Customers find goods online via a new mechanism.
Traditionally, ecommerce professionals pay attention to categories, filters, Google positions, paid shopping feed ads, and onsite search. All these channels are important; however, consumers have started researching products through artificial intelligence assistants that work in a totally different manner.
A customer does not type “best office chair” in the search engine and visit multiple web pages anymore. Instead, he/she asks:
“I require an ergonomic office chair worth less than $500 with adjustable lumbar support, mesh back, and weight capacity over 120 kg.”
This is much more advanced query.
To be able to offer a relevant product in response to such a question, an AI shopping engine must have enough information about items available on the retailer’s website. The name and description may not be sufficient.
AI commerce depends on product catalogs.
Preparing for AI shopping on BigCommerce platforms does not require developing the website anew. A more effective way of getting ready for this challenge is to make the catalog machine-readable while retaining its human-readability.
This means making product data available in the company better.
AI Shopping Changes What a “Good” Product Catalog Looks Like
A typical product page is created to be read by a human who looks at a screen.
They can understand images, titles, icons, specification tables, drop-downs, descriptions, and visual relationships between pieces of information.
The way AI operates is different.
The AI works best when key attributes of the product are easily distinguishable.
Take two entries for the same backpack.
The first entry is as follows:
Adventure Series Pro Backpack
Its description includes phrases like “premium construction” and “designed for modern explorers.”
The second one:
40L Waterproof Hiking Backpack with Laptop Compartment
Its attributes include:
- Capacity: 40L
- Material: Ripstop nylon
- Waterproof: Yes
- Laptop compartment: Up to 16 inches
- Weight: 1.2 kg
- Suitable for: Hiking and travel
- Colors: Black, Green and Navy
Both pages are equally visually appealing to the visual shopper.
An AI engine trying to answer a question “Which waterproof 40L backpack will accommodate my 15-inch laptop?” will receive a lot more useful data from the second catalog entry.
This is how BigCommerce merchants should adjust.
Start by Looking at the Catalog Through a Customer’s Questions
Before switching industries or integrating a new tech solution, consider the queries that buyers have when purchasing a product.
For example, a customer who buys furniture can ask about the following:
- Is it going to fit in my room?
- What material is it made of?
- Is it easy to assemble?
- How many people can be seated?
- Is it appropriate for outdoor use?
A person buying clothes might inquire about the fit, material, size, color, care recommendations, and season appropriateness of the product.
A business-to-business customer might ask about voltage, dimensions, compatibility, pack quantity, certification, operating temperature, manufacturer number, or minimum order quantity.
These queries provide the information that should be available in the catalog.
Since the relevant data is available in the sales representatives’ knowledge, in PDF files, in images, and even in paragraphs without structure, it will not be easy for automated buying software to handle that information.
Thus, the first step to take is quite simple:
Convert buying information into product information.
Businesses working with a BigCommerce development company in India can integrate this step into their existing store development process rather than treating AI readiness as a separate initiative.
Product Titles Need Context
Product naming can be a straightforward task to undertake.
A lot of companies have used names which make sense only if you know the range first.
Examples include names like:
- Aura 2.0
- Heritage Series
- Model XR
They work perfectly in terms of branding, but do nothing to tell us about the product itself.
We need to provide some context to make them useful.
For instance, instead of saying:
- Aura 2.0
We should be saying:
- Aura 2.0 Wireless Noise-Cancelling Headphones
And instead of Heritage Series we should be saying:
- Heritage Solid Oak 6-Seater Dining Table
What we are trying to achieve is not to force as many keywords into product titles as possible.
We need to make sure the product name can stand alone in terms of context.
Product Descriptions Should Explain, Not Advertise
One of the main problems in the catalog is ambiguous copy of the products.
Copy with phrases like “premium quality,” “perfect for modern lifestyle,” or “ideal choice” is rather promotional than informative.
It becomes more visible because of the AI-assisted shopping.
For instance, what if the question of the customer is:
“Will this frying pan work on an induction cooker?”
The phrase “premium cookware for every kitchen” does not give the answer.
But a helpful phrase will be:
“This 28 cm stainless steel frying pan works on induction, gas, electric and ceramic cooktops. It is made of three layers of material and provides even heating of the cooking area. The stainless steel handle of this pan is oven safe.”
It gives the necessary information to work with.
In BigCommerce, while improving a catalog, it is necessary to consider real questions of the customers.
Don’t Leave Important Details Buried Inside Descriptions
Description provides context, but important specifications must never be confined to mere paragraphs.
Think about a description of an outdoor jacket like:
“Constructed of recycled polyester with a waterproof outer layer, insulation, and adjustable hood for wet and cold weather.”
Important information is embedded in this sentence.
A stronger catalog also stores:
| Product Detail | Value |
| Material | Recycled polyester |
| Waterproof | Yes |
| Insulated | Yes |
| Hood | Adjustable |
| Fit | Regular |
| Available Sizes | S to XXL |
| Suitable Conditions | Cold and wet weather |
It makes the data easier to process with filters, searches, comparisons, feeds, and AI-based product matching.
