AI Models

​

Model Usage

You can select which AI model suits you best for different uses from the list of models offered by the platform or plug in your own custom AI model. Supported usages include:

• Conversation
• Image Analysis
• Image Generation
• Embeddings Generation
• Audio Transcription
• Text to Speech
• Document Summarization
• Speech to Speech (Conversation / Realtime)
• Moderation

These models have various functions, performance profiles, and feature sets.

​

How Praxis AI Uses Models

Praxis AI can orchestrate multiple providers and models in parallel using a unified interface:

• Configure several providers in Configuration → Personalization and AI Models.
• Assign preferred models to each Model Use (Conversation, Images, Audio, etc.).
• Overwrite the conversation provider for each Assistant

This orchestration is transparent to your LMS, Web SDK, or REST integrations.

​

Model Selection

​

Default for Your Digital Twin

Each Digital Twin in Praxis AI can use different models optimized for its domain. To select or change models:

1. Go to the Admin section.
2. Edit your Digital Twin.
3. Open the Personalization and AI Models section.
4. Review or change the model used for each Model Use (Conversation, Images, Audio, etc.).

​

Conversation at Runtime

At runtime, you can easily switch the LLM used for Conversation by accessing the Settings in the Side Bar panel and review model capabilities by clicking the Model Options detail Custom Model is displayed in the same section in the Side Bar

​

Specific to each assistants

You can specify which Conversation model to use for each assistants Behind the scenes, Praxis AI’s Neural Engine can route requests to different models based on:

• Use Case
• Assistant Specific model
• Token budget and cost constraints
• Model availability and latency
• User preferences and history

This allows you to balance quality, speed, and cost without changing your front-end integration.

​

Platform Models

Praxis AI middleware offers access to a broad catalog of state-of-the-art AI models. You can select the model that best fits your needs based on performance, cost, and capabilities. The default model is configured to use the latest, most capable model available on the platform. In most cases, you should keep default selected unless you have a specific requirement (for example, strict cost control, specific provider, or latency constraints). Models can be accessed using:

• The OpenAI Client
• Or through Amazon Bedrock

​

Provider-Based Models

Praxis AI exposes conversation and related capabilities (vision, audio, embeddings, moderation, realtime) through two main provider types:

• Amazon Bedrock
• OpenAI-Compatible Clients

Each provider contains groups (Amazon, Anthropic, OpenAI, Gemini, etc.) and individual models with specific capabilities and uses.

​

Bring Your Own AI Model (BYOM)

You can connect your own hosted LLM (for example, a model deployed on Google Vertex AI, private OpenAI-compatible endpoint, or a Bedrock-hosted custom model) and use it as a replacement for any of the supported usages.

​

Configure a Custom Model

To add a custom model for Conversation (or any other use):

1. In the Admin UI, edit your Digital Twin.
2. Under Personalization and AI Models, click Add AI Model.

3. In the Add AI Model panel, enter the properties required to connect to your LLM:

• Model Name
  The exact model identifier published by your hosting platform.
  This value is case sensitive and must match your provider’s model name, for example:
  gemini-flash or projects/my-proj/locations/us/models/my-model.
• Status
  Active models are considered by the system for routing and selection.
  Inactive models are ignored but kept in configuration.
• Description
  Human-readable description of the LLM for admins and authors using this Digital Twin.
• Model Use
  The specific usage for this model (for example, Conversation, Image Generation, Document Summarization).
  This determines which internal calls will use this model.
• Client Library Type
  Choose from:
  • Open AI for OpenAI-compatible endpoints (including many custom or Vertex AI gateways exposing an OpenAI-style API).
  • Bedrock for Amazon Bedrock-hosted models.
    Most Gemini-based models connected through an OpenAI-compatible proxy should use Open AI.
• API URL
  The base public URL of your model endpoint, for example:
  https://ai.my-school.edu or your Bedrock-compatible endpoint.
  Typically, the model name or ID is appended to this base URL when interacting with the LLM.
• API Key
  The secret key used to authenticate requests to your endpoint.
  Keep this key secure and confidential; rotate it periodically for security.

4. Click Save to register the new custom AI model.

Once saved:

• The model appears in the list of custom AI models.
• For its configured Model Use, it will replace the platform default model.
• All conversations or tasks mapped to that Model Use will start using your custom model without any client-side code changes.

​

End-to-End Workflow