Reduce Enterprise AI Hallucinations With Governed Knowledge

Jinba Brain grounds AI work in validated company records. Build review workflows that apply explicit rules, hold unsupported answers, and preserve evidence for investigating errors.

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Build a workflow for bank AI risk management to handle reviewing internal compliance answers before release using approved Jinba Brain policy records and their source references. Hold an answer if it lacks supporting records or contains conflicting policy versions and require specialist approval. Check execution permissions before accessing sensitive inputs and require explicit reviewer approval before releasing a result. Log access outcomes, source references where permitted, validation checks, and reviewer decisions; never publish knowledge changes from this review workflow.

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Ground Answers and Keep Unsupported Claims in Review

An AI answer can sound plausible while applying the wrong business definition. Jinba Brain structures source-backed company knowledge, while Jinba Flow lets teams encode checks and review paths. Build a workflow that withholds unsupported responses and records review outcomes. Measure accuracy on your own questions; validated records do not guarantee error-free model output.

Describe the reviewing internal compliance answers before release workflow in Jinba Flow chat, then refine and test its visual flowchart.
Hold an answer if it lacks supporting records or contains conflicting policy versions and require specialist approval. Set explicit standard and exception review paths in the workflow.
Validate policy versions, business definitions, and supporting evidence and enrich them from connected, authoritative systems before review.
Record validation and reviewer decisions in the workflow log, and let authorized staff run approved workflows through Jinba App.

Build Deploy and Run Your Governed Workflow

Send weekly metrics to Slack every Monday at 9am

1

Build in Jinba Flow

Work with bank AI risk management to define policy versions, business definitions, and supporting evidence, authoritative sources, and reviewers. Describe the process in Jinba Flow, refine its visual flowchart, and test valid, conflicting, and missing-data cases. Brain schema definitions are agreed with your team; Flow is the workflow editor.

test_runner.log

Running workflow test...

✓ Input validation 12ms

✓ Fetch from Sheets API 847ms

✓ Process 24 rows 156ms

✓ Send to #sales 203ms

PASS All steps completed 1.22s

Output preview:

"sent": true, "channel": "#sales"

2

Deploy securely (API / batch / MCP)

Deploy the workflow as an API, batch process, or MCP server in the agreed Jinba environment. Connect only approved sources and configure access controls and logging. For disconnected operation, use models and connections that remain inside the network boundary.

3

Run in Jinba App (guardrailed UI)

Authorized staff submit requests through Jinba App chat or generated forms. The workflow applies configured rules, routes evidence gaps to reviewers, and releases only approved results. Log held and denied outcomes too; proposed knowledge changes use a separate validation and approval process.

Enterprise Ready

Control, security, and support for large organizations.

On-Prem

Jinba

Private

On-premises or private cloud hosting

Run Jinba in your own environment with full data control.

Team Permissions

128 Active

Sarah Chen Engineering Lead

Workflows

Michael Ross Product Manager

Analytics

Guest User External Auditor

Admin Access

SSO Enforced

Updated 2m ago

Advanced access control

Role-based permissions and SSO integration.

audit_stream.log

1

[200] 09:14:22 GET /api/v1/health_check

2

[INFO] 09:14:23 Worker node_04 connected

3

[AUTH] 09:15:01 User admin@corp verified

4

[AUDIT] 09:15:02 Role assignment update

5

// Syncing to edge...

6

[200] 09:15:08 Config updated

Audit logging

Complete compliance and security oversight tracking.

Marketing

RESTRICTED

HR

ALLOWED

Screening

Automation

Organization management

Spaces, roles, and approvals for your team.

OpenAI

Gmail

Slack

HubSpot

Salesforce

Notion

Linear

GitHub

Teams

Dropbox

OpenAI

Gmail

Slack

HubSpot

Salesforce

Notion

Linear

GitHub

Teams

Dropbox

Pre-built & custom integrations

100+ pre-built integrations plus custom connectors for your internal systems.

Alice 10:42 AM

We need a connector for our internal CRM. Is this possible?

James 10:43 AM

Sure! Can you share the API spec?

crm_api_spec.pdf

Got it. I'll have the connector ready by tomorrow.

James Next day

Connector is live!

Thank you!

Dedicated Engineer Support

Work side-by-side with our engineers to remove blockers and accelerate your workflow development.

