Organizational knowledge rarely resides in a single location. Relevant information is distributed across file systems, document management systems, intranets, project platforms, emails, and other business applications. Anyone trying to answer a specific question often needs more than one document. They must research, compare, and assess information from multiple sources. The challenge is not simply to find as many results as possible. The real task is to identify the information that is actually relevant within a large and heterogeneous body of content. At the same time, users need to understand which documents support a statement and whether important information may be missing.
INTERGATOR 6.8 addresses this workflow. The new version combines improved hybrid search with a more flexible Agentic Chat. The two functions complement each other: search identifies relevant documents and passages, while the chat structures the research process, brings results together, and presents them in an understandable way.
When a Search Request Does Not Fit into a Single Search Box
In everyday work, research often begins with incomplete information. A project manager may know the name of a customer but not remember the exact project title. A technical employee may be looking for a particular error description without knowing which terminology was used in the documentation. A specialist department may need to understand the current status of a case whose information is spread across minutes, presentations, reports, and correspondence.
Conventional search methods work particularly well when users know clear and distinctive terms. These may include reference numbers, product names, invoice numbers, abbreviations, or precise technical terms. The process becomes more difficult when users describe their information need in natural language or when the relevant documents use different wording.
Purely semantic search does not solve this problem completely either. While it recognizes similarities in meaning, it can be less precise for individual words, numbers, or highly specific names. In practice, short and precise search requests therefore place different demands on a search system than detailed questions.
Hybrid Search Combines Terms and Meaning
INTERGATOR 6.8 therefore combines lexical and semantic search methods. Lexical search considers specific terms and linguistic variants. Semantic search additionally evaluates the similarity in meaning between the request and the indexed information. INTERGATOR combines the results from both methods and prioritizes them according to relevance.
This supports different working styles without requiring users to select the appropriate technical method for every search. A search for a specific case number benefits from the lexical component. A question such as “Which technical risks were identified for the introduction of the new process?” can instead find semantically relevant documents and passages, even when the exact wording does not appear in the source material. This approach can also improve multilingual search requests. For example, a user may formulate a question in German and still find relevant English-language documents, provided that the embedding model and the respective configuration support this scenario.
In INTERGATOR AI+, a Large Language Model can also identify relevant terms and passages within a document. This makes it easier to understand why a result is relevant to the request. The displayed text still comes from the indexed document. The model helps select the relevant passages but does not generate the underlying document content.
From Search Result to Verifiable Answer
A good list of search results is often only the first step. For more complex questions, users need to open several documents, compare statements, and prepare the findings for other people. This is where Agentic Chat in INTERGATOR 6.8 comes into play. The connected language model first analyzes the user’s input. Depending on the request, the model can decide whether it can answer directly, whether it needs to use INTERGATOR search, or whether a clarifying question would be helpful.
This is particularly useful for unclear or ambiguous questions. For example, when an employee asks for the “current status of the project,” the project name, relevant period, or desired perspective may be missing. Instead of immediately starting a potentially unsuitable search, the chat can ask for the missing context. For a clearly formulated specialist question, the model can use search, retrieve relevant information, and generate an answer based on the results. Depending on the model used, it may also perform several searches in sequence and compare the findings. Reasoning models support such multi-step tasks, although they usually require more time and computing resources than simpler models.
A Realistic Research Process
An employee in the maritime sector is preparing for a technical meeting on hydrogen technology. The employee asks:
“What challenges does deep-sea shipping face in relation to hydrogen technology?”
Agentic Chat can first recognize that organizational information is required to answer the question. It uses INTERGATOR search to find technical studies, project documents, safety concepts, regulatory documents, or market analyses. Depending on the model and the question, it can formulate different search requests, compare the results, and create a structured overview.
