Article
An Intelligent Search for the Procurement Office of the Federal Ministry of the Interior and Community
Nortal created an AI-based proof of concept to tackle operational challenges in the procurement process by offering solutions to specific pain points. The intelligent search, as part of the Nortal Tark solution, aims to answer sustainability-related queries and identify relevant documents in the procurement context. The primary goal is to showcase how AI can enhance decision-making in a political context, while promoting transparency in an eGovernment setting. The proof of concept was successfully delivered, meeting all customer requirements, and ensuring data sovereignty for the Procurement Office of the Federal Ministry of the Interior and Community.
Challenge
The digitalization of procurement poses several challenges for the German public sector, including complex processes, tracking sustainability in procurements, and a significant documentation burden. The Procurement Office of the Federal Ministry of the Interior and Community produces hundreds of service descriptions for procurements each year, but the data within is largely untapped. This makes it difficult to evaluate procurement sustainability and use past procurements for strategic political decisions. Furthermore, it is crucial for procurers to have quick and easy access to existing information and documents to streamline their workflows efficiently. Addressing these issues is vital for enhancing transparency and efficiency in German public procurement.
Solution
The solution uses artificial intelligence to improve the creation of service descriptions in public procurement. Users are supported by the proof of concept AI text assistant, which uses an internal dataset of service descriptions and Sustainability documents and guidelines.
The AI text assistant works in two stages. In the first stage, users ask questions and receive relevant text passages with a percentage indicating their similarity to the query. The second stage involves generating responses using the selected Large Language Model (LLM) to ensure accurate and contextual responses in natural language. The implemented solution “Nortal Tark” is generally LLM agnostic, meaning that any LLM can be used.
The user interface design prioritises accessibility, user needs and confidence in AI answers. By emphasising task simplicity, the design streamlines complex processes. The minimalist interface minimises distractions and promotes a calm user experience.
The solution is extensible to allow for flexible interactions, including multiple document formats and integration with platforms such as SharePoint or Confluence. It can operate in Q/A or chat mode, and the system highlights the exact snippet from which the answer is derived, ensuring transparency and accuracy.
A second feature of the solution was to provide statistical information on the sustainability of a procurement. Sustainability notes contain critical sustainability information, but their accessibility is currently a challenge. These notes, completed during the procurement process, offer significant potential for comprehensive reporting and data-driven decisions in sustainable procurement.
The sustainability statistics feature of Intelligent Search aims to make this data accessible and aggregated. The proof of concept provides initial analysis and visualisation for easy understanding and re-use.
Result
A reliable proof of concept was developed to meet the operational requirements of the Procurement Office, with a focus on data sovereignty. The AI-based solution utilises the power of a Large Language Model to address sustainability and efficiency issues in procurement. It provides optimisation and transparency by making relevant documents easily accessible, searchable, and reusable. The initiative aims to showcase the potential of AI in enhancing strategic decision-making in a policy context and promoting transparency in an eGovernment environment. Additionally, it aims to simplify the work of procurement officers by offering a practical and scalable solution.
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