Overview

AI Knowledge is based on Retrieval-Augmented Generation (RAG), an advanced AI approach that enhances large language models (LLMs) by dynamically retrieving information from specified sources in real time. Unlike standard AI models, which rely solely on previously trained data, RAG dynamically searches for relevant documents from designated knowledge bases before generating a response. This ensures that AI-generated answers are not only contextually accurate but also up-to-date, making this technology particularly useful in IT Service Management (ITSM) and corporate environments where knowledge is constantly evolving.

Matrix42 offers flexible deployment options for AI-powered solutions. When using the CAI (Conversational AI) platform with locally hosted Natural Language Processing (NLP) and Large Language Model (LLM) components, fully owned and operated by Matrix42, no external providers are involved at any stage of data processing. All data remains within the customer’s infrastructure or approved Matrix42-hosted environments (e.g., within the EU), ensuring full control over infrastructure, data flow, and compliance.

Alternatively, customers may choose to integrate external LLM providers, such as Azure OpenAI Service or OpenAI. In such cases, agent input and context required for generating a response are transmitted to the infrastructure of the selected provider and processed in accordance with their regional hosting and data protection policies. Note: In case of using own models, Matrix42 is not responsible for the quality of generated responses.

Matrix42 GenAI can power AI Knowledge by leveraging Retrieval-Augmented Generation (RAG). This allows support agents to receive accurate, context-aware answers based on the organization's internal knowledge, such as:

• IT service documentation.
• Knowledge bases.
• Company policies.
• Internal repositories (wikis, PDFs, Confluence, and structured data sources).

The model ensures that only authorized and relevant information is retrieved and shared, enhancing agent efficiency and case resolution capabilities while reducing the workload on IT support teams.

Possible widget embeddings:

• M42 Pro (Agent Widget)
• MS Teams

Data Sources

AI Knowledge supports the following data sources:

• M42 Core/Pro and Enterprise
• SharePoint
• Confluence
• DokuWiki
• HelpJuice
• Web Pages – supports pages where elements are not dynamically generated.
• Local Files – supported formats: CSV, XLS/XLSX, DOCX, PDF.

Available Runtimes

• M42 local GenAI (Finland, Germany)
• Bring Your Own Model (Azure OpenAI / OpenAI)

Additional Features

Firewall

The system has security mechanisms to restrict access to unauthorized resources and control data flow to ensure security. The firewall can be based on OpenAI, Azure OpenAPI runtime, or a local classification model.

Reranking

A mechanism for sorting documents based on their relevance to the given query.
Selection of the best documents for the prompt based on relevance scoring.

Full-Text Search (FTS)

Full-text search enabling retrieval of relevant fragments within documents and files.

System Workflow

The agent submits a query – interaction occurs through a widget or another system interface integrated with ITSM.

Embedding-based search – the query is encoded into a vector and compared with existing documents in the database.

Ranking and reranking – the system sorts results based on relevance.

Best documents are included in the prompt – selected content is used to formulate the response.

Response generation by GPT – the model generates a response based on system prompt instructions and selected documents.

Context Handling

The system supports follow-up questions, allowing agents to continue the conversation while maintaining contextual continuity.

• Option to disable context for independent responses.

Automatic Data Refresh

Ability to periodically re-fetch data, e.g., new KB articles.
Ensures up-to-date information through scheduled source updates.

Data Processing and Privacy

Matrix42 processes exclusively the data that is necessary to generate a relevant and accurate response. This typically includes the agent’s input (query), applicable contextual attributes from the M42 Pro platform, and relevant content retrieved from the organization’s knowledge base.

By default, conversation transcripts are retained for a period of 12 months. This retention period can be customized based on the client’s preferences or internal policies. No data collected during interactions is used for training AI models. Matrix42 does not process, store, or download any sensitive or personal data beyond what is strictly required for response generation.

The following data points are recorded in system logs for auditing and monitoring purposes:

• question – the agent’s question.
• response – the previous answer, enabling context continuity.
• sessionId – a unique identifier for the conversation session.
• addressIp – the IP address of the agent (if available).
• startTime – the timestamp marking the session’s initiation.
• processingTime – the time taken to generate the response [s].
• rawQuestion – the original input submitted by the agent.
• rawResponse – the unprocessed response generated by the AI model.