Using AI securely in public administration does not mean uploading as many documents as possible to a general-purpose chatbot. What matters is which information is included, who is permitted to access it, how results are produced, and whether the underlying sources can be verified.
This creates a specific challenge for CIOs, IT managers, digital transformation officers, and specialist departments: they need to make existing administrative knowledge more accessible without compromising permissions, professional responsibility, or traceability. Neither a conventional keyword search alone nor an AI chat disconnected from internal information systems is sufficient. A controlled approach therefore combines several steps: finding relevant information, narrowing down result sets, reviewing original documents, analysing content, and preparing results for the respective administrative process. INTERGATOR supports this workflow using connected and approved data sources.
Why Administrative Knowledge Remains Difficult to Access
Public authorities usually do not lack knowledge. The problem is that this knowledge is often distributed across different systems and repositories. Administrative instructions may be stored on the intranet, templates in a document management system, older coordination records in emails, and supplementary materials in file repositories or specialist applications. Even a simple question can therefore require several research steps. An employee may need to determine which requirements apply to a particular administrative process. They first search for a policy, open several versions, compare modification dates, and then look for additional information in meeting minutes or guidance documents.
Traditional keyword searches are useful when the filename, reference number, or technical term is known. For more complex questions, however, exact terms may not be enough. Relevant documents may use different wording, be distributed across multiple sources, or contain only part of the required information. A generic AI chat does not automatically solve this problem either. Without controlled access to internal information, it may lack the necessary factual basis. Manually uploading documents also raises further questions: Is the selection complete? Is the file current? Is its processing permitted? And can users later identify which source supports a particular statement?
How Controlled Research Across Multiple Data Sources Works
Controlled knowledge use begins with clearly defined data sources. INTERGATOR can make information from connected systems such as file repositories, intranets, document management systems, email systems, and other specialist applications searchable through a central interface. The original documents remain in their respective source systems. Relevant content and metadata are prepared in a search index. This can include the title, filename, author, storage location, document type, and creation or modification date.
The research process can then proceed step by step:
- A user enters a known term, reference number, or professional question.
- The search identifies relevant documents from connected and approved sources.
- Facets and filters narrow the results by data source, period, document type, author, or other criteria.
- Relevant documents are reviewed in the preview or opened in the original application.
- Depending on the edition and configuration, chat functions or AI tasks assist with analysis and preparation.
This workflow keeps information retrieval connected to source verification. Users can move from a summarised statement back to the relevant document and review its professional context.
The Roles of Lexical, Semantic, and Hybrid Search
Lexical and semantic search address different research requirements. Lexical search focuses on specific terms and direct word matches. It is particularly suitable for reference numbers, names, identifiers, abbreviations, and known phrases. Semantic search places greater emphasis on the meaning of a query. It can therefore identify documents that describe the same topic using different terminology. This is useful when users formulate their information need as a natural-language question or do not know the exact term used within the authority.
In the relevant INTERGATOR editions, hybrid search combines both approaches. It evaluates direct word matches together with semantic similarity. A query can therefore include precise identifiers as well as a more detailed description of the information required. Hybrid search does not eliminate the need for professional filtering. In large information repositories, perspectives, facets, and filters remain important. Users who require complete result sets, exact counts, or specific metadata should continue to use the standard search interface.
Why Permissions Form the Basis of AI Use
Controlled knowledge access requires existing access rights to remain effective during both search and AI-supported processing. Users may only find and open content for which they have the necessary permissions. This also applies when two people submit the same question. Different access rights can produce different search results and therefore different information bases. Sharing a link does not extend the recipient’s permissions.
This separation is essential for public-sector organisations. Not every document should be available across the entire organisation. Personnel files, internal coordination records, confidential cases, and department-specific information require differentiated access controls. The available sources, perspectives, and functions depend on the installation, edition, and configuration. An implementation should therefore consider not only the language model, but also data sources, permission concepts, update cycles, and professional responsibilities.
