Why Multimodal LLMs Miss Products in E-Commerce Catalogs (And How to Fix Your Data)

A 2026 study by GS1 India and Kanvic Consulting assessed 510 product listings across eight e-commerce, quick-commerce, and e-pharmacy platforms. It found that 14% of the products failed to meet an 80% data-quality benchmark, while 27% failed the benchmark for data completeness.

For multimodal LLMs, incomplete product details can make it harder to distinguish items that look alike but differ in size, material, features, or compatibility. When product images and text also provide conflicting information, matching a customer’s query to the right item becomes more challenging.

Product feed optimization helps retailers address these gaps by improving the accuracy and consistency of product attributes, descriptions, images, and variant details.

How Do Multimodal LLMs Miss Products?

Multimodal large language models can process different types of information, including text and images. In e-commerce, this can help a system interpret a request such as “a lightweight waterproof jacket with an adjustable hood” and identify products that match those requirements.

However, understanding the general category is not the same as identifying the exact product. A jacket may look suitable in an image, but its listing might not mention its weight, waterproof rating, or hood features.

Research published in Findings of ACL 2026, AFMRL: Attribute-Enhanced Fine-Grained Multi-Modal Representation Learning in E-commerce, examines this challenge. The researchers explain that multimodal representation models can struggle to distinguish highly similar products using fine-grained details. Their approach uses attributes extracted from product images and text to improve retrieval.

This highlights an important distinction: product feed optimization can make product information more useful, but it cannot automatically solve every retrieval or ranking issue.

A product can be missed at several stages:

• Query understanding: The system misunderstands the shopper’s requirements.
• Data interpretation: Product attributes are incomplete or unclear.
• Retrieval: The relevant product does not enter the candidate results.
• Ranking: The product appears but is placed below less relevant options.
• Response generation: The system retrieves a product but omits it or describes it incorrectly.

Identifying the stage where the failure occurs helps retailers choose the right fix instead of rewriting every product description.

Six Product Data Problems That Make Items Hard to Find

1. Missing or Incomplete Attributes

Product titles often contain only a brand, model, or broad category. That information may be enough for a branded search but insufficient for a detailed request.

For example, a shopper looking for an office chair with adjustable lumbar support may not find the right option if the product listing mentions only “ergonomic office chair.”

Product data optimization should focus on the specifications that distinguish products within each category. Clothing listings may need fabric, fit, and size information, while electronics may require model numbers, dimensions, and compatibility details.

Start by checking which attributes shoppers use to compare products. Then identify missing fields in the catalog and prioritise the products that receive the most search traffic or have the greatest commercial importance.

This is a practical starting point for how to optimize product data for AI: make the information that determines product relevance explicit rather than expecting a model to infer it.

2. Images and Descriptions Do Not Match

Multimodal systems can use both visual and textual information, so inconsistencies between them can complicate product matching.

Imagine a listing that describes a black backpack but displays a navy-blue version. Another listing may show a three-piece kitchen set while its description refers to a single item. The information does not clearly represent what the customer will receive.

E-commerce product data should describe the same product shown in the images. Check that the correct image is associated with each SKU, variant, and bundle. Confirm that product titles, descriptions, and specifications reflect the actual item.

As part of product feed optimization, retailers should also check whether image URLs work, images are accessible to the relevant systems, and product variants are connected to the correct media.

3. Low-Quality or Unhelpful Images

A product image may technically be present but still provide little useful information. Blurry photos, thumbnails, heavy cropping, and images that hide important features can make visual identification harder.

The right image requirements depend on the product category. Furniture listings may benefit from multiple angles and dimension diagrams, while clothing listings need clear views of the garment and its relevant details.

Review image quality alongside textual information rather than treating the two as separate tasks. Product catalog optimization should ensure that every image represents the correct item and adds useful information.

Google’s product data specification identifies low-quality images and conflicting product information as issues that can affect the display of Shopping ads and free listings. These are Google-specific requirements, but they illustrate why accurate, accessible product assets matter.

4. Product Language Does Not Match Search Language

A retailer may describe a product using internal terminology that customers rarely use. For instance, a listing titled “portable hydration vessel” may be harder to match with a query for a “reusable water bottle.”

