Attribute Enrichment
AI-powered product attribute generation, validation, and normalization at catalog scale
Your catalog is incomplete. Your shoppers already know it.
Missing colors. Inconsistent sizing. Vague descriptions. Most product catalogs have thousands of attribute gaps — and every one of them is a search filter that doesn't work, a recommendation that misfires, and a shopper who leaves empty-handed. MXP Attribute Enrichment uses Gemini AI to fill those gaps automatically, at catalog scale, without putting the burden on your merchandising team.
The business problem it solves
Incomplete and inconsistent product attributes are a silent killer of search performance. A shirt missing its color won't surface in a color filter. A jacket tagged "Blck" instead of "Black" breaks normalization. A product with no description has nothing for the search engine to match against. These aren't edge cases — in most catalogs, they're the norm. And fixing them manually, product by product, is not a viable strategy at scale.
How MXP is different
AI that generates and then checks itself — MXP uses a two-phase enrichment pattern. Phase 1 generates candidate attribute values from product data using Gemini AI. Phase 2 independently validates those values in a separate Gemini call — an "AI checks AI" approach that catches hallucinations before they reach the catalog. Values that don't pass validation are flagged for human review, not published automatically.
Confidence-based publishing — every AI-generated value carries a confidence score: HIGH, MEDIUM, or LOW. High-confidence values publish automatically. Lower-confidence values wait in a review queue for a merchandiser to approve, reject, or edit. Teams get the speed of automation without surrendering quality control.
No approval bottleneck — enrichment runs overnight without requiring anyone to sign off on every change. Merchandisers intervene only when they want to — not as a mandatory step in every product update.
Full coverage of catalog needs — MXP enriches more than just attributes:
| Capability | What it delivers |
|---|---|
| Attribute generation | Fills in missing values — color, size, material, fit, and any custom attribute |
| Attribute validation | Corrects inaccurate values — wrong colors, misclassified brands, inconsistent sizing |
| Attribute normalization | Standardizes format across the catalog ("BLK", "black", "Black" → "Black") |
| Description generation | Creates rich, search-optimized product descriptions from existing attributes and images |
| SEO generation | Produces keyword-rich titles, meta descriptions, and alt text for organic discoverability |
| Marketing copy | Generates taglines and feature highlights tailored to placement context |
Per-tenant, per-category control — different product types need different attributes. Backpacks, olive oils, and apparel each have their own enrichment configuration. MXP can automatically identify the most significant attributes for each product type from catalog statistics — eliminating manual discovery entirely.
What this means for your customers
- Search filters become reliable — because the attributes powering them are complete and consistent
- Merchandisers spend time on strategy, not data cleanup
- New products are enriched automatically on their first indexing run — no manual processing required
- SEO improves as product descriptions and metadata become richer across the entire catalog
- Quality stays high — the two-phase validation pattern means AI errors are caught before they go live
In practice
A fashion retailer's catalog has 40,000 products. 12,000 are missing a color attribute, 8,000 have inconsistent sizing labels, and 5,000 have no product description. After one MXP enrichment run: 9,500 color values are published automatically with HIGH confidence; 2,500 go into the review queue. Sizing is normalized across all 8,000 products. Descriptions are generated for all 5,000 products and queued for a final merchandiser check before publishing. The color facet starts working. Search relevance improves. The merchandiser reviewed 2,500 items — not 25,000.
For a detailed look at how the pipeline works under the hood, see the Data Enrichment Flow architecture page.