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How to Add AI Search to Your eCommerce Website

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Search has always been one of the most important functions of an eCommerce website.

For retailers with hundreds, thousands or even hundreds of thousands of products, it is often the fastest path between a customer arriving on a website and finding the product they want to buy.

But traditional eCommerce search has a fundamental limitation.

It expects customers to know what to search for.

A customer might type:

“Ford Ranger brake pads”

Traditional search can handle this reasonably well.

But what happens when the customer searches:

“I have a 2021 Ford Ranger Wildtrak and need front brake pads suitable for towing. What will fit my vehicle?”

Or:

“I need a black outdoor umbrella approximately 3 metres wide that can handle windy conditions.”

Or, in B2B:

“Show me the 32mm electrical conduit fittings we normally purchase that are currently available from our Brisbane warehouse.”

These aren’t really search queries anymore.

They’re conversations.

This is where AI-powered eCommerce search has the potential to fundamentally change how customers discover products online.

Instead of forcing customers to translate what they want into a handful of keywords, AI search allows them to describe what they are trying to achieve.

For retailers, manufacturers and distributors with large or complex product catalogues, this represents an enormous opportunity.

But adding AI search to an eCommerce website isn’t simply a matter of connecting ChatGPT to your product catalogue.

To work effectively, AI needs access to accurate product data, inventory, pricing, customer information and potentially ERP, PIM and other business systems.

In this guide, we’ll look at how AI eCommerce search works, what is required to implement it, and how businesses can introduce AI-powered product discovery without necessarily rebuilding their entire eCommerce platform.

What Is AI eCommerce Search?

AI eCommerce search uses artificial intelligence to understand what a customer is trying to find rather than simply matching the words they type against product names and descriptions.

Traditional eCommerce search is primarily based around keywords, attributes, synonyms and search rules.

A customer searches:

“blue outdoor umbrella”

The search engine looks for products containing or associated with:

Modern search platforms have become considerably more sophisticated than simple keyword matching, but the basic interaction remains similar.

AI search introduces another layer.

It can potentially interpret the meaning and intent behind a customer’s request.

For example:

“I need an umbrella for a windy coastal balcony with limited space.”

An AI-powered search experience could potentially understand that the customer is looking for:

The customer hasn’t necessarily entered any of those exact product attributes.

The AI has interpreted the customer’s requirements and used product data to identify suitable products.

That is a very different search experience.

Traditional eCommerce Search vs AI Search

Traditional search isn’t going away.

In fact, for many searches it remains the fastest and most effective approach.

If someone enters a SKU such as:

ABC-12345

they don’t need an AI conversation. They need the correct product immediately.

Similarly, a customer searching for:

“Nike Air Max 90”

has already communicated exactly what they want.

AI becomes particularly valuable when the customer’s requirement is more complex than the search query.

Consider an automotive retailer.

Traditional search might be:

“Ford Ranger brake pads”

An AI-assisted search could be:

“I have a 2021 Ford Ranger Wildtrak and tow a caravan regularly. I need replacement front brake pads. What are my options?”

Answering this accurately could require the system to understand:

The opportunity isn’t simply to replace the search box with a chatbot.

It is to combine the speed of traditional search with a much deeper understanding of customer intent.

What Can Customers Ask an AI eCommerce Search?

The potential queries depend heavily on the retailer and the quality of its underlying product data.

For a fashion retailer:

“I need a smart casual navy outfit for an outdoor wedding in summer.”

For an electrical distributor:

“Show me IP66-rated enclosures suitable for outdoor installation that are at least 400mm high.”

For an automotive parts retailer:

“What brake rotors fit my 2020 Toyota Hilux SR5?”

For a furniture retailer:

“I have a 2.8 metre wall and want a low entertainment unit in light timber. What would fit?”

For a B2B distributor:

“Show me the safety gloves our company normally orders that are available for delivery this week.”

These searches demonstrate why AI search is potentially much more powerful than simply improving keyword matching.

The customer can communicate the problem they are trying to solve.

