Prototype Use Case: Policy Intake Workflow

Why This Example Exists

This is not a claim about a live customer deployment. It is a representative blueprint for an insurance workflow we are often asked about: a team receives unstructured requests by email or portal, needs to classify them, extract fields, and decide what can be handled automatically versus what needs review.

The real question is usually not whether automation is possible. It is whether the team can safely automate part of the work while keeping a human review path for exceptions.

Example Approach

Phase 1: Process Review

We would start by mapping the workflow, understanding request types, and identifying the points where automation is safe and where it is not. Typical buckets might include:

• Simple requests that are easy to classify and route
• Complex requests that require a reviewer
• Ambiguous or incomplete requests that need follow-up

We would then review a sample of historical requests to judge whether the data is sufficient for a pilot.

Phase 2: Workflow Prototype

In a prototype, we might combine:

1. Capture: email, form, or portal submissions into a single intake point
2. Classification: route each request to a processing path
3. Extraction: turn unstructured text and attachments into structured fields
4. Validation: check the extracted data against the system of record
5. Routing: send simple cases forward and queue uncertain ones for review

Phase 3: Human Review Interface

The review tool matters as much as the model. For this kind of workflow, the UI should make it easy to see the source request, the extracted data, and the reason a case was flagged for review.

Useful features include:

• Side-by-side source and extraction view
• Highlighting of uncertain fields
• Approval, edit, and escalation actions
• Audit trail for later review

What We Would Measure

For an actual pilot, we would measure:

• Processing time per request
• Percentage of requests routed automatically
• Review workload for staff
• Error rate on extracted fields
• User confidence in the workflow

Why This Blueprint Matters

The goal is not full automation for its own sake. The goal is to reduce repetitive work where it is safe, keep humans in the loop where it matters, and build a workflow that can be expanded after a pilot proves value.

Typical Technical Building Blocks

• Ingestion via email, form, or API
• LLM or rules-based extraction depending on the problem
• Validation against the system of record
• Internal reviewer dashboard
• Logging and audit trail

What We Learn from Projects Like This

The most important decision is usually not the model. It is whether the workflow is worth automating, where human review belongs, and what success actually looks like.