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End-to-End Proton PIM Workflow Overview

This article walks through the full lifecycle of product data in Proton PIM—from initial upload to use.

Step 1: Add Products in your “Import” Tab

Products enter Proton PIM in one of two ways:

  • Single product creation for small updates or testing

  • Bulk CSV upload for larger catalogs

At minimum, each product must include:

  • Product ID

  • Product description

Additional data improves enrichment quality.

Step 2: Enrichment Begins

Once products are added:

  • Enrichment starts automatically

  • Multiple products are processed in parallel

  • Progress can be tracked on the Jobs page

During this step, Proton PIM generates structured product data using available inputs and trusted sources.

Step 3: Review Enriched Products

After enrichment completes, products appear in the Review Queue.

Here, users can:

  • Compare original input data with enriched results

  • Verify key identifiers like part numbers and specs

  • Edit or correct any fields

  • Approve products when ready

This review step ensures accuracy and gives your team full control over what enters the catalog.

Step 4: Approve & Add to Catalog

When a product is approved:

  • It moves into the Catalog

  • It becomes part of your official product data set

  • All changes and approvals are tracked for visibility

The Catalog serves as your centralized source of truth.

Step 5: Manage & Maintain Product Data

From the Catalog, teams can:

  • Search and filter products

  • Make updates as data changes

  • Maintain consistent product information over time

This allows product data to stay accurate as catalogs evolve.

Step 6: Export or Integrate Product Data

Approved product data can be:

  • Exported via CSV

    • Used in other Proton tools

  • Integrated with external systems like ERPs, CRMs, or ecommerce platforms

Because all data flows from the Catalog, downstream systems receive consistent, trusted product information.

Putting It All Together

The Proton PIM workflow is designed to:

  • Scale product onboarding

  • Improve data quality

  • Maintain governance and control

  • Reduce manual cleanup over time

Each step builds on the last, ensuring product data is accurate before it’s shared or activated elsewhere.