Catalog Optimization
Best Product Data Enrichment Tools for Shopify Brands (2026)
Compare 9 product data enrichment tools, from PIMs like Akeneo and Plytix to AI tagging tools like Lily AI. Features and pricing checked on each site.
Sakshi Gupta
The best product data enrichment tools fill the attributes shoppers and AI agents use to compare products, like material, fit, use case and category. For a Shopify brand, Nudge Catalog Enrichment comes first, because it enriches the fields AI agents read. PIMs like Akeneo and Plytix store and govern the data; AI tools like Lily AI and Pumice generate it.
This guide is for the Head of Ecommerce or Growth who has a catalog of a few hundred to a few thousand SKUs, thin product data, and a question from leadership about why the brand's products do not show up in ChatGPT answers. It splits the market into two groups that are often confused, PIMs and AI enrichment tools, and compares nine tools on what they do, who they suit, and what they cost.
If you want the strategy behind enrichment rather than a tool comparison, start with our product data enrichment guide. If you want to see where your own catalog stands first, the free catalog scan shows how readable your products are to AI.
What is product data enrichment, and how is it different from a PIM?
Product data enrichment means adding the facts a product record is missing: attributes, categories, tags, use cases and richer descriptions. A record that says "Women's Jacket, Navy, $180" can be listed. A record that also says "recycled nylon, water-resistant, packable, fits true to size, built for commuting" can be compared, and comparison is what an AI answer does.
A PIM (product information management system) is where that data lives. It is the governed record that teams edit, approve and send to channels. Enrichment tools generate or fill the data. Some PIMs now include AI enrichment, and some enrichment tools push into a PIM, which is why the two get mixed up. Our catalog management software guide covers PIM, DAM and feed tools in more depth.
Nudge sits on the enrichment side, aimed at one reader: the shopper's AI agent. It does not replace a PIM. It finds the gaps that stop AI from understanding a product and fixes them in the catalog.
What product fields do AI shopping agents actually read?
AI agents do not look at your page design. They read structured product data: feeds, schema and platform attributes. The clearest public example is OpenAI's product feed spec for ChatGPT, which lists the fields a merchant can send.
Field type | Examples from the ChatGPT feed spec | Why it matters for enrichment |
|---|---|---|
Required for discovery | item_id, title, description, url, brand, seller_name, image_url, availability, price | Without these, the product is not in a useful feed at all |
Variants | group_id, variant_dict | Lets the agent tell sizes and colors apart |
Optional attributes | material, color, size, gtin, mpn, product_category, gender, age_group, dimensions, weight | These are the comparison fields most catalogs leave blank |
Reviews and returns | review and return fields | Answers "is it any good" and "can I send it back" |
The spec asks for a "factual product description" and warns against inventing an MPN to stand in for a missing GTIN. That is a useful rule for any AI enrichment tool: generated values must be true, not plausible.
On Shopify, the equivalent layer is the Standard Product Taxonomy. Assigning a product category gives you category metafields, which Shopify calls product attributes. For a shirt, that includes size, color, fabric and neckline. Most Shopify catalogs have categories assigned loosely and these metafields mostly empty.
This is the gap Nudge Catalog Enrichment targets. Catalog Analysis scans and scores every product for AI readability and checks findings against the live product page, not just the backend record. The platform then assigns Shopify taxonomy categories and writes category metafields in bulk, behind a preview and an approval step.
Which are the best product data enrichment tools for Shopify brands?
Tool | Type | Best for | AI enrichment (per vendor site) | Pricing (per vendor site) |
|---|---|---|---|---|
Nudge Catalog Enrichment | AI enrichment for AI agents | Shopify brands that need products read and recommended by AI | Scores every product for AI readability, writes taxonomy, category metafields, schema and feed fixes after approval | Book a demo |
Akeneo | PIM | Teams that need governed product data across many channels | Text and image enrichment, option to connect your own LLM, agents that flag data gaps | Advanced and Premium by quote |
Plytix | PIM | Small and mid-size brands that want a lighter PIM | 500 free AI credits a month on every plan; unlimited AI is a paid add-on | Priced by SKU count; Shopify connector is a paid add-on |
Salsify | PIM / PXM | Large brands and retailers syndicating to many retailers | Intelligence Suite tasks, Angie assistant, text extraction from packaging and spec sheets | By demo |
Pimberly | PIM | Retailers, distributors and manufacturers with large catalogs | Pimberly AI add-on | From $36,000 a year (50,000 SKUs); AI add-on from $7,500 a year |
Hypotenuse AI | AI content and enrichment | Teams writing product copy at volume | Attribute enrichment from images, web pages, UPC codes or factsheets (Enterprise) | Custom; free trial on Basic |
Lily AI | AI attribution | Brands with large paid shopping budgets | Conversational attributes and keyword-led titles, starting from your Merchant Center feed | Tailored to catalog size, channels and ad spend |
Pumice.ai | AI enrichment API | Marketplaces and teams with heavy supplier onboarding | Categorization to your taxonomy, attribute extraction, PDF catalogs to CSV, deduplication | Custom, credit based |
Vue.ai | AI tagging | Fashion and retail teams tagging from images | Image, text and OCR attribute extraction with thousands of preset tags | By demo |
Features and prices were checked on each vendor's own site on November 11, 2026. Several vendors render prices with scripts or quote only, so confirm before you buy.
