Custom LLM Configuration

Introduction

This release introduces Custom LLM Configuration in Unifize.

Organizations can now explicitly configure how AI behaves across structured workflows, including prompt governance and model selection. This improves consistency, auditability, and execution control when using AI within checklist-based fields.

AI behavior is no longer dependent on implicit defaults. It is now governed through defined configuration at the organization level.

Release Capabilities

1. Organization-Level System Prompt

   1. Admins can define a System Prompt for the organization.

   2. The System Prompt applies across AI-enabled fields.

   3. Changes are effective for new executions.

   4. Access is restricted to authorized administrators.

2. 1. Organizations can select which AI model is used for execution.

   2. Model selection is explicit and configurable.

   3. Switching models does not change governance or field-level controls.

3. 1. AI is triggered only within defined AI-powered checklist fields.

   2. Output is returned in structured format aligned to Unifize schemas.

   3. AI execution is scoped to the record and field context.
4. 1. AI behavior is governed by explicit configuration.

   2. Prompt precedence is enforced.

   3. No hidden or implicit AI instructions are applied.

Unifize supports integration with a broad ecosystem of AI model providers. This enables flexibility across cloud, enterprise, hosted, and private model deployments.

Supported providers include:

Enterprise & Foundation Model Providers

• OpenAI

• Anthropic

• Azure OpenAI / Azure AI

• AWS Bedrock

Additional Platforms & Model Hosts

• DeepSeek

• DeepInfra

• Fireworks AI

• ElevenLabs

This allows organizations to use approved vendors, regional deployments, or private model infrastructure without redesigning workflows.