The eCommerce platform can then help identify the product.

How Does AI eCommerce Search Actually Work?

There are several ways to implement AI-powered product search, and the architecture will depend on the eCommerce platform, product catalogue, business systems and desired customer experience.

At a simplified level, the architecture may look something like:

Customer Query

↓

AI / Natural Language Interpretation

↓

Product Search and Retrieval Layer

↓

Product Catalogue + Attributes + Business Rules

↓

ERP / PIM / Inventory / Pricing Data

↓

Relevant Products

↓

eCommerce Storefront

The important point is that the AI should not simply invent an answer.

It needs to retrieve information from trusted business data.

If a customer asks:

“Do you have this product available in Brisbane?”

the answer should come from actual inventory information.

If a B2B customer asks:

“What price do we pay for this?”

the answer may need to come from customer-specific pricing maintained within the ERP or commerce platform.

If someone asks:

“Will this fit my vehicle?”

the answer needs to be based on reliable vehicle compatibility information.

This is why AI eCommerce search is as much an integration and data challenge as it is an AI challenge.

Your Product Data Determines How Good Your AI Search Can Be

One of the biggest misconceptions surrounding AI is that it can somehow compensate for poor product data.

In many cases, the opposite is true.

AI makes good product data even more valuable.

Imagine an outdoor furniture retailer has two umbrella products.

Product A contains:

Product B contains:

Which product can an AI system reason about more effectively?

Clearly, Product B.

If the customer asks:

“Which umbrella is suitable for a windy restaurant courtyard?”

the AI needs structured information with which to answer the question.

This is why we believe businesses considering AI search should also examine whether their product data is genuinely eCommerce ready.

AI search may expose product data weaknesses that traditional navigation previously hid.

AI Search and Your ERP

For many established retailers, manufacturers and distributors, important product information doesn’t live exclusively within the eCommerce platform.

It may exist across:

The ERP might hold SKU, inventory and pricing.

The PIM might contain detailed product specifications.

The eCommerce platform might contain merchandising information and customer-facing content.

An external database might contain compatibility information.

Effective AI search may need to bring these data sources together.

This is particularly important when customers ask questions involving real-time business information.

For example:

“Which of these products can I collect from the Gold Coast today?”

That isn’t simply a product-search question.

It is a product + inventory + location question.

The AI layer needs reliable access to all three.

AI Search for Shopify

AI-powered search can be introduced into a Shopify environment in several ways.

The right approach depends on the complexity of the catalogue and the search experience required.

For some retailers, existing Shopify search functionality and specialised search applications may provide enough capability.

More sophisticated retailers may require an external search platform, custom search index or AI retrieval layer integrated with Shopify.

A typical architecture could involve:

Shopify

↓

Product/Search Index

↓

AI Interpretation Layer

↓

Relevant Product Retrieval

↓

Shopify Product Results

Importantly, Shopify can remain the commerce engine.

The business doesn’t necessarily need to replace its eCommerce platform simply because it wants to introduce AI-powered search.

For businesses using Shopify alongside an ERP or PIM, the architecture may also allow AI search to access richer product information than is stored directly within Shopify.

AI Search for Adobe Commerce

Adobe Commerce is particularly well suited to complex catalogue environments where search, product attributes, customer groups and integrations play a major role in the buying experience.

AI-powered search can potentially sit alongside existing Adobe Commerce search capabilities and external search technologies.

The appropriate solution depends heavily on the retailer’s requirements.

For example, an enterprise retailer may need AI search to consider:

Rather than thinking about AI as replacing Adobe Commerce search entirely, businesses should consider where natural-language understanding can improve the existing product discovery journey.

AI Search for B2B eCommerce

B2B may ultimately be one of the most compelling applications of AI-powered commerce search.

B2B catalogues are often significantly more complicated than consumer catalogues.

Customers may need to understand:

The buyer may know the problem they are trying to solve without knowing the exact SKU required.

Consider:

“I need a replacement three-phase motor suitable for this application. What options do we have under our account?”

