Compliance Document Retrieval with AWS Bedrock

Query an existing Bedrock Knowledge Base — raw retrieval or RAG with citations — with PDF sync into its S3 source, inside governed Jinba workflows. Centralize retrieval so regulated research is logged and reusable.

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  1. Document arrives A new case enters the queue
  2. Retrieve from the knowledge base Grounded in your synced document corpus
  3. Draft with a Bedrock model Runs in your own AWS account
  4. Compliance gate Route by risk and completeness
  5. Log the evidence Answer, sources and timestamps

What you can do with AWS Bedrock Knowledge Base + Jinba

  • Retrieval in Your Own AWS Account

    Jinba queries the Bedrock Knowledge Base in your own AWS account, so retrieval runs against your data in your cloud rather than a shared public service, with every query kept in the run history.

  • Deterministic Workflow Execution

    Combine Bedrock AI inference with Jinba's 80% rule-based deterministic architecture. Produce consistent, auditable outputs that satisfy regulatory review without stochastic token burn at scale.

  • Audit Logging and Governance Controls

    Every Bedrock-powered workflow step is captured in Jinba's audit log. Combined with RBAC, SSO, and version control, teams meet the governance requirements of banking and insurance regulators.

  • Team Wide Workflow Sharing

    Workflows built in Jinba Flow that invoke Bedrock models are shared across the operations team under role-based permissions, not siloed to individual developers or analysts.

How teams use it

KYC Document Processing with Private AI

Automate KYC data extraction and validation using Bedrock-hosted models inside Jinba Flow. Sensitive customer documents are processed by models in your own AWS account, with every step logged for regulatory review and team reuse.

Compliance Workflow Automation

Run compliance checks against internal policy documents using private Bedrock inference. Jinba's deterministic execution layer ensures consistent, rule-bound outputs that compliance officers can audit and approve.

Contract Review and Extraction

Invoke Bedrock foundation models within Jinba Flow to extract clauses, flag risk terms, and summarize contracts. Results are structured, versioned, and surfaced to legal teams via Jinba App.

Loan Underwriting Document Assessment

Build underwriting workflows that send loan application documents to a private Bedrock model for structured data extraction and risk scoring, keeping all data within your own AWS environment.

Regulatory Reporting Workflow Automation

Aggregate data from internal systems, process it through Bedrock-hosted models, and generate structured regulatory reports inside Jinba Flow with full version history and change tracking.

Works well with

Microsoft SharePointMicrosoft 365

Automate document intake, extraction and routing

Microsoft OneDriveMicrosoft 365

Pull files into flows and write results back

Microsoft Entra IDMicrosoft 365

Look up M365 profiles and manager relationships

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.

Does Jinba support on-premise deployment with AWS Bedrock?

Jinba Flow can run on-premise or in a private cloud. AWS Bedrock is an AWS cloud service, so Bedrock steps call the Knowledge Base and models in your own AWS account and need access to AWS. The documents those steps use stay in your AWS account rather than going to a shared public model endpoint.

Is my data sent to a public AI model when using AWS Bedrock with Jinba?

No. Jinba's private model hosting uses AWS Bedrock in your own AWS account. Jinba Flow calls your Bedrock-hosted models directly, so inference data stays in your AWS account rather than going to a shared public endpoint. That matters in banking, insurance, and healthcare, where customer or patient data cannot go to a public model.

How does Jinba maintain audit logs for AWS Bedrock workflow steps?

Every workflow step executed in Jinba Flow, including steps that invoke AWS Bedrock models, is captured in Jinba's built-in audit log. Logs record inputs, outputs, the user or system that triggered the execution, and the workflow version in use. This gives compliance officers and regulators a complete, tamper-resistant record of every AI action taken, which is a requirement Jinba is built to satisfy and that generic AI tools typically cannot provide.

Can non-technical teams run AWS Bedrock powered workflows safely?

Yes. Workflows built in Jinba Flow that use AWS Bedrock are published to Jinba App, where non-technical business users such as KYC analysts, compliance officers, or loan processors can invoke them via a conversational interface or auto-generated input forms. Role-based access controls ensure only authorized users can run specific workflows, and every execution is logged, keeping the experience safe and governable without requiring operational staff to understand the underlying model configuration.

How does Jinba reduce AWS Bedrock token costs for enterprise workflows?

Jinba's deterministic architecture means that approximately 80% of workflow logic is rule-based and does not call a language model at all. Only the steps that genuinely require AI inference are routed to AWS Bedrock. This selective invocation model avoids the token burn that occurs when stochastic AI agents use a language model for every decision. The result is a significant reduction in Bedrock API costs at production scale compared to agent frameworks that call the model on every workflow step.

Deploy Secure Bedrock Workflows Today

Talk to our team about running governed AWS Bedrock workflows in your own AWS account.

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