AI Models
In this view, you get an overview of all language models that you have available, as well as manage which language models should be available to use in your organization’s Intric environment. As an administrator, you can control exactly which models should be available in different parts of the platform. By then connecting specific models to different security classes, you can ensure that more resource-intensive or specialized models are only used where they are really needed.
Different types of models
For the platform to function optimally, three different types of models are used with specific areas of responsibility:
| Model Type | Description |
|---|---|
| Completion (Language Models) | Functions as the system’s “brain”. Used to generate text, conduct dialogue and solve logical tasks based on the user’s instructions. |
| Embedding (Text Embedding) | Converts information into mathematical vectors to enable search in knowledge libraries (RAG). Rule: Only one active model per security class. |
| Transcription (Transcription) | Converts speech to text. Makes it possible to index audio files and interact with the assistant via voice. |
Sub-processor column
Section titled “Sub-processor column”Each model in the table now includes a Sub-processor column. This shows which company directly processes data when that model is used.
| Scenario | What is shown |
|---|---|
| Model accessed via an external API (e.g. OpenAI, Anthropic) | The respective model vendor |
| Model hosted by Intric on our own infrastructure | Intric’s underlying infrastructure provider |
From a GDPR and data handling perspective, the sub-processor is the entity that receives and processes your organization’s data when the model is called. Use this column when reviewing your Data Processing Agreements (DPA) or when deciding which models are appropriate for a given security classification.
Model selection – how should I think?
Section titled “Model selection – how should I think?”When choosing which models should be active for different assistants or security classes, base your decision on the following principles:
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Don’t overcomplicate it: Most modern language models are general-purpose and handle most everyday tasks very well.
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Speed vs. Reflection ability: For the personal assistant and simpler tasks, use a fast model. This provides a smoother user experience where responses come directly.
- For complex tasks, such as advanced legal analysis, medical review, or heavy problem-solving, choose a model with high reflection ability, even though these may feel somewhat slower.
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Cost efficiency: By limiting access to the most advanced (and often more expensive) models to specific security classes where they are actually needed, you can optimize your organization’s resource usage.
You also need to consider what types of personal data might possibly be processed by the assistants being used. Read more about how this is clarified to users by using Security Classifications.