New ID_Check v2.6.11 — a new ID recognition engine improves OCR for Korean documents, PII masking is now set from the dashboard, and submissions can be searched by custom fields (cf1–cf3). See what's new →
New ID_Check v2.6.11 — a new ID recognition engine improves OCR for Korean documents, PII masking is now set from the dashboard, and submissions can be searched by custom fields (cf1–cf3). See what's new →
Step-by-step guide to creating your first Omni workflow from scratch.
This guide walks you through creating a workflow in the Omni dashboard. A workflow defines your verification logic — the policy, engines, and output format — that Omni applies every time you run an analysis.
Creating a workflow involves four steps in the dashboard. Each step builds on the previous one.
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Step 1: Basic Info
Start by giving your workflow a name and an optional description.
Name — A short, descriptive label for the workflow (e.g., “Invoice Review”, “Vendor KYB Check”). This is how you will identify the workflow in the dashboard and API responses.
Description — A brief summary of what this workflow does. This is for your team’s reference only and does not affect how Omni processes documents.
Step 1: Enter a workflow name and description.
Use naming conventions that reflect the document type and verification purpose. For example: “Invoice - Amount Validation” or “KYB - Business Registration”. This makes it easier to manage multiple workflows in a project.
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Step 2: Policy Definition
Write a natural language policy that describes what Omni should verify. This is the core of your workflow — it tells the AI agent what to check, what to extract, and how to make decisions.Your policy should include:
The type of documents you expect
Specific verification steps (what to check)
Pass/fail criteria (what constitutes approval vs. rejection)
Example policy:
Verify the submitted invoice by:1. Extracting the vendor name, invoice number, date, and total amount2. Checking that all required fields are present and non-empty3. Validating that the invoice date is not in the future4. Cross-checking that line item totals match the stated total amount5. Approve if all checks pass; reject if any critical field is missing or amounts do not match
Step 2: Write your verification policy in natural language. Optional policy documents can be uploaded.
Vague policies produce vague results. Be specific about what documents to expect, what fields to check, and what your approval criteria are. See the Policy Writing Guide for detailed best practices.
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Step 3: AI Model Selection
Choose the AI model the workflow will use.
Model
Provider
Description
Gemma4 12B
ARGOS
Open-source model self-hosted by ARGOS
Claude Sonnet 4.5 (Bedrock)
AWS
Latest AI model from Anthropic
Step 3: Choose the AI model the workflow will use.
You do not pick verification engines here. They are selected automatically from the policy, and you can see which ones a workflow ended up with under engines in GET /workflows/:workflowId. For what each engine does, see Engines.
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Step 4: Output Schema
Define the JSON structure for the results you want back. This tells Omni exactly what format to return after analysis — field names, data types, and structure.Example output schema:
Use the Field Builder tab to add fields visually, or JSON Schema Input to edit the schema as JSON. The Schema Writing Guide at the bottom of the step summarizes Draft-07 rules and Omni constraints (see Output Schema).
Step 4 — Field Builder: define fields, types, and descriptions. Use JSON Schema Preview to validate.
Step 4 — JSON Schema Input: edit Draft-07 JSON Schema directly; stays in sync with Field Builder.
We recommend a decision block with a result field, verificationStatus, and a reasons array. The system does not enforce this structure, and the field is user-defined — separate from the top-level system verificationStatus. Matching the system enum (verified / pending_review / rejected) keeps downstream routing simple. See the Output Schema documentation for more details.
After you finish Step 4, Omni shows a confirmation with your playbook ID and a summary of playbook actions.
Align terminology across policy, Item names, and JSON output schema
Whenever you rely on specific technical terms or verification targets (such as document types), use the same wording in all three places below.
Layer
What to keep consistent
Policy
Document names and target labels in your natural-language policy
Item name
The name you give each document when you add it to a profile (e.g. the name field when creating or uploading an Item via the API)
JSON output schema
Field names, descriptions, and any contextual text that refers to those documents or checks
For example, if the policy says you will review a business registration certificate, register the Item under the same label (e.g. the equivalent term your team uses consistently in Korean or English), and use that same term in the output schema where you describe validation for that document. That alignment helps the agent connect policy, uploaded files, and structured results reliably.In a customer-defined JSON output schema, keep descriptions and context for specific values aligned as well. For instance, if a field such as document_validation is true, the surrounding description should refer to the same document type (e.g. business registration) and clarify what “sufficient information” means (company name, address, representative, and other fields expected on that document).
At runtime, whenever you add an Item, set item.name (or the Item name field in the API) to match the policy wording and the terminology used in your JSON output schema. If these diverge, the agent may map documents and fields incorrectly.
Data Objects — Item names shown here (e.g. business registration certificate) should match your policy and output schema.
Once you complete all four steps, your workflow is ready to use. You can open it from the workflow list to manage profiles and analyses, or review policy, engines, and output schema from the workflow configuration view.
Workflow detail — profiles, analyses count, and Workflow modification.
Workflow configuration — Policy definition, selected engines, and JSON output schema (after creation or via edit).
If your use case matches one of the workflow templates (KYB, Invoice, AML, Compliance), start there and customize. It is faster than building from scratch.
Test with real documents
After creating a workflow, run a few test profiles with actual documents to validate that your policy and schema produce the results you expect. Adjust as needed.
Iterate on your policy
Your first policy draft may not be perfect. Review the analysis results, identify where the AI misunderstood your intent, and refine the policy language. Small wording changes can significantly improve accuracy.
Keep output schemas aligned with your systems
Design your output schema to match what your backend or compliance system expects. This eliminates the need for post-processing transformations.