The solution is not to add every possible synonym to a product description. Instead, use clear product names and explain the features customers are likely to search for.

Review site-search queries, customer support questions, product reviews, and category filters to identify gaps between catalog language and customer language. Use those findings to improve descriptions without adding unsupported claims.

This form of AI search optimization helps make product information more understandable and relevant to real customer needs.

5. Incorrect Variants and Product Relationships

A product may be available in several colours, sizes, capacities, or configurations. If the catalog does not distinguish these options clearly, a system may identify the right product family but return the wrong variant.

For example, a shopper searching for a 1-litre insulated bottle should not receive a 750 ml version simply because both share the same product name.

Each purchasable variant should have a stable identifier and accurate variant-specific attributes. Its price, availability, image, and product URL should correspond to the correct option.

OpenAI’s product feed documentation specifies that product feeds should represent purchasable items or variants with stable identifiers. Retailers preparing data for AI shopping systems should follow the requirements of the specific platform they want to support.

6. Duplicate and Outdated Records

Duplicate products, discontinued items, outdated prices, and conflicting stock information can undermine the reliability of search results.

A product might be marked as available in one data source but unavailable on the website. Two listings may describe the same item using different identifiers, making it harder to maintain a consistent record.

A strong product catalog optimization routine should identify duplicates, remove obsolete information, and keep product pages, feeds, and inventory systems aligned.

These checks are also part of product feed optimization. However, if the catalog is accurate and the system still fails to retrieve the product, retailers need to investigate indexing and retrieval rather than continuing to edit the listing.

What Do Search Queries and Consumer Surveys Tell Us?

Search queries provide a practical way to identify missing product information. Rather than testing only product names, use queries that reflect how people describe their needs.

Examples Include:

• Lightweight waterproof jacket with detachable hood.
• Reusable water bottle that fits a car cup holder.
• Office chair with adjustable lumbar support.
• Laptop charger compatible with a specific laptop model.
• Black running shoes in size 9.

For each query, check whether the catalog contains the relevant attributes and whether the search system returns the correct product. Record the missing details and use them to prioritise data improvements.

Consumer research also demonstrates why product information deserves attention. Syndigo’s 2025 State of Product Experience report surveyed more than 8,500 consumers globally.

It found that 44% had abandoned a purchase because they could not find sufficient product information online, while 75% said incomplete or inaccurate product information could create a negative opinion of a brand.

These figures measure consumer behaviour, not the percentage of products missed by multimodal LLMs. Still, they show that incomplete product information can affect shoppers even before an AI system enters the process.

For retailers, the practical takeaway is to use search-query analysis and customer feedback together. Product feed optimization should address information gaps that affect both product matching and customers’ ability to make informed decisions.

How to Diagnose Whether the Problem Is Data or Retrieval

Before changing thousands of product records, test a representative sample. Choose products from important categories and run queries that test different kinds of matching.

Next, determine where the product disappears.

If an exact SKU search works but a query using product attributes fails, inspect those attributes and the system’s ability to retrieve them.

If the product never appears in the candidate results, investigate indexing, filters, identifiers, and retrieval settings.

If it appears but ranks poorly, assess the ranking logic. If the returned product is correct but its details are wrong, investigate the data used to generate the response.

Teams can track three common retrieval metrics:

• Recall@K: The proportion of relevant products found within the first K results.
• Precision@K: The proportion of the first K results that are relevant.
• Mean Reciprocal Rank (MRR): How highly the first relevant result appears across test queries.

These metrics help teams understand whether a change improves retrieval instead of relying on assumptions about AI visibility.

They also provide a measurable basis for how to improve product visibility in AI search. Track the results before and after each change so the team can identify which fixes have the greatest impact.

How to Fix Product Data for Multimodal LLMs

Start by identifying gaps in your e-commerce product listings, from missing attributes to inconsistent images and descriptions. Use these targeted improvements to strengthen product feed optimization and help AI systems match products more accurately.

1. Build Category-Specific Attribute Requirements

Create a checklist of required and useful attributes for each product category. Avoid relying on a generic description template for every item.

For example, a laptop listing may need processor, memory, storage, screen size, and compatibility details. A sofa listing may need dimensions, upholstery material, seating capacity, and assembly information.

