Integrate Azure AI Search with Jinba

Connect Azure AI Search to Jinba Flow to build auditable, on-premise document retrieval workflows for regulated enterprises. Power KYC intake, contract review, and compliance checks with full audit logging.

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  1. Verification requested A case needs checking against indexed records
  2. Query Azure AI Search Hybrid keyword and vector search over your content
  3. Assess the match Compare retrieved records with the submission
  4. Escalate mismatches Anything inconsistent goes to an analyst
  5. Return the verdict Decision plus the records behind it

What you can do with Azure AI Search + Jinba

  • Private On-Premise Deployment

    Run Azure AI Search workflows inside your own infrastructure or private cloud. Jinba Flow supports on-premise and air-gapped deployment so sensitive documents never leave your controlled environment.

  • Auditable Search Workflow Execution

    Every search query and result routed through Jinba is logged end to end. Compliance and audit teams get a full, tamper-evident record of what was retrieved, by whom, and when.

  • Deterministic Workflow Integration

    Wrap Azure AI Search inside Jinba's 80% rule-based workflow engine. Search results feed into downstream compliance checks, document drafting, or approval steps with consistent, repeatable outputs.

  • Team-Wide Governance and Permissions

    Publish Azure AI Search workflows as shared team assets in Jinba Flow with RBAC, SSO, and Active Directory integration so only authorized staff can trigger sensitive document retrieval.

How teams use it

KYC Document Retrieval and Verification

Trigger Azure AI Search from a Jinba Flow KYC workflow to locate and surface relevant customer documents, then route results through compliance checks and audit logging before analyst review.

Contract Search and Compliance Review

Use Azure AI Search to locate contract clauses across large document repositories, feeding results into Jinba Flow for automated compliance checks, flagging, and auditable approval routing.

Regulatory Document Ingestion and Audit

Ingest and index regulatory filings through Jinba Flow using Azure AI Search, ensuring every document retrieval step is logged, permissioned, and reproducible for internal and external audit requirements.

Loan Underwriting Document Search

Automate retrieval of applicant financial documents stored in enterprise repositories via Azure AI Search, routing structured results into Jinba's loan underwriting workflow for consistent, auditable decisioning.

Internal Compliance Search Workflow

Let compliance officers run governed search queries across internal repositories through Jinba App, with Azure AI Search handling retrieval and Jinba enforcing RBAC controls and full audit trail logging.

Learn how

How to Migrate SharePoint RAG to a Production-Ready Pipeline with Azure AI Search and Jinba

Move a proof-of-concept retrieval setup onto infrastructure that survives a compliance review.

Works well with

Microsoft SharePointMicrosoft 365

Automate document intake, extraction and routing

Azure OpenAIAI Models

OpenAI models hosted in your own Azure tenant

AWS Bedrock Knowledge BaseSearch & Data

RAG and document sync inside your own VPC

Microsoft OneDriveMicrosoft 365

Pull files into flows and write results back

Microsoft TeamsMicrosoft 365

Manage channels and messages where teams work

Frequently Asked Questions

Everything you need to know about Jinba. Can't find the answer you're looking for? Reach out to our support team.

Can Jinba run Azure AI Search workflows fully on-premise?

Yes. Jinba Flow supports on-premise and private-cloud deployment, including air-gapped environments. Your Azure AI Search integration runs entirely within your own infrastructure, so no query data or document content is routed through a public third-party service. This is a core reason regulated banks and insurance companies choose Jinba over generic cloud automation tools.

Is my Azure AI Search data sent to a public AI model?

Not by default. Jinba supports private model hosting via Azure AI, AWS Bedrock, or self-hosted models. When you build an Azure AI Search workflow in Jinba Flow, you control which model, if any, processes retrieved content. Jinba's 80% deterministic, rule-based architecture means many workflow steps execute without invoking an LLM at all, reducing exposure of sensitive document data.

Does Jinba provide audit logs for Azure AI Search queries?

Yes. Every workflow execution in Jinba Flow is fully audit logged, including the inputs passed to Azure AI Search, the results returned, the user or system that triggered the workflow, and each downstream step taken. These logs satisfy the auditability requirements common in banking, insurance, legal, and healthcare compliance reviews.

How does Jinba enforce access control on Azure AI Search workflows?

Jinba Flow publishes Azure AI Search workflows as shared team assets governed by role-based access control, SSO, and Active Directory integration. Only users with the appropriate permissions can trigger a search workflow or view its outputs. This team-level governance layer is the key difference between Jinba and individual AI tools that lack enterprise access controls.

How long does it take to build an Azure AI Search workflow in Jinba?

Technical and semi-technical teams can describe their Azure AI Search workflow in natural language and Jinba Flow generates a draft workflow to review and refine in a visual editor. Regulated enterprises typically go from requirement to a tested, deployed workflow in days rather than the weeks or months required by traditional consultant-led implementations or rigid automation platforms.

Build Governed Azure AI Search Workflows

Talk to our team about deploying Azure AI Search workflows with full audit logging, on-premise hosting, and enterprise access controls inside Jinba Flow.

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