Integrate Azure OpenAI with Jinba

Yes. Jinba supports private model hosting via Azure AI, meaning your Azure OpenAI models can run within your own private cloud or on-premise environment. Data does not leave your perimeter. This is a core requirement for banks, insurers, and other regulated enterprises operating in air-gapped or strict data-residency environments, and it is a built-in deployment option in Jinba Flow, not an add-on.

No. When you configure Azure OpenAI as your model provider in Jinba, the integration uses your own Azure subscription and private model endpoint. Jinba does not route your data through any public model or shared inference layer. Your documents, workflow inputs, and outputs remain within your own infrastructure, consistent with the data governance requirements of regulated financial institutions and insurers.

Jinba's deterministic architecture wraps Azure OpenAI LLM calls inside rule-based workflow logic. Approximately 80% of a typical workflow is deterministic, meaning the same inputs always produce the same governed outputs. Every step is logged end-to-end in Jinba's audit log, including the model call, inputs, and outputs. This gives compliance officers a complete, tamper-evident record of every automated decision for regulatory review.

Yes. Jinba App provides a conversational interface and auto-generated input forms that allow non-technical business users, such as KYC analysts and compliance officers, to execute Azure OpenAI workflows built in Jinba Flow without learning any workflow tooling. Access is governed by role-based permissions (RBAC) and Active Directory integration, so only authorized users can invoke specific workflows.

Jinba is purpose-built for regulated enterprises that need deterministic, auditable AI workflows, something Microsoft Power Automate was not designed to deliver at that compliance level. Jinba adds on-premise deployment, end-to-end audit logging, RBAC, SSO, version control, and feature flags out of the box. Jinba also generates workflow drafts from plain-language descriptions, enabling regulated teams to build and deploy Azure OpenAI workflows in days rather than months.