Organize a large inventory into categories
Build a clear classification system and get suggestions for catalog placement, with ambiguous items kept for review.
How this works
Build a clear classification system and get suggestions for catalog placement, with ambiguous items kept for review.
Classify inventory against a governed taxonomy rather than inventing a new category for every unusual item. Preserve the distinction between a suggested classification and the catalog owner’s confirmed category.
Before you start
Defined operating scope
A large inventory needs consistent categories despite ambiguous product descriptions.
Required source material
Obtain Product list; Descriptions and attributes; Classification rules. If connectors are unavailable, use authorized files with the same fields.
Decision and exception rules
Required distinguishing attributes are absent. Request those attributes and leave the item unclassified.
Prepare the inputs
- item_records
- Required
IDs, descriptions and attributes
Each item needs a stable key and available specification.
- taxonomy
- Required
Category IDs, hierarchy and definitions
Use a versioned set with explicit exclusions.
- classification_rules
- Required
Attribute requirements and tie-breakers
Define when multiple categories are permitted.
- review_state
- Required
Existing labels and reviewer decisions
Do not overwrite confirmed classifications silently.
Implementation, step by step
Establish the working record
- Confirm the selected records, effective versions and responsible owner.
- Check item_records, taxonomy, classification_rules, review_state before beginning. Record the source version and the selected scope; an unavailable value stays unknown rather than becoming an inferred fact.
Checkpoint: The input record identifies the selected item_records and any missing required fields.
Define categories and edge cases
- Define category boundaries using representative edge cases, including items that must remain unclassified.
- Verify each category exists in the selected taxonomy version.
Checkpoint: A saved intermediate result for: Define categories and edge cases.
Store items and the taxonomy in a catalog database
- Store item attributes and taxonomy version together so later changes can be reviewed rather than hidden.
- Review boundary cases and confirm that missing attributes lead to uncertainty rather than invented specifications.
Checkpoint: A saved intermediate result for: Store items and the taxonomy in a catalog database.
Get AI category suggestions with explanations
- Suggest a category with the decisive attributes and alternative candidates; do not use a confidence number as evidence.
- Verify each category exists in the selected taxonomy version.
Checkpoint: A saved intermediate result for: Get AI category suggestions with explanations.
Review ambiguous items and assemble collections
- Route ambiguous suggestions to catalog owners and build collections only from confirmed or explicitly permitted provisional labels.
- Review boundary cases and confirm that missing attributes lead to uncertainty rather than invented specifications.
Checkpoint: A saved intermediate result for: Review ambiguous items and assemble collections.
Package the result and unresolved work
- Check ambiguous items and keep AI suggestions separate from confirmed values.
- Classifications are organizational suggestions, not product safety certifications.
Checkpoint: A catalog with agreed categories and a queue of ambiguous records. Unresolved items remain visible in the review record.
Decisions along the way
Required distinguishing attributes are absent.
Request those attributes and leave the item unclassified.
Two categories fit under the stated rules.
Show both with the conflicting rule and ask the taxonomy owner.
A new taxonomy invalidates confirmed labels.
Create a migration review queue instead of silently relabeling inventory.
What you will produce
inventory-classification-database-result.json
JSONA catalog with agreed categories and a queue of ambiguous records. Label proposed and completed states separately.
Include: item_id · taxonomy_version · suggested_category · decisive_attributes · alternatives · confirmed_category · status · evidence_reference · review_note
inventory-classification-database-review.md
MarkdownKeep the evidence and unresolved decisions needed to apply this result, including exceptions involving review_state.
Include: source_reference · question · owner · next_action
Check the result
- Verify each category exists in the selected taxonomy version.
- Review boundary cases and confirm that missing attributes lead to uncertainty rather than invented specifications.
- Inspect item_records and taxonomy in both the original and output; confirm that record identity and interpretation survived the transformation.
- Check ambiguous items and keep AI suggestions separate from confirmed values.
When something goes wrong
Required distinguishing attributes are absent.
Request those attributes and leave the item unclassified. Retain the affected evidence and recovery status.
Two categories fit under the stated rules.
Show both with the conflicting rule and ask the taxonomy owner. Retain the affected evidence and recovery status.
A new taxonomy invalidates confirmed labels.
Create a migration review queue instead of silently relabeling inventory. Retain the affected evidence and recovery status.
A worked example
SYNTHETIC EXAMPLE · NOT A REPORTED BUSINESS RESULT
Request
Organize a large inventory into categories for the following bounded example; prepare the result for review.
Inputs
Synthetic example: Taxonomy separates indoor and outdoor lamps by weather rating. Item I-4 says portable lamp but has no rating; I-5 has an approved outdoor rating.
Expected result
Synthetic expected result: I-4 remains unclassified with weather rating requested. I-5 receives an outdoor suggestion linked to its rating; confirmation status remains separate.
Run a small pilot
Start small
Trial a representative batch containing the ordinary case and the exception demonstrated above.
Record the baseline
Record current preparation time, review effort and correction types for the same records.
Agree success criteria
Agree acceptable review and error limits; measure reviewed classification accuracy; ambiguous items correctly sent for review.
Stop or revise
A new taxonomy invalidates confirmed labels. Pause affected records and apply this recovery: Create a migration review queue instead of silently relabeling inventory.
Keep the scope clear
- Classifications are organizational suggestions, not product safety certifications.
- Keep original descriptions and confirmed labels recoverable during taxonomy changes.
Check before using
Check ambiguous items and keep AI suggestions separate from confirmed values.
Business activities
- Manage product catalogs and storefronts
FROM A RECIPE TO YOUR WORKFLOW
Bring the company context together.
This workflow needs reliable inputs before it needs a better prompt. Moat8 Vault brings working information into a readable archive for the AI tools you use.
For this recipe: Product list · Descriptions and attributes · Classification rules
Start with one recurring task, agree which sources to use, and review the first result with your team.
Discuss this workflow