Model Governance Policy

Last Updated: 19 June 2026

Company: Myntriq Pte Ltd (UEN 202537571M), Singapore

Contact: hello@myntriq.io

Purpose

This policy describes how Myntriq selects, routes, evaluates, and governs the AI models available through MyntriqOS. It is intended for customers, procurement teams, and technical reviewers who need to understand how model decisions are made and what controls exist over AI model behaviour on the platform.

1. Model Architecture

MyntriqOS uses a multi-model architecture. Rather than being tied to a single AI provider, MyntriqOS routes inference requests through a managed model routing layer (LiteLLM) that can serve multiple models from multiple providers.

This architecture provides:

• Provider independence — if a single provider experiences an outage or changes its terms, the platform continues to operate through alternative models
• Model choice — customers can select models that match their requirements for capability, cost, or data handling
• Future flexibility — new models can be added to the routing layer as they become available, without requiring customers to change how they interact with the platform

All model inference is handled server-side through Myntriq's LiteLLM service. Customers and users interact with a unified platform interface — they do not interact directly with model provider APIs.

2. Supported Models

The following models are currently available through MyntriqOS:

Model availability by environment

3. Model Selection Principles

Myntriq evaluates models against the following criteria before making them available on the platform:

Capability — The model must be capable of performing the tasks for which it will be used in MyntriqOS (reasoning, instruction following, structured output generation, content creation). Capability is assessed through internal testing against MyntriqOS use cases.

Safety and alignment — The model must have demonstrated reasonable alignment with safe and responsible AI practices, including resistance to generating harmful content and responsiveness to safety instructions.

Data handling — The model provider must offer clear terms around how data submitted through their API is used. For commercial models (OpenAI, Anthropic), Myntriq requires that the provider's API terms confirm that customer data is not used to train the provider's models.

Reliability — The model must be reliably available through a hosted API or proxy service. Myntriq does not offer models that require customer-managed hosting.

Commercial viability — The model must be available at a cost level that is consistent with the platform's pricing structure and the expected usage patterns of MyntriqOS customers.

4. Open-Source Models

MyntriqOS includes support for open-source AI models (currently Llama 3.3, developed by Meta). Open-source model inclusion reflects several important values:

Independence from proprietary models. Open-source models are not controlled by a single commercial entity. This reduces the risk that changes to a proprietary model provider's terms, pricing, or capabilities create a problem for customers without alternatives.

Cost efficiency. Open-source models are generally available at lower per-token costs, making them suitable for high-volume, lower-complexity tasks (routine query handling, summarisation, content drafting).

Transparency. For customers in certain regulated sectors or with specific procurement requirements, open-source models may be preferred because the model architecture is publicly documented.

Myntriq currently routes Llama 3.3 inference through OpenRouter, which provides hosted inference for open-source models without requiring Myntriq or its customers to manage model infrastructure.

5. Model Permissions and Customer Control

Organisation-level model permissions

Each MyntriqOS customer organisation has a model permissions configuration that determines which models are available within that organisation. This allows:

• Customers to restrict agents to specific models that meet their internal data handling or compliance requirements
• Customers to enable or disable specific models for their users
• Administrators to configure model permissions through the platform settings

Default model permissions are configured during environment setup and can be adjusted by organisation administrators.

Agent-level model selection

Where agent-level model selection is supported, administrators can configure specific agents to use specific models. This allows a customer to run different agents on different models — for example, using a more capable model for executive intelligence tasks and a cost-efficient model for routine query handling.

User model selection

In standard deployments, users interact with agents — they do not select models directly. Model selection is an administrative function. This ensures that model governance decisions are made at the organisation level, not by individual users.

6. Governance Controls

Audit logging

Every AI model invocation through MyntriqOS is recorded in the audit log with:

• The model used
• The agent identifier
• The user identifier
• Timestamp
• Token usage (input and output)
• Estimated cost
• Outcome

This provides administrators with complete visibility over which models are being used, by whom, for what, and at what cost.

Cost visibility

The Governance Dashboard provides cost visibility at the organisation, agent, and user level. Cost data is derived from token usage metrics and the current pricing of each model. This allows organisations to monitor AI spend and to make informed decisions about model selection and usage policies.

Usage limits

Myntriq reserves the right to apply usage limits at the organisation or user level to prevent abuse or to manage platform capacity. Where usage limits are applied, affected customers will be notified.

7. Adding New Models

Myntriq evaluates new models on an ongoing basis. The criteria in Section 3 apply to all new model additions.

When a new model is added to MyntriqOS:

1. The Subprocessor List is updated to include the model provider if not already listed

2. The MyntriqOS Privacy Policy and this Model Governance Policy are updated to reflect the new model

3. Customers are notified through the platform or by email at least 14 days before the model becomes available in production

4. New models are initially added to the available models list but not activated by default in existing organisations — administrators must opt in

8. Removing or Deprecating Models

If a model is removed from MyntriqOS — due to provider changes, performance issues, security concerns, or strategic reasons — affected customers will be notified with reasonable notice (minimum 14 days where feasible) and provided guidance on migrating agents to alternative models.

9. Model Limitations

Customers should be aware that:

• All AI models, regardless of provider, may produce incorrect, incomplete, or biased outputs
• Model capabilities change over time as providers update their models — Myntriq cannot guarantee that a specific model version will be available indefinitely
• Model behaviour may vary based on the prompt, context, and system instructions provided
• No model is certified for use in regulated decision-making without human review

The AI Usage Policy contains further guidance on the limitations of AI-generated outputs and the customer's responsibility for reviewing them.

10. Contact

For questions about model governance, model availability, or to request information about specific model data handling terms, contact hello@myntriq.io.

*Last updated: 19 June 2026*