Shoppeal
Reviewing an AI-assisted exception recommendation

Solution Blueprint

Exception Management

Operations, quality, and business-unit leaders dealing with recurring exceptions that currently require someone to dig through multiple systems to resolve.

The Problem

Every operational system has a happy path, and a set of cases that fall outside it. When something doesn't match the expected pattern, someone has to investigate, decide what it means, and coordinate a response across tools. This is the core of our Exception-to-Action Engineering method: we build the layer that gathers context, applies your decision rules, and recommends the right action, with a person approving before anything consequential happens.

Our Approach

Handle the cases your existing systems can't resolve automatically.

Understand

We map the exception, the systems it touches, and the baseline cost of handling it today.

Decide

We document the decision rules and build the system that applies them and drafts a recommendation.

Approve

A person reviews and approves every recommendation before it becomes an action: no autonomous execution by default.

Measure

We track the result against the baseline: time, accuracy, and business impact.

The Outcome

Recurring exceptions get a consistent, documented recommendation with a human approving every action, measured against the original baseline, with controlled execution (Phase 2) available once the pilot proves reliable.

What You Get

Deliverables

  • 01An exception map: what triggers the case, who handles it today, and what it costs
  • 02A decision rulebook that encodes how the exception should be evaluated
  • 03A working recommendation system with a human-approval step before action
  • 04A measurement report comparing the new workflow against the documented baseline

Dealing with this in your operation?

Tell us what's happening. We'll help you figure out whether this is the right starting point.