Or:

“Show me all IP67 connectors compatible with this cable size that are available from our Sydney warehouse.”

These are potentially high-value queries.

An intelligent B2B search experience could combine product discovery with customer-specific commercial information.

That could significantly reduce the amount of product research traditionally performed by sales representatives and customer service teams.

AI Search for Automotive eCommerce

Automotive is another category where AI-powered search has enormous potential because product compatibility is critical.

A customer rarely wants a generic brake pad.

They want:

the brake pad that fits their vehicle.

Automotive eCommerce businesses may already use vehicle data services and fitment databases such as PartsDB, Vehicle Logic or TecDoc.

AI creates an opportunity to build a more conversational layer on top of this structured information.

Instead of forcing the customer through:

Year → Make → Model → Variant → Category

the customer might simply ask:

“I need front brake pads for my 2022 Ford Ranger Raptor.”

The system could identify the vehicle, validate fitment and return compatible products.

More sophisticated requests could incorporate use cases:

“I do a lot of towing. Which brake pads would you recommend?”

This combines vehicle compatibility with product characteristics and customer intent.

The AI isn’t replacing structured automotive data.

It is making that data easier for customers to interact with.

AI Search Should Not Be Allowed to Guess

This is one of the most important principles when implementing AI within eCommerce.

A conversational answer can sound convincing even when it is wrong.

That is unacceptable when the answer involves:

An AI search implementation therefore needs clearly defined boundaries.

Where possible, responses should be grounded in verified catalogue and business data.

If the information isn’t available, saying:

“I don’t have enough information to confirm compatibility.”

may be substantially better than generating an incorrect recommendation.

The objective isn’t to create the most impressive AI demo.

It is to create a reliable commerce experience.

Do You Need a Chatbot?

Not necessarily.

AI search and chatbots are often grouped together, but they are not the same thing.

A retailer could provide a conventional search interface while using AI behind the scenes to interpret natural-language queries.

Alternatively, the interface could become conversational.

For example:

Customer: I need an outdoor dining table for eight people.

Search: Is the table going to be permanently exposed to the weather?

Customer: Yes, and we’re close to the ocean.

The search experience can now refine the results based on additional context.

For complicated purchasing decisions, this can be extremely powerful.

For straightforward product searches, it may be unnecessary.

The best implementation should match the buying journey rather than adding a chatbot simply because AI is fashionable.

Can You Add AI Search to an Existing eCommerce Website?

In many cases, yes.

Implementing AI search doesn’t automatically require an eCommerce replatform.

An existing Shopify or Adobe Commerce website may be able to integrate with an external search service, AI model or custom retrieval layer.

This can make AI search an attractive standalone digital project.

A retailer could potentially start with a clearly defined use case, such as:

natural-language product discovery

and progressively expand the implementation.

Future capabilities might include:

This incremental approach can be considerably more practical than attempting to create an all-encompassing AI assistant from day one.

Build vs Buy: How Should You Implement AI Search?

There isn’t one AI search technology that will be appropriate for every retailer.

Businesses should generally consider three approaches.

1. Native eCommerce Search

Start by understanding what your existing commerce platform already provides.

For relatively straightforward catalogues, improving product data, attributes, synonyms, filters and merchandising may deliver substantial improvements without requiring a major AI implementation.

2. Specialist Search Platform

Platforms specialising in eCommerce search and product discovery can provide sophisticated functionality without requiring the business to build an entire search engine.

Depending on requirements, technologies such as Algolia, Klevu, Searchspring, Constructor and other search platforms may form part of the solution.

The key question shouldn’t be:

“Which AI search tool is best?”

It should be:

“Which architecture best solves our customers’ product discovery problem?”

3. Custom AI Search

Complex businesses may benefit from a custom AI layer.

This could combine a large language model with search indexes, APIs and business systems to create functionality specifically designed around the retailer’s catalogue.

This approach can provide substantially greater flexibility.

It also requires considerably more thought around architecture, performance, security, accuracy and ongoing management.

What Does AI eCommerce Search Cost?

