AI-generated descriptions in iCat are designed to help contents teams work faster without compromising billing accuracy.
When AI Descriptions Are Applied
AI descriptions in iCat are never applied over human-entered data.
They are added only when:
No description is entered during packout, or
An admin intentionally runs AI descriptions post-packout
Manually entered descriptions always take precedence. AI is used to fill gaps, not undo field work — keeping your team fully in control while still benefiting from AI-assisted efficiency.
Is AI Expected to Be Perfect?
Yes — when it matters.
Accuracy expectations in iCat are determined by how a description is used.
If a description impacts estimating, billing, or reporting, AI is held to a near-perfect standard.
Where AI Must Be Accurate (and Is Held to That Standard)
Billable & Accuracy-Critical Items
This category includes any item that is not billed via flat-rate box pricing, including:
Single, non-boxed items
Individually inventoried items placed into a regular box but billed separately
Non-Salvage inventory (items requiring reliable identification for documentation, valuation, or workflow clarity)
For all items in this category:
AI descriptions are expected to be accurate and dependable
With a clear photo of a single item, current accuracy is measured at 99%+
These descriptions directly impact estimating, billing, reporting, and documentation
Precision is mandatory — not optional
Core Rule (Summary)
If an item is not in a cleaning box and not inventoried for storage-only / Return-As-Is (flat-rate by box type), the description must be accurate — and it is.
Where AI Does Not Need to Be Perfect
Cleaning Boxes
For cleaning boxes, AI is intentionally used in a more approximate way.
The goal is to:
Provide a general understanding of what’s in the box
Improve searchability and packback context
Support documentation — not billing
Important clarifications:
Billing is based solely on the box type selected
Box descriptions have zero impact on billing
Descriptions are informational only
Photos remain the primary source of record and evidence
Because these descriptions do not affect billing, item-level precision is not required in this workflow.
Recommended Best Practice: Human Review
While AI descriptions in iCat are held to a near-perfect standard where accuracy matters, human review is still a recommended best practice.
This is not because AI is unreliable — but because input quality matters.
Common factors that can affect results include:
Blurry or poorly framed photos
Multiple items captured in a single image
Obstructed or partially visible contents
For this reason, iCat workflows are designed with the expectation that teams will review AI-generated descriptions as part of a standard packout or QA process, especially for billable items.
A quick review helps ensure:
Descriptions correctly reflect what was actually inventoried
Edge cases caused by photo quality are caught early
Estimating and billing remain accurate and defensible
AI accelerates the work — it does not replace accountability.