1. Nudge Catalog Enrichment
Nudge Catalog Enrichment is built around one question: can an AI agent read this product well enough to recommend it? Catalog Analysis scans every product, scores it for AI readability, and verifies each finding against the live product page. It produces a deployable JSON-LD snippet and a changelog that ties each fix to a score change.
The fixes cover the fields agents use. The platform assigns Shopify taxonomy categories and writes category metafields in bulk, audits Google Merchant Center offers and writes attribute fixes, and, when Merchant Center is not connected, drafts the request instead of guessing. Nothing ships until you approve it. Agentic Protocol Integration connects the catalog to ACP, UCP and Shopify agent endpoints, so agents can recommend and check out natively.
What makes it different from the rest of this list is that enrichment is tied to an outcome you can see. Nudge AI Visibility tracks which products AI answers mention, per prompt and per platform, so you can enrich the SKUs that are missing from answers first.
Limit: Nudge is not a full PIM. It does not replace your system of record, manage digital assets, onboard suppliers or syndicate content to retailers like Target or Walmart. If that is your main problem, start with a PIM below and use Nudge on top of it.
2. Akeneo
Akeneo is one of the best-known PIMs. Its Product Cloud includes PIM, DAM, a Supplier Data Manager and syndication to retailers and marketplaces. AI features listed on its site include text and image enrichment with the option to connect your own LLM, agents that flag gaps and inconsistencies, AI channel mapping and AI extraction of supplier data. Shopify is listed among the commerce platforms it sends content to.
Limit for a DTC brand: Advanced and Premium editions are quote only, and a PIM rollout is a real project. It suits teams with several editors and many channels more than a single-store brand.
3. Plytix
Plytix is a PIM aimed at smaller teams. Its pricing page sets price with a SKU slider from 100 to 25,000+ SKUs and offers Standard, Pro and Enterprise plans, all with unlimited seats. Every plan includes 500 free AI credits a month, and AI Unlimited, feed management and the Shopify connector are paid add-ons.
Limit for a DTC brand: The Shopify connection is an add-on, not a default, and plan prices were not readable on the page we checked, so get a quote for your SKU count.
4. Salsify
Salsify is a product experience management platform used by large brands and retailers. Its AI features include the Angie conversational assistant, an Intelligence Suite of AI task types inside its workflow engine, translation, persona and seasonal content variants, and extraction of structured text from packaging, nutrition labels and spec sheets.
Limit for a DTC brand: Pricing is by demo and the platform is built for enterprise retail syndication. A Shopify-first brand with one storefront may pay for reach it does not use.
5. Pimberly
Pimberly publishes its prices. The Regular plan is $36,000 a year for 50,000 SKUs and 20 users, Pro is $60,000 for 100,000 SKUs, and Corporate is $90,000 for 250,000 SKUs. Pimberly AI is an add-on from $7,500 a year. It targets retailers, distributors and manufacturers.
Limit for a DTC brand: The entry price and SKU limits are sized for large catalogs. A brand with 800 SKUs is paying for capacity it will not need.
6. Hypotenuse AI
Hypotenuse AI started as a product copy generator and now covers enrichment. Its Basic plan includes a product description generator and bulk CSV and XLSX import and export, with a free trial. The Ecommerce Enterprise plan adds attribute enrichment that fills missing details from images, web pages, UPC codes or factsheets, plus taxonomy management, tagging and categorization, and a custom PIM integration.
Limit for a DTC brand: Attribute enrichment, the feature most relevant here, is Enterprise only and custom priced.
7. Lily AI
Lily AI's Lily Max adds conversational attributes, works high-demand search terms into titles and descriptions, and enriches page content and schema. It starts from your Google Merchant Center feed and runs matched-spend tests against a control. It offers a free feed audit, and pricing is tailored to catalog size, channels and monthly ad spend.
Limit for a DTC brand: Pricing is tied partly to ad spend, so it suits brands with large shopping campaigns. Lily AI states plainly that it does not guarantee placement or citation on AI surfaces, which is an honest and fair caveat for this whole category.
8. Pumice.ai
Pumice.ai is an AI enrichment engine delivered through an API. It maps products to your own taxonomy (it claims over 97% accuracy), generates attribute values from keys you define, searches the web to fill gaps in vendor files, converts vendor PDF catalogs to CSV, and deduplicates SKUs. Pricing is custom and credit based.
Limit for a DTC brand: It is built for marketplaces and teams onboarding many supplier SKUs, and it connects to a PIM through an API, so you need someone to wire it in.
