AI knowledge database for fire protection
Category
Digitalization & Automation
Project Tags
AI, automation, process digitization
Year
2026
Type of work
Concept development, AI development, Google Workspace integration
Type of work
AI knowledge database for fire protection
Fire protection companies work with a multitude of standards, TRVB guidelines, and testing guidelines, which often reference each other. A single question, such as regarding escape route width, can require searching through hundreds of pages of documents. The knowledge of where to find specific information often rests with a few experienced employees – this leads to lost time during vacations or staff turnover, and delays in inspection reports.
DigitalArk developed an AI-powered knowledge database to address this, consolidating over 250 PDF standards, TRVB guidelines, and testing guidelines into a single, searchable knowledge resource. Employees submit their questions in simple German, and the AI provides the appropriate answer, including precise source information, directly within their familiar Google Workspace.
Project
Fire protection companies work with a multitude of standards, TRVB guidelines, and testing guidelines, which often reference each other. Finding the right resource for a specific question often means sifting through hundreds of pages of documents – time that is then lacking in daily operations. Furthermore, this knowledge often depends on individual experienced employees, leading to delays during vacations or staff turnover.
DigitalArk developed an AI-powered knowledge database to address this, consolidating over 250 PDF standards, TRVB guidelines, and testing guidelines into a single, searchable resource. Employees submit their questions in simple German, and the AI provides an understandable answer with precise source information – directly within the familiar Google Workspace.
The solution was designed as a seamless research process within Google Workspace, requiring no additional software or system changes. An employee's question is simultaneously checked by AI against all stored standards, TRVB guidelines, and testing guidelines. Cross-references between individual documents are also automatically taken into account, eliminating the need to manually search for the relevant standard and TRVB guideline.
The appropriate answer is summarized clearly and linked to the precise source – standard, section, point – ensuring that every answer remains verifiable. A key element of the implementation was safeguarding against fabricated values: If information is missing from the stored sources, this is clearly indicated instead of being guessed. Regular updates with new standard versions ensure the information remains up-to-date.
Challenges & Solutions
Analysis & Concept:
The central challenge was to create a reliable source of knowledge without compromising accuracy and traceability – an aspect that is particularly critical in the field of fire protection. DigitalArk therefore focused on comprehensive source citations rather than generic AI answers.
Implementation & Results:
The result is a knowledge database that significantly reduces search time across multiple referencing documents. Answers are provided directly with source information, can be verified in the original source, and can be immediately imported into Google Docs or audit reports. New topics, locations, or user groups can be added at any time.
