{"id":9406,"date":"2026-04-15T12:26:12","date_gmt":"2026-04-15T10:26:12","guid":{"rendered":"https:\/\/brain4industry.cz\/?post_type=aktuality&p=9406"},"modified":"2026-04-15T12:38:17","modified_gmt":"2026-04-15T10:38:17","slug":"ai-assistant-for-company-data","status":"publish","type":"aktuality","link":"https:\/\/brain4industry.cz\/en\/novinky\/ai-assistant-for-company-data\/","title":{"rendered":"How AI Is Turning a Project Archive into an Active Source of Know-How"},"content":{"rendered":"\n

Historical project data as an untapped source of company knowledge<\/strong><\/h2>\n\n\n\n

Many companies possess years of valuable project data but find it difficult to use it effectively. Historical documents, technical materials, and project documentation are often stored in archives without efficient search capabilities or systematic use in new projects. In the era of digitalization and artificial intelligence, however, this data can represent a significant source of company know-how.<\/p>\n\n\n\n

The architectural studio MS Architekti faced exactly this situation. Over more than twenty years of operation, the firm had built an extensive archive of projects stored across DVDs, file servers, and other internal repositories. The data existed in a wide range of formats, with inconsistent folder structures and no systematic management. Although the archive contained valuable company knowledge, searching for relevant information was time-consuming and historical experience was only partially used when designing new projects.<\/p>\n\n\n\n

AI solution for working with unstructured data and project documentation<\/strong><\/h2>\n\n\n\n

The Brain4Industry team therefore designed and implemented a solution based on LLM technology that enables efficient work with large volumes of unstructured data. The first step was the development of an AI assistant<\/a> capable of extracting information from historical documents and storing it in a local vector database. The system was later extended with an AI agent that can work with internal templates and automatically generate documents based on stored data.<\/p>\n\n\n\n

The development process also included optimization of the data pipeline, implementation of models to improve search accuracy, and continuous learning based on user feedback. As a result, the AI system gradually adapts to the terminology and specific needs of the architectural practice.<\/p>\n\n\n\n

The project is ongoing, with the system continuously evolving \u2013 currently undergoing further refinement, feature expansion, and optimization based on real-world usage.<\/p>\n\n\n\n

Benefits of AI for working with project data<\/strong><\/h2>\n\n\n\n

The solution has enabled the company to quickly access information from past projects and significantly improve the efficiency of preparing new documentation. The project archive is gradually transforming into an active knowledge system that supports architects in their everyday work.<\/p>\n\n\n\n

Solutions of this type can help companies across industries make better use of their existing data and transform it into practical tools for more efficient workflows, innovation, and faster decision-making.<\/p>\n\n\n\n

Brain4Industry helps companies turn data into practical AI solutions<\/a> that increase efficiency, support innovation, and deliver real business value.<\/strong><\/p>\n\n\n\n

\"\"<\/figure>\n","protected":false},"excerpt":{"rendered":"

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