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Your policy is the most important part of an Omni workflow. It tells the AI agent exactly what to check, how to evaluate documents, and when to approve or reject. A well-written policy produces accurate, consistent results. A vague one produces unreliable output. This guide covers the principles of effective policy writing, provides templates for common use cases, and explains how your policy language influences engine selection.

What Makes a Good Policy

Good policies share four characteristics:
Omni’s NLP Workflow Generator parses your policy to determine which engines to activate and how to orchestrate them. The more precise your language, the more accurate the engine selection and execution.

Policy Structure Template

Every policy should follow this four-part structure:
You do not need to use these exact section headers. What matters is that your policy covers all four areas: objective, inputs, steps, and decisions. Omni parses the meaning, not the formatting.

Examples by Use Case

Engines activated: Text Verifier - Glove, AML Search - Person
Engines activated: Text Verifier - Glove
Engines activated: Text Verifier - Glove
Engines activated: Text Verifier - Glove (for name extraction), AML Search - Person (for screening)

Common Mistakes

Avoid these pitfalls when writing policies:
Problem: Omni does not know what to verify. “Verify the document” gives no information about which fields to check or what constitutes a pass.Fix: Be explicit about what to check.
Problem: Without clear approve/reject rules, the AI agent has to guess what constitutes a passing result.Fix: Always end your policy with explicit decision criteria.
Problem: Omni performs better when it knows what type of document to expect. Without this context, extraction accuracy can suffer.Fix: Specify expected document types at the beginning of your policy.
Problem: Putting too many unrelated verification tasks into one policy makes it hard for the AI agent to execute cleanly. Results become less reliable.Fix: If your process involves fundamentally different types of checks (e.g., invoice validation AND employee background screening), split them into separate workflows.

Policy and Engine Mapping

Omni’s NLP Workflow Generator analyzes your policy text to suggest which engines to activate. Understanding this mapping helps you write policies that trigger the right engines.

AML Search - Person

This engine is suggested when your policy contains keywords related to sanctions and watchlist screening. Trigger keywords:
  • “AML”, “anti-money laundering”
  • “sanctions”, “sanctioned”
  • “watchlist”, “watch list”
  • “screening”, “screen against”
  • “PEP”, “politically exposed”
Example phrases that activate AML Search:

Text Verifier - Glove

This engine is suggested when your policy describes document analysis, data extraction, or field validation tasks. Trigger keywords:
  • “extract”, “extraction”
  • “verify”, “validate”, “validation”
  • “check”, “compare”, “cross-check”
  • “match”, “consistency”
  • “present”, “missing”, “required fields”
Example phrases that activate Text Verifier:
Both engines can be activated simultaneously. For example, a KYB policy that mentions “extract company details” (Text Verifier) and “screen directors against AML watchlists” (AML Search) will correctly suggest both engines. You can always manually override the suggestions in Step 3 of workflow creation.

Quick Reference

What’s Next?

Creating a Workflow

Put your policy into action by creating a workflow.

Workflow Templates

Start with a pre-built template and customize it.