A software development firm specializing in BigCommerce development in India can provide insight into which type of data management will work best for your needs: native product fields, custom fields, options, variants, metafields, external product systems, or integrations.
The key is consistency.
Consistency Becomes More Important as the Catalog Grows
A small catalog can survive a little inconsistency.
A catalog containing 50,000 products cannot.
Imagine that different teams describe the same material as:
- Stainless Steel
- stainless steel
- SS
- S/S
- 316 SS
Or dimensions appear as:
- 20 inches
- 20″
- 50.8 cm
- 508 mm
A person will be able to understand the meaning of those values.
However, automated systems will first need to normalize them to compare them reliably.
That is why there needs to be a data standard for large BigCommerce catalogs.
Choose what names the colors, materials, dimensions, capacities, categories, and other significant features will have.
This is also true when data is coming from suppliers.
When you have five different sources and they give you five different naming schemes, importing all of them in BigCommerce will lead to a catalog issue that later impacts searches, filters, feeds, and AI discovery.
Businesses that outsource catalog integration from BigCommerce developers in India should add normalization guidelines in the integration requirements.
Variants Need More Attention Than Many Brands Give Them
AI-based search engines for shopping do not just have to know whether the product is there.
They have to know if the specific one that the customer is looking for is there.
Consider that a consumer asks,
“Do you carry this running shoe in navy, size 10?”
The parent product might be there, but the navy, size 10 product might not be available.
Variant information can include:
- SKU
- Size
- Color
- Price
- Inventory
- Weight
- Image
- Availability
- Product-specific identifiers
The exact requirements will vary based on the catalog.
In B2B stores, variants could be pack sizes, voltage, dimensions, finish, or case quantities instead of standard color and size varieties of retail items.
The catalog must mirror how customers buy the product.
If the existing BigCommerce variant system has become cluttered, then that would be worth evaluating before launching a new store channel.
Product Identifiers Help AI Understand Exactly What Is Being Sold
Product identifiers may not be exciting, but they matter.
Depending on the product category, a catalog may use:
- SKU
- GTIN
- UPC
- EAN
- ISBN
- MPN
- Brand
- Manufacturer part number
These identifiers will help us differentiate between one product and another, especially since there are several channels where products can be purchased.
“Wireless Sony headphones” can be referring to a wide range of products.
It is the manufacturer and model number that will give us the precise product.
The Google product documentation also suggests providing the appropriate identifiers as well as any product information we have in case they exist.
Our main task here is simple; provide accurate identifiers. Do not just make up identifiers just because there is a field available for it.
Inventory Has to Be Trustworthy
Recommendation is only valuable when the suggested product is available for purchase.
This is where artificial intelligence shopping gets highly dependent on backend functionality.
A shopper can ask:
“Find a black standing desk that is in stock and able to ship within this week.”
Although the AI software will be able to recommend the best possible match, the recommendation will fall through if the data regarding the actual inventory is not up-to-date by several hours.
In BigCommerce brands should understand where the inventory truth is maintained.
While for some merchants BigCommerce will serve as a main inventory database, others will have their inventory managed via ERP, WMS, OMS or multiple warehouses.
It doesn’t really matter what approach works better.
All that counts is that the storefront and external shopping applications will get reliable information from the single source of authority.
That’s where BigCommerce ecommerce development services in India become more than just front-end work.
Pricing Needs the Same Treatment
Pricing can become even more complicated than inventory.
A store might have:
- Standard prices
- Sale prices
- Customer-group pricing
- B2B contract pricing
- Quantity discounts
- Regional pricing
- Promotional pricing
A shopping engine for AI shouldn’t assume which price it must use.
The commerce system should be able to give the right price for the proper context.
It is especially true when dealing with B2B BigCommerce stores.
The distributor could charge different prices for the same item based on the buyer, purchase terms, volume of purchases, or market environment.
In such a case, the public price for the product does not reflect what an authenticated customer would pay.
In order to prepare the catalog, one must consider both product data and business logic governing its modification.
Images Still Matter in AI Shopping
Shopping via AI is not evolving into a text-only process.
Images will still be extremely important, especially for categories such as fashion, furniture, beauty, home interior design, jewelry, etc.
A good set of images may consist of:
- Clear primary image
- Alternate images from different angles
- Zoom-in on product
- Image with the product in use
- Scale image
- Variant image
- Package image if applicable
And, most importantly, an image should correspond to the particular product/variant.
If a customer chooses a walnut finish for furniture but all images are of oak furniture, then the catalog creates confusion despite any written information.
This means that when businesses choose BigCommerce design services in India, the images and information about products and variants must be considered together.
Important Specifications Should Not Live Only in PDFs
This problem occurs particularly frequently in manufacturing and business-to-business ecommerce.
A product page could include:
Industrial Pump Model PX500
And then all relevant information is buried within a 12-page downloadable technical spec.
The spec could include pressure ratings, measurements, materials, voltage requirements, temperature range, certifications, and compatible accessories.
All of that information would be useful as documentation, but what really matters to the buyer needs to be included on the product page as well.
