Public grants are an important instrument for financing socio-economic development, innovation, and public policy objectives. However, identifying and comparing key information on available grants remains difficult due to the fragmented, heterogeneous, and largely unstructured manner in which grant information is published by entities administrating grants. Recent advances in LLMs enable new approaches to processing unstructured textual data and generating standardized representations that summarize and enhance key information and thus facilitate comparison. Nevertheless, their direct application in domains requiring high informational reliability is constrained by issues such as hallucination and limited reproducibility. This paper examines the practical applicability and limitations of LLMs for standardizing grant information originating from heterogeneous, human-created sources. We propose a hybrid algorithmic architecture that bifurcates the standardization process into two complementary pathways. Descriptive textual fields are generated using a tightly controlled, parameter-constrained LLM pipeline, while hard factual data are extracted using predetermined, rule-based methods. This design restricts the scope of generative models to mitigate hallucination risks while preserving flexibility in processing unstructured content. The proposed solution was implemented as a web-based system and successfully evaluated under real-world conditions. The results demonstrate that a constrained and selective application of LLMs can support effective standardization of heterogeneous information while mitigating the risks associated with hallucination and inconsistency. The proposed approach is not limited to the grant domain but is of a general nature and can be transferred to other business areas characterized by fragmented, unstructured, and inconsistently published information.