AWS Bedrock Claude 3.5 Sonnet

Azure OpenAI GPT-4 Turbo

Meta Llama 3 Self-hosted 70B

Private model hosting

Use Bedrock, Azure AI, or your own models securely.

On-Prem

Jinba

Private

On-premises or private cloud hosting

Run Jinba in your own environment with full data control.

Team Permissions

128 Active

Sarah Chen Engineering Lead

Workflows

Michael Ross Product Manager

Analytics

Guest User External Auditor

Admin Access

SSO Enforced

Updated 2m ago

Advanced access control

Role-based permissions and SSO integration.

audit_stream.log

1

[200] 09:14:22 GET /api/v1/health_check

2

[INFO] 09:14:23 Worker node_04 connected

3

[AUTH] 09:15:01 User admin@corp verified

4

[AUDIT] 09:15:02 Role assignment update

5

// Syncing to edge...

6

[200] 09:15:08 Config updated

Audit logging

Complete compliance and security oversight tracking.

Marketing

RESTRICTED

HR

ALLOWED

Screening

Automation

Organization management

Spaces, roles, and approvals for your team.

OpenAI

Gmail

Slack

HubSpot

Salesforce

Notion

Linear

GitHub

Teams

Dropbox

OpenAI

Gmail

Slack

HubSpot

Salesforce

Notion

Linear

GitHub

Teams

Dropbox

Pre-built & custom integrations

100+ pre-built integrations plus custom connectors for your internal systems.

Alice 10:42 AM

We need a connector for our internal CRM. Is this possible?

James 10:43 AM

Sure! Can you share the API spec?

crm_api_spec.pdf

Got it. I'll have the connector ready by tomorrow.

James Next day

Connector is live!

Thank you!

Dedicated Engineer Support

Work side-by-side with our engineers to remove blockers and accelerate your workflow development.

AWS Bedrock Claude 3.5 Sonnet

Azure OpenAI GPT-4 Turbo

Meta Llama 3 Self-hosted 70B

Private model hosting

Use Bedrock, Azure AI, or your own models securely.

Talk to Our Team

Discuss reviewing internal compliance answers before release with Jinba. Contact sales to scope the sources, review rules, deployment requirements, and evaluation criteria for your department.

Book a Demo

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 guarantee zero AI hallucinations?

No. Validated records and explicit rules can reduce unsupported answers, but extraction, source data, and generated responses still need evaluation. The overview calls for an evaluation dataset and ongoing accuracy checks. The example workflow holds missing or conflicting evidence for review instead of presenting an unsupported answer.

What should an AI answer evaluation include?

Use representative questions with approved expected answers, source references, and business definitions. Include conflicting versions and missing facts, then check whether the workflow routes those cases correctly. The overview recommends assigning a data scientist and preparing the evaluation dataset and method before proceeding.

How do teams build and run the workflow?

Builders describe the process in Jinba Flow, refine its visual flowchart, and test it with representative data. Workflows can be deployed as APIs, batch processes, or MCP servers. Authorized business users run approved workflows in Jinba App through chat and generated input forms. Knowledge connections and review rules must be configured for your use case.

What validation and approvals are included?

The Brain overview describes checks for schema conformance, referenced IDs, duplicates, and source hashes, followed by reviewer approval before records are saved. The example workflow adds department-specific hold rules. Source validation does not prove every business statement is correct; reviewers and evaluations still need to test the records and resulting answers.

What can our team trace during review?

The overview describes each knowledge record retaining its source path and SHA-256 hash, with full record change history. Jinba also provides platform audit logging. Configure the workflow log to link validation and reviewer decisions to the records used. A hash supports integrity checking; it does not prove that the source is factually correct.

Can we run Jinba on our infrastructure?

Jinba offers on-premise and private-cloud deployment. The Brain overview describes unmodified raw data in an on-premise store. For an air-gapped requirement, agree a locally hosted model and source connections that work within the network boundary. Managed services such as AWS Bedrock or Azure AI require connectivity and are not interchangeable with a fully disconnected deployment.

How should we start and measure success?

The Brain overview proposes a 4 to 8 week pilot in one department, with results measured on your own questions. Agree source systems, schema definitions, reviewers, an evaluation dataset, and acceptance criteria first. Treat pilot timing and any accuracy, response-time, or token-cost benefit as things to validate for your deployment, not guaranteed outcomes.

Build your governed workflow with Jinba

Talk to our team about your process, integrations, and deployment requirements.

Book a Demo