.NRW demonstrator offers various output formats such as XML or PDF.

Are you curious?
Click here to go to the demonstrator!

Where is more information to be found?

Study by KI.NRW

Learn about where we encounter modern language technologies in our everyday and professional lives and the economic opportunities (only available in German language).

AI products “Made in NRW”

Filter our AI map by “language and text comprehension”.

AI provider from NRW

Our AI map illustrates who offers AI methods related to image recognition in their portfolio.

Contact us

Dr. Nicolas Flores-Herr

Business Unit Manager Document Analytics

Fraunhofer IAIS
Schloss Birlinghoven
53757 Sankt Augustin

Phone +49 2241 142532

Email schreiben

Dr. Iuliu Konya

Senior Research Engineer

Fraunhofer IAIS
Schloss Birlinghoven
53757 Sankt Augustin

Phone +49 2241 142543

Email schreiben


Marius Nißlmüller

Student assistant Business Development

Fraunhofer IAIS
Schloss Birlinghoven
53757 Sankt Augustin

Email schreiben

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