The cost of implementing AI search can vary dramatically.

Adding an existing search application to a relatively straightforward Shopify store is very different from creating an enterprise AI product discovery platform integrated with ERP, PIM, inventory and customer pricing.

The major cost drivers include:

For this reason, AI search projects should begin with a clearly defined business case and use case rather than selecting technology first.

Start With the Customer Problem, Not AI

There is an understandable temptation for businesses to start with:

“We need AI on our website.”

That is the wrong starting point.

Instead ask:

What are customers currently struggling to do?

Perhaps customers can’t find compatible products.

Perhaps search performs poorly when customers don’t know the correct terminology.

Perhaps B2B buyers repeatedly call customer service for product recommendations.

Perhaps the catalogue contains thousands of technically similar products.

Perhaps customers don’t understand which product suits their application.

These are problems worth solving.

AI may then become part of the solution.

A Practical AI Search Implementation Roadmap

For most established eCommerce businesses, we recommend approaching AI search progressively.

Step 1: Identify High-Value Search Problems

Review what customers are searching for, where zero-result searches occur and where customers require assistance.

Talk to sales and customer service teams.

They often know exactly which questions customers repeatedly ask.

Step 2: Audit Product Data

Determine whether the product information required to answer those questions actually exists.

Identify missing attributes, inconsistent values and unstructured information.

Step 3: Map the Data Architecture

Document where information currently resides.

This may include Shopify, Adobe Commerce, ERP, PIM, POS, WMS and external product databases.

Step 4: Define the AI Use Case

Start narrow.

For example:

“Allow customers to describe the product they need in natural language and return relevant catalogue products.”

That is substantially easier to measure than:

“Build an AI shopping assistant.”

Step 5: Select the Architecture

Determine whether the solution should use native platform capabilities, an established search provider or a custom AI implementation.

Step 6: Build a Proof of Concept

Test the experience against real customer questions.

Not artificial demo queries.

Use questions taken from search logs, sales teams and customer service.

Step 7: Measure Commercial Performance

Ultimately, AI search needs to improve business outcomes.

Measure:

AI functionality should be treated like any other eCommerce investment.

It needs to generate value.

Choosing an AI eCommerce Development Partner

AI eCommerce projects sit across several disciplines.

The AI model itself may only be one component.

A successful implementation can require expertise across:

This is particularly important for enterprise and B2B businesses.

An AI developer may understand language models but not understand your ERP.

An eCommerce developer may understand Shopify but not product data architecture.

A search vendor may understand search but not the systems containing your customer-specific pricing.

The challenge is connecting these components into a reliable commerce experience.

Where OSE Can Help

OSE works with retailers, manufacturers and distributors to design and build sophisticated eCommerce solutions across Shopify and Adobe Commerce.

Our work frequently involves the systems that become critical when introducing AI into eCommerce, including:

For businesses considering AI-powered product search, the first step doesn’t need to be a major AI transformation project.

It can start with a specific question:

What would our customers be able to do if they could simply tell our website what they needed?

From there, we can assess the product data, systems and architecture required to make it possible.

Final Thoughts

AI has the potential to fundamentally change product discovery in eCommerce.

For decades, online stores have required customers to understand the structure of the catalogue.

Customers navigate categories.

They select filters.

They enter keywords.

They refine results.

AI provides an opportunity to reverse that relationship.

Instead of customers learning how the catalogue works, the website can begin to understand what the customer wants.

But the businesses that benefit most from AI search won’t necessarily be those that install the latest AI tool first.

They will be the businesses with strong product data, well-integrated systems and a clear understanding of the customer problem they are trying to solve.

For simple eCommerce catalogues, conventional search may continue to work extremely well.

For complex retail, B2B, manufacturing and automotive catalogues, however, AI-powered product discovery could represent a significant step forward.

And importantly, businesses don’t necessarily need to rebuild their eCommerce platform to begin.

An AI search initiative can start with one high-value use case, one customer problem and one well-defined proof of concept.

Then the results can determine what comes next.

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