How Agentic Chat Supports Research Without Making Autonomous Decisions
Agentic Chat in INTERGATOR AI+ supports natural-language and, where applicable, multi-step research. It analyses a request and can respond directly, use the INTERGATOR search, or ask a clarifying question if the request is ambiguous. For more complex tasks, a suitable language model can formulate several search queries, compare results, and combine information from multiple documents. In INTERGATOR 6.8, the chat primarily uses the INTERGATOR search as its tool.
This distinguishes the approach from an autonomous AI agent that independently operates specialist applications, modifies processes, or makes decisions. The chat researches and prepares information. However, it cannot generally evaluate complete data sets, operate arbitrary facets, or perform actions in connected systems. Optional chat scopes can further limit the permitted research area. Depending on the configuration, the chat can be restricted to selected documents, lists, favourites, data sources, or other defined document collections.
How Source References Support Professional Review
An AI-generated answer becomes useful in administrative processes only when its basis can be reviewed. When the chat uses information from the INTERGATOR search, statements can be linked to inline references, while the documents used can also be listed below the answer. Users can open the relevant source, review the cited passage, and continue working with the original document. This connection between an answer and its source improves traceability, but it does not guarantee that the answer is correct.
Answers without references may be based fully or partly on the language model’s general knowledge. Even when sources are provided, not every sentence must be taken directly from a document. Source references therefore support verification but do not replace it.
How AI Tasks Support Repetitive Document Work
Not every information-related task requires an ongoing chat. Employees often need to review several documents against the same criterion or prepare one document in a structured format. AI tasks can apply predefined or individually formulated instructions to visible search results or to an open document. Possible tasks include summaries, identification of key topics, structured extraction of facts, or an individual question applied to each document. In a result list, the task processes each document separately. It does not automatically create an overall analysis of all results. In the document preview, the processing applies only to the currently open document. Frequently used tasks and custom instructions can be pinned and reused.
This supports recurring review and analysis processes. A specialist department could, for example, review several retrieved documents individually to determine whether they contain information about a specific deadline, responsibility, or requirement. The results then serve as a basis for further professional processing.
The Business Value of a Controlled Approach
The main benefit is not simply faster text generation. What matters is a more consistent information process. Employees begin their research from a common entry point instead of searching several systems one after another. Relevant documents can be narrowed down, opened, and compared more efficiently. Repetitive evaluations can use the same instructions, while source references make results easier to review and share.
This can reduce manual research effort and improve the reuse of existing administrative knowledge. It can also reduce dependence on individual knowledge holders who previously knew specific storage locations, document titles, or historical connections. Collaboration between IT and specialist departments also becomes more concrete. Instead of discussing “AI in public administration” in abstract terms, both sides can assess a clearly defined use case: Which data sources are required? Which permissions apply? How many steps can be supported?
The Limitations of AI-Supported Knowledge Use
The quality of research depends on the available information base. Documents that are not connected, are outdated, contradict one another, or have incorrect permissions can create gaps. Newly created or modified content may not become available until the next update of the search index. Language models can also shorten information, misjudge relationships, or generate incorrect statements. The quality of the output depends on factors including the task definition, the selected model, the configuration, and the document content.
Multi-step research may require additional computing resources and incur higher costs. Functions also differ by edition, project, and operating environment. Every implementation should therefore consider technical, organisational, and professional requirements together. Professional responsibility remains with people. INTERGATOR supports research, analysis, classification, and preparation. Approvals, legal assessments, administrative decisions, and binding statements must still be made by the responsible personnel.
Controlled AI Use Begins with a Clearly Defined Use Case
Using AI securely in public administration requires more than a capable language model. Organisations need approved data sources, a robust permission concept, traceable research paths, and binding rules for professional review. INTERGATOR combines organisation-wide search and structured document work with hybrid search, Agentic Chat, and AI tasks, depending on the edition and configuration. This approach helps employees find, evaluate, and prepare existing administrative knowledge for specific tasks.
The most sensible starting point is a clearly defined use case with known data sources, specified user groups, and verifiable results. On this basis, an organisation can assess the actual contribution AI can make to administrative work and identify which technical or organisational prerequisites still need to be established.
Would you like to assess a specific use case? Talk to us about the data sources, research processes, and professional requirements that should be considered.