Product data optimization should make these details consistent across similar products. It should also distinguish between confirmed specifications and information that is unavailable. Do not fill missing fields with guesses.

2. Validate Product Images and Variant Data

Check that each image matches its product and variant. Confirm that size, colour, capacity, and other variant-specific details agree across the product page and feed.

Use automated checks where possible to flag missing image URLs, duplicate identifiers, empty required fields, and conflicts between the feed and website.

This makes product feed optimization more systematic and reduces dependence on manual checks alone.

3. Keep Product Feeds and Search Indexes Updated

Updating a product page does not guarantee that every system using the catalog will immediately reflect the change. Feeds, caches, and search indexes may update on different schedules.

OpenAI’s product feed documentation explains how structured catalog data helps ChatGPT surface products with accurate pricing, availability, and seller information. Its requirements apply to its own product discovery integrations; other AI platforms may use different mechanisms.

Retailers should check how updates move from their source catalog to each supported search destination. Ensure that changes to titles, images, prices, and availability reach the relevant systems.

4. Optimise E-Commerce Product Listings and Google Merchant Center Feeds

E-commerce product optimization involves improving product titles, descriptions, images, attributes, and variant details so customers and shopping platforms can understand each product accurately. Retailers should use clear product names, include relevant specifications, and ensure prices and availability match the website.

For stores using Google Merchant Center, review product feed diagnostics to identify missing attributes, incorrect identifiers, image issues, and product disapprovals. Keep feed information consistent with the product pages and follow Google’s product data specification.

These improvements can support product discovery across Google’s shopping surfaces. However, better product listings do not guarantee visibility across every AI search platform, so retailers should also test how their products perform in the systems they want to target.

5. Test Changes Against Real Queries

After correcting the data, repeat the original queries and compare the results. Check whether the right product appears, whether the correct variant is returned, and whether the information shown matches the source catalog.

Do not assume that a longer description or more keywords will automatically improve results. The objective of AI search optimization is to make products easier to identify and retrieve based on accurate information.

This testing process helps teams decide how to optimize product data for AI using observed performance rather than generic recommendations.

6. Prioritise High-Impact Products First

Large catalogs can contain thousands of products, so correcting every record at once may be impractical. Start with high-traffic categories, products with strong sales potential, and listings that repeatedly fail important queries.

Track missing attributes, image errors, variant mismatches, and retrieval failures. Fix the most frequent or commercially important problems first, then expand the checks to the rest of the catalog.

This approach makes product feed optimization more manageable and gives teams a clear way to measure progress.

How to Improve Product Visibility in AI Search

Better catalog data is important, but visibility also depends on whether the relevant platform can access and retrieve the product.

For how to improve product visibility in AI search, retailers should focus on four practical areas:

• Make product information complete: Include accurate names, descriptions, identifiers, prices, availability, and category-specific attributes.
• Keep the information consistent: Check that product pages, feeds, images, and variant records describe the same item.
• Follow platform requirements: Use the relevant product feed or structured data specifications instead of assuming that one format works everywhere.
• Measure retrieval performance : Test representative queries and investigate whether failures originate in the data, indexing, retrieval, or ranking.

E-commerce product data should also be maintained as products change. New variants, updated specifications, and changes in availability can make previously accurate records outdated.

For retailers asking how to improve product visibility in AI search, the goal should not be to guarantee a recommendation from every model. It should be to make the catalog accurate, accessible, and structured so supported systems have reliable information to work with.

That is the practical role of AI search optimization: improve the quality of product information, check how it performs in the intended systems, and correct the issues that prevent relevant products from appearing.

Conclusion: How Ingenious Netsoft Can Help

Ingenious Netsoft eCommerce development and integration expertise can help businesses address technical challenges affecting product discovery through:

• Catalog reviews: Identify missing attributes and inconsistent product details.
• Data validation: Check product information, identifiers, and image links.
• E-commerce integration: Improve consistency across websites and connected systems.
• Search testing: Identify missing products and incorrect matches.

Effective Product feed optimization starts by identifying the root cause, whether it involves incomplete product data or issues with indexing and retrieval, before prioritising the right improvements.

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