When corporate knowledge bases expand into massive archives, even a powerful AI assistant like AI-UniBot Personal Assistant & Corporate Chatbot can experience slower response times. This delay becomes critical, especially when quick access to information impacts decision-making or communication with Clients.

Such a noticeable increase in query processing times when working with massive amounts of unstructured data was perceived by our Clients as a notable limitation. So we implemented a mechanism for semantic index clustering, which fundamentally changes the approach to AI-powered search.

The core of this innovation is the structural division of the index into logical groups – semantic clusters. Now, instead of scanning the entire dataset every time a User makes a query, AI-UniBot first identifies which cluster the query topic belongs to. It then searches exclusively within that designated group. This is akin to finding a book on a clearly labeled shelf in a specific section of a library, no matter how vast its holdings are. This approach not only accelerates search operations but also improves the relevance of the results, as searches occur in a narrowly focused thematic context.

In practice, this unfolds as follows:

• Pharmaceutical Industry

Imagine a Sales Representative of a large Pharmaceutical Company during a meeting with Medical Doctors. The question was asked about the specific features of the new drug's action mechanism. Previously, searching for an answer in a database with thousands of documents (such as clinical trials, instructions, and registration materials) could take too much time, risking the loss of a valuable opportunity. After the implementation of clustering in AI-UniBot, all information was divided into 20 thematic clusters corresponding to classes of drugs (like anesthetics, antibacterial agents, antifungal agents, agents for the treatment of protozoal infections, etc.).

Now, when the Representative requests data on a specific agent for symptomatic pharmacotherapy in palliative care, AI-UniBot instantly identifies the Anesthetics cluster and searches exclusively through it. This ensures that an accurate answer is received in a matter of seconds, right during the dialogue, increasing the efficiency of the Representatives' work and strengthening the trust of the Clients.

• Industrial Holding

A large Industrial Holding, formed through numerous mergers and acquisitions, faced a critical issue: its knowledge was scattered across dozens of old file servers, SharePoint sites from different periods, and cloud storages. Without a single logical structure. Searching for technical documentation, standards, or instructions was like looking for a needle in a haystack: it took hours. The introduction of clustering in AI-UniBot based on organizational structure (i.e., Business Units, Metallurgy, Mechanical Engineering, Energy, Logistics, etc.) led to the creation of about 60 clusters. Now, even with a huge total volume of data, the search is limited to documents of only the relevant unit. This allowed us to reduce the average search time by 3-4 times, significantly speeding up the execution of production tasks and access to critical information.

• Regulation of complex processes

A Company with well-structured, but very similar business processes described them in the internal documentation. In it, knowledge is segmented by sections, for example, the Financial Sector or IT Outsourcing. However, this Company still faced a big issue: all the documents there were created according to the same templates of the Consulting Firm. Therefore, they contained numerous identical or very similar terms and phrases. This led to the fact that a regular semantic search for a specific question gave a lot of results from different processes, among which it was difficult to find the precise regulation needed. 

The distribution of all this documentation on AI-UniBot by business process classes (for example, Customer Service, Incident Management, Change Management, Financial Reports, etc.) formed about 15 clusters. And the issue was fixed. Now, the search for a query regarding, say, the incident escalation procedure takes place exclusively in the Incident Management cluster. This guarantees high accuracy, and, at the same time, the absence of noise results associated with other processes.

Technical implementation:

The whole process begins with the setup stage, when the AI-UniBot System Administrator defines and sets a list of semantic clusters that correspond to the Organization’s business logic. These clusters can be based on the structure of the Units, document types, products, processes, or any other significant category. When indexing newly created or long-existing documents, AI-UniBot analyzes their semantic content (key topics, concepts, nomenclature) and automatically assigns each document to one or more (if necessary) defined clusters. This information is recorded in the semantic index. 

When the User makes a query, AI-UniBot does not start scanning the entire index immediately. Instead, it first analyzes the semantics of the query to determine which cluster or clusters it belongs to. And only then does it launch a search that occurs exclusively within the index of selected clusters. This approach allows you to reduce the amount of data to be processed during each search query, sometimes by orders of magnitude. And this is what directly affects the system's response speed.

In short, the new semantic index clustering function in AI-UniBot Personal Assistant & Corporate Chatbot is a strategic solution for Organizations that work with huge arrays of unstructured information. This speeds up AI search by many times, and sometimes by orders of magnitude. After all, the search area is purposefully limited to relevant semantic clusters, which at the same time also increases the accuracy of the results. Thus, AI-UniBot once again confirms its reputation as a powerful tool that always provides the fastest access to critical business information in real time.