For example:
Maximum pressure: 10 bar
Voltage: 230V
Operating temperature: -10°C to 80°C
Material: Stainless steel
This will provide better customer experiences as well as making the information more easily understood by the search engine and other software systems.
BigCommerce has also suggested that merchants prepare themselves for agentic commerce should work on enhancing the quality of product data and technical information.
Product Pages Still Need Structured Data
Preparing a catalog for AI shopping does not mean abandoning established ecommerce SEO practices.
Product structured data remains useful because it explicitly describes information on a product page.
Depending on the product and implementation, this can include:
- Name
- Image
- Description
- SKU
- Brand
- Price
- Currency
- Availability
- Ratings
- Shipping information
- Return information
Google says that Product structured data will enable richer search experiences for your product information, and it advises combining structured data with Merchant Center feed whenever possible.
In case of customized BigCommerce storefronts, you can take the help of an experienced BigCommerce web developer from India or BigCommerce Stencil developer from India to see if the theme provides the right product information or not.
It is highly recommended in case of extensively customized themes.
Think Beyond the BigCommerce Storefront
It’s wrong to regard the BigCommerce catalog as merely existing for the sake of the website.
Product data could be used by:
Google Shopping, marketplaces, ad platforms, social commerce, comparison engines, mobile apps, sales tools, and AI-powered shopping experiences.
Furthermore, the BigCommerce team is busy implementing agentic commerce functionality. The BigCommerce Storefront MCP documentation outlines the tools allowing compatible AI agents to search for products in the catalog, extract information about them, create carts, and produce checkout URLs.
This is quite a telling hint of where commerce is going.
The product catalog is becoming a reusable repository of commerce data, not just a data store for one website.
Before Investing in AI, Audit the Catalog You Already Have
It may seem tempting to view AI Readiness as yet another technology initiative.
But for many brands, the first initiative should be far less sexy: clean up your catalog.
Select a representative sample of products and ask yourself the following questions:
- Are your titles clear enough to make sense without looking at the page?
- Are key specifications organized?
- Are your descriptions helpful?
- Are your attributes consistent?
- Are variant combinations correct?
- Are SKUs and identifiers accurate?
- Is your inventory updated?
- Is your pricing sourced from the right system?
- Do your images accurately reflect your products and variants?
- Does valuable data reside locked within PDF documents?
- Is your Product structured data set up properly?
- Are your external product feeds receiving all the data they need?
- You will likely find problems.
This is good.
Correcting these issues does not just benefit AI shopping; it helps onsite search, filters, merchandising, feeds, integrations, and overall customer experience.
Brands interested in BigCommerce Development Solutions in India can use this audit to see which issues are merchandising and which are technology problems.
What Should BigCommerce Brands Prioritize First?
Trying to perfect an entire catalog at once can become a huge project, particularly for merchants with tens of thousands of SKUs.
Prioritize in this order:
- Best-selling products: Start where better discovery has the greatest commercial impact.
- High-intent categories: Focus on products customers frequently compare using specifications.
- Missing critical attributes: Fix information customers regularly need before purchasing.
- Variants and inventory: Make sure the purchasable item is represented accurately.
- Identifiers: Correct SKU, GTIN, MPN, and brand information where applicable.
- Structured data and feeds: Confirm external systems receive the right information.
- Long-tail catalog: Apply the improved standards across the remaining products.
This makes catalog improvement manageable.
It also gives the team a repeatable model before attempting large-scale changes.
Where BigCommerce Development Fits Into AI Catalog Readiness
Not every modification will require developers’ efforts.
Marketing staff can make changes to titles, descriptions, specifications, features, and visuals.
It is the moment when developers get engaged if the issue concerns storage, synchronization, transformation, or presentation of data.
There are several occasions when a BigCommerce web development company in India may be needed by merchants, including:
- Custom catalog fields
- Product data imports
- ERP integration
- PIM integration
- Inventory synchronization
- Custom pricing
- Search integration
- Feed development
- API development
- Stencil modification
- Structured data correction
- B2B cataloging
BigCommerce website design agencies in India will give more attention to presentation of specifications, variations, visuals, comparison details, and purchase instructions to customers.
These two fields should go hand in hand.
A nice-looking product page will not make up for incorrect data, while precise catalog data will be useless without clear understanding from customers’ side.
Conclusion
Artificial Intelligence (AI) shopping assistants have arrived to transform the way products get discovered. But the preparation for AI shopping assistants is not about deploying an AI chatbot or implementing a complex technical stack.
It’s all about the catalog.
BigCommerce brands need to ensure better product title optimization, enhanced descriptions, structured attributes, accurate variants, reliable identifiers, and up-to-date price and stock information. Specifications must be provided on the product page, and the structured data and feeds must correctly represent what is offered by the merchant.
Those who are considering the BigCommerce development services in India, or a BigCommerce web design company in India can include these requirements into their development strategy.
The best AI shopping catalog is not the one with the largest amount of data.
It is the one that provides accurate and up-to-date information about the products being sold.
