While conducting strategic business analyses, the need arose to automate recurring evaluation steps, particularly the creation of structured analyses based on extensive documents.
These analyses require both an understanding of the content and the structured derivation of strategic recommendations—tasks that have been performed entirely manually up to now. The goal was to develop a solution that
The solution also needed to be as flexible as possible: it had to be modular, cost-effective to operate and compatible with existing systems.
Based on typical documents, analysis processes were identified, GPT models were adapted, and a cloud-based solution was developed that automatically generates structured analyses and reports.
At the start of the project, we worked with the client to define the requirements for automated business analysis. The goal was to efficiently derive structured analyses from extensive documents—taking into account relevant contextual information and typical evaluation dimensions. This well-defined use case scope laid the foundation for targeted development.
To support various analysis formats, the GPT model was trained using real-world analysis examples. This made it possible to extract relevant information from text-based documents and present it in a structured format. In addition, a high-performance model was provided for efficient use in day-to-day business operations.
The solution was container-based and integrated into the customer's Azure environment. Document uploads are possible both via a web interface and via API. Data processing takes place via a lean FastAPI backend. Thanks to the modular architecture, the system can be flexibly expanded - e.g. to include additional analysis types or data sources.
Based on the analyzed content, PDF reports are automatically generated that can take various forms—ranging from structured evaluations to strategic recommendations. The reports are ready for immediate use and are suitable for internal decision-making processes or project reviews.
Following successful testing with real documents, the solution was thoroughly documented and deployed. Thanks to its modular architecture, the application remains flexible and scalable—whether through additional analysis formats, extra data sources, or new features.
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