SMAX Service Management

SMAX (OpenText Service Management) is a complete Enterprise Service Management solution provided as a SaaS service from a data center in Norway, for the Nordic market. SMAX provides efficient handling and management of enquiries, orders and other internal and external requests.

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SMAX (OpenText Service Management) provides complete Enterprise Service Management by digitizing and streamlining all kinds of business processes where inquiries, orders, events or other "cases" are central. SMAX replaces both email-based processes and traditional "trouble ticketing" systems that e.g. IT departments and service desks use.

PRODUCT DESCRIPTION

SMAX is a SaaS-based Enterprise Service Management solution delivered as SaaS from a Norwegian data center, where all data processing and storage takes place in Norway in accordance with GDPR, and is operated in line with ISO standards 27001 (information security), 9001 (quality) and 14001 (environment).

For the end user

The end user meets SMAX through a portal, which is specified for the individual customer. The portal provides access to forms to provide input in the form of questions, searches, filling out forms, etc. To support the user, there is also normally documentation about services and other things (so-called knowledge articles) that can also be searched. An advanced chatbot is also part of the portal, and it is up to the user whether he wants to communicate via discussion with the chatbot, "Google-like" search, Teams integration or navigation in the service catalog and service categories that exist. Both the portal, chatbot and search are also found in the associated app .

When an end user fills out and submits a form, it goes through a predefined process. That process depends on the type of case – some are approved (e.g. if there are costs, security or other special elements to the case), while others go straight to the right service agent. Still other cases are so automated that they execute without going through a service agent at all.

For the service agent

Case managers in SMAX have a separate interface than the one used by end users, where they have access to more information, e.g. about previous cases from all customers, registered solutions, lookups against external systems, or similar. Case managers may have limitations on what types of data they can view based on which role or group/department they belong to. Areas such as HR, security, HSE and finance are areas that are often kept separate in this way.

Support for "all" processes

SMAX supports all common IT-type handlers, which are defined in the ITIL framework (ITSM). However, most customers use many more processes than these, which has gradually been called Enterprise Service Management (ESM). In practice, one can say that ESM is ITSM + "all other processes in the business".

User interface

All SMAX user interfaces (end user, case manager and mobile) support universal design according to the WCAG standard, which Norwegian regulations are also based on. The SMAX user interface is dynamic, using HTML5, with so-called " responsive design". The customer can establish new or changed forms, processes and service structures themselves, all with "point and click" development. No changes will be made to the system, the possibility of continuous upgrading that is part of the SaaS service, it ensures.

Artificial Intelligence

SMAX has extensive support for the use of artificial intelligence for both analysis and efficient text and image handling. Aviator – the built-in generative AI function – can be accessed as a chatbot by end users and case managers. In addition, it can be integrated into process flows, or used to create independent agents for different functions.

More on Aritficial Intelligence

SMAX includes both predictive AI (built-in functionality) and generative AI ( Aviator ). Predictive AI is used for automatic classification and assignment of cases, for suggesting solutions, for recognizing CIs, for pattern recognition of cases, and for analyzing planned and implemented changes. Generative AI delivers conversational guidance, case summarization, suggested solutions, and production of knowledge content directly in the portal (for end users) and agent interfaces.

Components/processes that can use AI

End-user portal: Aviator can answer questions in natural language, suggest relevant services ( intent matching ), provide step-by-step guidance, and retrieve information from both internal knowledge sources and external documents (SharePoint, Confluence) via built-in integrations.
Agents (Request/ Incident / Change / Problem/ Knowledge): Aviator summarizes cases, suggests solutions, analyzes risk of changes, and provides contextual assistance based on business data. New Aviator agents allow generative AI to also perform tasks autonomously, such as generating knowledge articles, defining metadata, and initiating process or integration flows, with full audit trails.

Opt -in / management

Predictive AI can be enabled/disabled per function. Some functions can be turned off (solution suggestions via email, live translation, OCR image interpretation, CI detection, expert recommendations), while other functionality is built into the interface, such as auto-classification , solution suggestions and analytics, but the caseworker can choose not to use these.
For generative AI, the administrator can control whether chat should use AI (Aviator mode) or not ("classic"). Aviator handles language dynamically and responds in the language the user is using, among the approximately 20 languages supported by the solution. Users and agents can interact with AI in their own language without the need for global settings. In addition, AI behavior can be customized via Aviator models (using configuration in AI Studio). Here, prompt templates , variables, token limits , and context sources can be configured per model. The model is an AI function that is derived from a processing rule (business rule ).

Impact of training data

Predictive AI uses the customer's own historical data to generate suggestions; no shared model is trained across customers.
Generative AI uses RAG architecture, where relevant documents and knowledge articles are first retrieved from the customer's approved data sources (documents, web pages, etc.) and then combined with the language model (LLM) for generation. The LLM is not trained on customer data. Access filtering ensures that users only receive answers based on information they have access to.

General information about the AI models used

Aviator comes standard with a private language model (LLM), powered by OpenText , that uses Llama 3 as the core model and vector index ( Milvus ) for contextual data processing in an isolated customer-specific environment. The architecture supports semantic search, context composition, and generation with built-in security mechanisms such as toxicity filtering and stop words .

The customer can also choose to use a public LLM backend (Gemini 2.5 Flash) for more advanced language understanding, while private LLM remains the standard for data sovereignty.

Note: Aviator requires a separate license in addition to the SMAX agent licenses, based on the number of queries handled per month.

Integrations with the rest of the IT landscape

There are many integration options. SMAX can be integrated with applications of all types for data and case exchange via API calls. For many applications, there are ready-made integrations that can be easily linked into the company's own processes.