The response may summarize key challenges such as hydrogen storage and refueling, the necessary port infrastructure, safety requirements, regulatory provisions, limited range, high costs, and the additional space required on board. Sources used in the response can be linked directly to individual statements. An additional document list can show which materials were particularly relevant. Users can then open these documents in the preview and verify the statements against the original content. The chat does not replace specialist assessment. It does, however, shorten the path from an open question to a verifiable basis for further work. The employee does not need to read every document in full but can focus on the most important passages.
Organizational Knowledge Remains at the Center
Agentic Chat can also respond without performing a search. This may be useful for a greeting, a translation, or a general writing task. General model knowledge can supplement a response when no research within the organization’s information base is required. Nevertheless, the central value of INTERGATOR remains access to connected and permission-controlled organizational information. The chat is designed to use INTERGATOR search as a tool whenever the information need requires it. Responses with source references show which statements are based on the available data. When references are missing, users should examine more carefully whether the response primarily comes from general model knowledge.
Chat scopes also remain available. They can limit the chat to selected documents, favorites, saved searches, or other defined document sets. Unlike before, however, users no longer need to select such a scope before every conversation.
Agentic Chat Is Not an Autonomous AI Agent
The term Agentic Chat describes a more flexible way of processing requests. It does not mean that INTERGATOR independently executes arbitrary processes or acts in third-party systems without control. In INTERGATOR 6.8, the chat essentially has access to INTERGATOR search as its tool. The model can formulate search requests, use search several times where appropriate, and process the returned results. It cannot automatically execute arbitrary business actions, modify records, or access external applications without restriction.
Advanced capabilities such as independently evaluating facets, querying arbitrary metadata, or triggering project-specific actions are not generally included in the product’s standard functionality. Such tools should be regarded as possible future extensions unless they have been explicitly implemented in a specific project. This distinction is important for a realistic assessment. INTERGATOR supports research, analysis, preparation, and contextualization. Responsibility for decisions and specialist evaluation remains with people.
What Business Value Does the Combination Provide?
Hybrid search and Agentic Chat address different parts of the same problem. Search improves access to relevant information. The chat helps turn that information into an understandable and verifiable answer. Specialist departments spend less time systematically varying search terms and manually comparing numerous documents. Project teams can establish a shared understanding more quickly. Sales and customer service teams gain a structured basis for conversations. Technical departments can investigate error patterns, requirements, or possible solutions across multiple documents.
For CIOs, CTOs, and digital transformation leaders, the main benefit lies in the controlled integration of existing information assets. INTERGATOR accesses connected data sources while respecting existing permissions. However, the quality of the results still depends on the underlying data, indexing, the models in use, and the specific configuration. Model selection also influences the outcome. A powerful reasoning model can process complex research tasks in several steps but requires more time, computing power, and potentially higher usage costs. Faster models may be more suitable for simpler tasks. INTERGATOR 6.8 therefore makes it possible to use different models for different types of work.
Risks and Limitations Still Matter
An AI-generated answer may be incomplete or incorrectly interpreted. Source references improve transparency, but they do not automatically guarantee that the interpretation is technically correct. For legal, technical, or commercially significant decisions, users must verify the underlying documents.
Even the best research process can only access information that has been connected, indexed, and made available to the respective user. Outdated documents, contradictory sources, or missing information remain organizational challenges. INTERGATOR can make such content easier to find, but it cannot automatically determine which source is authoritative. Organizations should therefore define specific use cases, select relevant data sources, and establish quality criteria before introducing these functions. Suitable models, clear permission concepts, and realistic expectations regarding the role of AI are equally important.
Conclusion: Research Becomes More Flexible, Not Arbitrary
INTERGATOR 6.8 combines precise search methods with a more flexible, dialogue-based research process. Hybrid search supports short terms as well as detailed and multilingual requests. Agentic Chat can assess the information need, ask clarifying questions where appropriate, use search, and combine retrieved information into a transparent answer.
The key advance is not supposedly autonomous AI. It is the better coordination of search, language models, organizational knowledge, and verifiable sources. When configured appropriately, INTERGATOR helps users search large information repositories more systematically, understand connections more quickly, and use findings as a more reliable basis for further work.