9. Vue.ai
Vue.ai's automated product tagging uses NLP, OCR and image recognition to pull attributes from product images, labels and text. It comes with thousands of preset retail tags, supports custom taxonomies, exports to CSV and integrates with PIMs. Fashion, grocery, electronics, home and beauty are listed verticals.
Limit for a DTC brand: Tagging is one solution inside a broader enterprise AI platform, and pricing is by demo.
What is the best AI product tagging tool for an ecommerce catalog?
It depends on where your tags need to end up. If tags feed onsite search and filters, an image-led tagger like Vue.ai or a taxonomy mapper like Pumice.ai fits. If tags need to reach Google Shopping, Lily AI starts from the Merchant Center feed.
If the goal is for AI agents to read and recommend your products, tags need to land in the fields agents read: Shopify category metafields, Product schema and your Merchant Center feed. Nudge Catalog Enrichment writes to those surfaces directly, which skips the export and re-import step most tagging tools leave to you. Our feed management guide explains why a feed tool alone cannot add data that was never recorded.
How do you fill missing product attributes across thousands of SKUs with AI?
To fill missing product attributes across thousands of SKUs with AI, audit what each product is missing, choose the attributes shoppers compare on, generate values from trusted sources, validate them against the live page, and write them back in bulk behind an approval step. Here is the process in order.
Audit per product, not per file. Score every SKU on which comparison fields are empty. Nudge Catalog Analysis does this for AI readability and checks findings against the live product page.
Fix the category first. On Shopify, the product category decides which category metafields exist. A wrong category means the AI fills the wrong attributes.
Pick the attributes that drive comparison. Material, fit, size range, use case and care beat internal codes. Use the optional fields in feed specs like ChatGPT's as a checklist.
Generate from evidence. Pull values from spec sheets, packaging, images and supplier files. Tools like Hypotenuse AI, Pumice.ai and Vue.ai extract from these sources.
Validate before you publish. Check every generated value against the live product and the label. A plausible wrong value is worse than an empty one, because AI answers will repeat it.
Write back in bulk with a gate. Push to Shopify metafields, schema and your feed in batches, with a human approval step. Nudge writes category metafields, JSON-LD and Merchant Center attributes this way.
Measure what changed. Track which products AI answers mention before and after. A readability score is a state, not a sales forecast.
For more on the schema side of step 6, see our guide to catalog enrichment for AI agents.
What is the best AI PIM for a mid-size DTC brand?
For a mid-size DTC brand on Shopify, with a few hundred to a few thousand SKUs and one or two storefronts, a heavy enterprise PIM is usually more than you need. The question is whether you have a governance problem (many editors, many channels, suppliers sending data) or a completeness problem (records exist but are thin).
Your situation | What fits |
|---|---|
Records exist in Shopify but are thin, and AI does not recommend your products | Nudge Catalog Enrichment, working on your Shopify catalog directly |
Many editors and channels, no governed record | A lighter PIM such as Plytix, or Akeneo if you expect to scale channels |
Selling into many retailers with their own content rules | Salsify or Akeneo for syndication |
Large supplier catalogs arriving as PDFs and spreadsheets | Pumice.ai or Akeneo's Supplier Data Manager |
Big shopping ad budget, weak feed attributes | Lily AI |
Many brands end up with both: a PIM as the record, and an enrichment layer that writes the fields AI reads. Nudge works alongside a PIM rather than replacing one.
What happens after your catalog is enriched?
Enrichment makes a product readable. It does not make it convert. When a shopper clicks through from an AI answer, they arrive with a very specific question, and a generic product page often does not answer it.
Nudge Funnels serves a version of the same product URL built for that arrival, on your own domain, and tests it against your default page as control. Agent Funnels give the shopper's agent a machine-legible version of the page that is fact-identical to the original. To see how your catalog reads to AI and what to fix first, book a demo.
Frequently asked questions
What is the difference between PIM software and a product data enrichment tool?
A PIM is the governed record where product data is stored, edited and sent to channels. An enrichment tool generates or fills missing data, such as attributes, tags and descriptions. Some PIMs include AI enrichment, and many brands use both together.
Do Shopify brands need a PIM?
Not always. A single-store brand with a few hundred SKUs can often manage product data in Shopify itself, using the Standard Product Taxonomy and category metafields. A PIM earns its cost when you have many editors, many channels or heavy supplier onboarding.
Can AI fill product attributes accurately?
Yes, when it works from evidence such as spec sheets, labels and images, and every value is checked before it goes live. Generated values that are plausible but wrong get repeated by AI answers, so keep a human approval step in the workflow.
Which product attributes matter most for AI search?
The ones shoppers compare on: material, size, color, fit, use case, dimensions and care, plus accurate category, price and availability. OpenAI's ChatGPT feed spec lists material, color, size, GTIN and product category among its optional fields.
Does product data enrichment guarantee AI recommendations?
No. Enrichment makes a product readable and eligible to be compared. Recommendations also depend on reviews, the sources AI trusts and competing products. Measure enrichment by which products AI answers mention over time, not by a readability score alone.
