Product data enrichment service
Product data enrichment, built for AI to read.
We fill, verify and structure your product data (attributes, identifiers, variants, offers and policies) in Shopify, your PIM and Seller Central, so ChatGPT, Google AI Mode, Microsoft Copilot and Alexa for Shopping (formerly Rufus) can match, compare and recommend every SKU. Priced per SKU, from 100 to 100,000 products.
Free 10-SKU sample · 5-day turnaround · No commitment
The definition
What is product data enrichment for AI shopping?
Product data enrichment (also called product catalog enrichment, or catalogue enrichment) is turning every buying fact about a product into a structured, validated field, and keeping those fields true everywhere they're read. Material, dimensions, fit, compatibility, GTIN, variant structure, price, availability, shipping and returns: each one a value a machine can match and repeat, not a sentence it has to interpret.
AI shopping assistants choose and describe products from structured data: catalogs, feeds and page markup. A shopper asks “is this real leather?” and the assistant needs material: full-grain leather, not “crafted from buttery Italian hide”. When the fact only lives in prose or a photo, the product is skipped or misdescribed. Enrichment is the forty fields behind the one description.
The full method, with an audit worksheet and governance model, is in the catalog enrichment playbook.
Where AI reads your catalog
Four pipes. One source of truth.
The same product fact is read by four or five systems under different field names. Enriching once and mapping it to each is cheaper than fixing every channel, and it's the only way they stay consistent.
Shopify Catalog
Read by ChatGPT, Microsoft Copilot and Shopify's agentic storefronts
Your Shopify products, categories, variants and metafields are shared to AI channels through Shopify Catalog. Thin fields here are thin everywhere it syndicates.
Merchant Center and page markup
Read by Google AI Mode, AI Overviews and Shopping
Google reads your Merchant Center feed and the Product, Offer and policy structured data on each page, and expects the two to agree.
Amazon product-type attributes
Read by Alexa for Shopping (formerly Rufus) and Amazon search
Each Amazon product type defines its own attributes, often dozens. Answers inside Amazon come from those fields, your bullets and your reviews.
Product feeds and the open web
Read by OpenAI's product feed, Perplexity, Gemini
Approved merchants can send OpenAI a feed directly; assistants with web search also read your pages and what other sites say about the product.
What we fill
The same fact, in every system that reads it.
A cut of the field map we work from. Amazon's attribute names vary by product type, so we pull yours from its template before mapping.
| Fact | Shopify | Amazon | schema.org | Merchant Center |
|---|---|---|---|---|
| Category | Standard Product Taxonomy category | Product type, item_type_keyword | category | google_product_category |
| GTIN (UPC, EAN) | Variant barcode | externally_assigned_product_identifier | gtin | gtin |
| Brand, MPN | Vendor; metafield | brand, part_number | brand, mpn | brand, mpn |
| Color, size, material | Category metafields; variant options | color, size, material | color, size, material | color, size, material |
| Variants | Product with variants | variation_theme, parentage_level | ProductGroup, hasVariant | item_group_id |
| Price, availability | Variant price; inventory | purchasable_offer, fulfillment_availability | Offer price, availability | price, availability |
| Compatibility, specs | List metafield or reference | Product-type specific | isAccessoryOrSparePartFor | product_detail |
| Shipping, returns | Shipping profiles, return rules | merchant_shipping_group | OfferShippingDetails, MerchantReturnPolicy | shipping, return_policy_label |
The full map, with the OpenAI feed's required fields and policy markup notes, is in the playbook.
Service, software or in-house
What catalog enrichment software doesn't do.
A PIM stores and syndicates product data; a generative AI tool writes it fast. Neither decides what your category needs or checks that a value is true. That's the part we do, inside whichever tools you keep.
AI catalog management, with people accountable
How the enrichment pass works.
The same pipeline runs on the free 10-SKU sample and on a 50,000-SKU catalog, just wider.
Start from questions
Site search terms, support macros, return reasons and the questions shoppers ask assistants in your category become the candidate fields.
Score every record
Each field marked present and valid, wrong type or unit, trapped in prose, or missing. Wrong is worse than missing: an agent repeats it with confidence.
Extract, then verify
AI drafts values from your descriptions and documents. A person checks each one against a source. If it can't be verified, it stays empty.
Type it for each channel
Metafield definitions with types and units for Shopify, product-type attributes for Amazon, a mapping table so one fact doesn't become two.
Lock it with a gate
A written category schema (required, recommended, optional) and a publishing gate, so SKU number 1,001 doesn't arrive half-empty.
Shopify catalog enrichment
- Every product in the right Standard Product Taxonomy category, so its category metafields exist
- Category metafields and typed metafield definitions, with units and storefront access
- Variant options, barcodes and SKUs that agree with each other
- Product and Offer structured data that matches the admin and the feeds
Related: metafields vs. a PIM, variant data for AI.
Amazon cataloging
- Product-type attributes filled from the template for each ASIN's product type
- Browse node and item type keyword corrections
- Variation families with the right variation theme
- Backend attributes mapped from the same source as Shopify, so the channels agree
Related: backend attributes, browse tree corrections.
Example
What enrichment changes on one product.
An illustrative product page, before and after. These are the fields we fill; what it does for surfacing depends on your catalog and category.
Before
- 4 attributes filled
- Basic Product schema only
- No image alt text
- 78-word description
After
- 22 attributes filled
- Product, Offer and variant markup that matches the feed
- Alt text on all 6 images
- 247-word description that answers the category's questions
Scope tiers
Pricing scales with your catalog.
Priced per SKU by catalog size. The Setup + Catalog Sprint starts from $4,000; we quote a fixed per-SKU rate after the free sample.
See the full breakdown (Readiness Audit, Catalog Sprint and the Commerce GEO retainer) on the pricing page.
Starter
First-time enrichment · under 500 SKUs
- Attribute fill-in
- Schema + descriptions
- Image alt tags
- Single platform
5-day turn on 100 SKUs
Growth
Catalogs 500–5,000 SKUs
- Everything in Starter
- Cross-platform feeds
- AI-consumption rewriting
- Variant and GTIN clean-up
10-day turn on 1,000 SKUs
Enterprise
Catalogs 5,000–100,000 SKUs
- Everything in Growth
- Source-of-truth PIM integration
- Automated sync
- Monthly attribute audit + dedicated lead
Custom cadence
How it works
Four steps to an AI-ready catalog.
Sample
Send us 10 SKUs. We enrich them free in 5 business days. You see the work before you commit.
Scope
We map your catalog, normalise your taxonomy, and quote a fixed per-SKU rate. No surprises.
Enrich
Batch delivery with daily progress updates. We work in your PIM, Shopify admin or a shared sheet.
Sync
Feeds and markup updated from one source of truth. Monthly audit on Enterprise.
What you get back
- A before/after table per SKU: field, old value (or "prose only" or empty), new typed value and its source
- The category schema: required, recommended and optional fields, units and allowed values
- Publish-ready output: Shopify metafield namespaces and keys, Amazon attribute columns, feed mappings
- A gap list: facts that need a supplier answer or a warehouse measurement before they can be filled
- A plain go/no-go read on whether each category is ready for agent channels
What we won't claim
Enrichment makes a catalog able to carry structured answers. It doesn't guarantee that any assistant recommends you, and a 10-SKU sample doesn't prove a citation rate or a ranking change; those need live measurement over time. We measure validation, coverage and a fixed prompt panel, and say what we see.
See how often assistants name brands in ten categories in the AI Shelf Index.
Catalog for AI
From the playbook.
The guides we work from. Free to read, written for operators.
Product Catalog Enrichment for AI Shopping: The Playbook
What catalog enrichment is, where AI shopping reads product data, a field map across Shopify, Amazon, schema.org and Merchant Center, an audit worksheet, governance, and build vs. buy.
Sujan Bhuiyan · · 20 minHow We Enrich 10 Sample SKUs Before You Commit to Catalog Work
A free 10-SKU pass is not a teaser PDF. It is a worked before/after on your messiest hero products so you can decide whether full enrichment is worth it.
Sujan Bhuiyan · · 10 minDesigning a Product Attribute Schema AI Can Use
Filled fields decay; the schema endures. A field guide to the per-category contract that keeps a catalog AI-ready long after the enrichment project ends.
GigaCommerce Team · · 12 minAlso useful
Related reading and next steps.
FAQ
Questions we get a lot.
Still unsure? Ask an operator, not an SDR.
Book a 20-minute callWhat is product data enrichment?
It's putting every fact a shopper asks about (material, size, fit, compatibility, identifiers, price, availability, returns) into a named, validated field instead of leaving it in a paragraph or a photo, and keeping those fields true across every channel that reads them. Product catalog enrichment is another name for it, and SKU enrichment is the same work counted per variant.
Can I see what you'd do before paying?
Yes. The free 10-SKU sample is the point. Send us 10 product URLs and we return the same 10 enriched, with the category schema and a gap list, in five business days. No sales call required.
Is this software or a service?
A service. We use AI to draft values and people to verify them, and we work inside the tools you already have: Shopify metafields, your PIM (Akeneo, Salsify, Plytix and similar) or Seller Central. If you're choosing catalog enrichment software, the build vs. buy table in our playbook lays out when a PIM earns its cost.
Do you use AI to write the data?
To draft it, yes; to decide it, no. A model extracts candidate values from your descriptions, spec sheets and supplier files, and a person checks each one against a source before it's published. Anything that touches safety, compatibility or a claim is checked by hand. We don't fill a field to raise a fill rate.
How much does product data enrichment cost?
It's priced per SKU, quoted after the free sample, because the work depends on your category and how much source data exists. The Setup + Catalog Sprint starts from $4,000; see pricing for the published floors.
We already have a PIM. Can you work in it?
Yes. We work in your PIM under your existing user accounts, with an audit trail. We don't replace your tooling; we fill it and leave the schema and publishing rules behind.
Do you do Amazon catalog management too?
Yes: product-type attributes, browse node and item type corrections, variation families and backend attributes, mapped from the same source as your Shopify data so the two don't drift. Listing copy, A+ and ads sit with our Amazon service.
How do I know it worked?
Three measures at handoff: structured-data validation pass rate, attribute coverage per SKU before and after, and how often your products are named for a fixed panel of shopping prompts in your category. We won't promise a citation rate; no one can.