Online shopping has changed faster than most e-commerce search tools have.
For years, retailers built their search experience around a simple assumption. A shopper would type a product name, the system would match a few keywords, and the right item would appear. That model worked well enough when people searched in short, predictable phrases like black sneakers, blue sofa, or wireless earbuds.
That is no longer how many people shop.
Today’s customers search in a much more natural way. They type things like a dress for a summer wedding that is not too formal, a couch for a small apartment with storage, or skincare for dry sensitive skin that will not feel heavy. Sometimes they are not even searching for a specific product. They are searching for a style, a solution, a mood, or a use case. They expect the experience to understand what they mean, not just what they typed.
That shift is one of the biggest reasons e-commerce search is being forced to evolve, and it is also where John Andrews has built a clear point of view through Cimulate. Rather than treating search as a keyword-matching tool, Cimulate is part of a growing movement that sees product discovery as an intent problem. In other words, the real challenge is not finding matching words. It is understanding what the shopper is actually trying to do.
That may sound like a subtle difference, but for retailers, it changes everything.
Why Keyword Search Is Starting to Show Its Limits
Traditional keyword search was built for a more structured kind of e-commerce. It assumes that shoppers know what they want, know how to describe it, and use the exact language the retailer’s catalog expects. In real life, that rarely happens as cleanly as brands would like.
A shopper might search for office chair for back pain when the product title says ergonomic desk chair. Another shopper may type quiet luxury handbags under 300 even though the catalog is organized by materials, silhouettes, and brand names. Someone else may want running shoes for flat feet but search results are still shaped more by literal word matches than by fit, intent, or context.
This is where the gap starts to show.
Search systems can return technically relevant items while still missing the real need behind the query. A shopper sees results, but not the right results. The experience feels close, yet not helpful enough. That kind of friction matters because e-commerce discovery is not just a utility feature. It plays a major role in whether a customer keeps browsing, refines their search, or leaves the site altogether.
Retail teams feel this pain too. When search falls short, merchandisers and e-commerce operators often have to fill the gaps manually. They maintain synonyms, set rules, boost certain products, bury others, monitor weak queries, and keep adjusting the system so it does not underperform. Over time, search becomes something that constantly needs attention. It works, but only with ongoing babysitting.
That is one of the reasons the conversation around onsite search has become much broader. Retailers are no longer just asking how to improve keyword results. They are asking how to create a better discovery experience overall.
Who John Andrews Is in This Shift
John Andrews is the CEO and co-founder of Cimulate, a company focused on AI-native commerce and product discovery. His work sits at the intersection of retail technology, shopper behavior, personalization, and search relevance.
What makes his perspective interesting is that it is not limited to search as a standalone feature. The bigger idea behind Cimulate is that e-commerce discovery should reflect how people actually shop. That means understanding context, interpreting natural language, responding to behavior in real time, and helping retailers surface the most relevant products in a way that feels intuitive.
That is a much bigger ambition than simply improving a search bar.
It is also one that matters more now because digital commerce is entering a new phase. Search is no longer just about a customer typing a few words into a box. It is becoming part of a larger discovery layer shaped by personalization, conversational interfaces, recommendation systems, and agentic commerce experiences.
From that angle, Andrews is not just working on a better e-commerce search engine. He is pushing toward a different model for how retailers connect shoppers with products.
From Matching Keywords to Understanding Intent
The most important shift in this space is the move from keyword logic to intent-aware discovery.
Keyword search focuses on surface-level language. It tries to match the shopper’s query with indexed product data, tags, or catalog attributes. That approach can work for straightforward searches, but it tends to break down when the query becomes more nuanced.
Intent-aware discovery starts from a different place. It asks what the shopper is actually looking for, even if the language is messy, incomplete, conversational, or indirect.
That distinction matters because shoppers do not think like databases.
They often describe an outcome instead of an item. They describe a situation instead of a category. They may search emotionally, visually, or functionally. A parent might want something durable for a toddler. A traveler may want a carry-on bag that works for short business trips. A shopper looking for furniture may care about apartment size, ease of assembly, or aesthetic style more than exact product terminology.
A system built around intent can handle that kind of complexity much better than one built around literal keyword overlap alone.
This is one of the clearest ways to understand what Cimulate is trying to do. The company’s positioning reflects a belief that product discovery should be context-aware, adaptive, and aligned with real shopper behavior. That makes it part search, part personalization engine, part merchandising intelligence layer.
Why Context Matters More Than Exact Phrases
One reason keyword search struggles is that words rarely carry enough meaning on their own.
Take a search for jacket. That one word could mean a lightweight spring layer, a waterproof shell, a blazer for work, or a winter coat. Even a longer query like women’s jacket for travel still leaves room for interpretation. Is the shopper prioritizing packability, weather resistance, wrinkle resistance, style, or price?
Context helps answer those missing questions.
In e-commerce, context can come from many places. It can come from the shopper’s session behavior, the products they have already viewed, category patterns, seasonal demand, past clicks, geographic signals, or the structure of the broader shopping journey. It can also come from natural language itself, especially when AI systems are able to interpret the relationship between words rather than treat them as isolated tokens.
This is where AI-native commerce tools have a real advantage. Instead of relying only on rigid rules and exact phrases, they can work with richer signals. They can interpret meaning more flexibly and respond in ways that feel closer to how a knowledgeable store associate might guide someone in person.
That is a useful way to think about the broader promise of modern product discovery. It is not just about finding products faster. It is about reducing the gap between what a shopper wants and what the store understands.
What It Means to Be AI-Native in Commerce
The phrase AI-native gets used a lot, but in e-commerce, it points to something specific.
A legacy search system may add AI features on top of an older framework. It might bolt on better query understanding, automated ranking, or some personalization. That can create improvements, but the core architecture often still reflects an earlier era of e-commerce, one centered on static rules, keyword tuning, and manual optimization.
An AI-native approach starts from a different foundation. It is designed from the outset to interpret data, behavior, language, and context in a more dynamic way. Instead of treating AI as a support feature, it treats intelligence as part of the core system.
That matters because e-commerce has grown too complex for purely manual logic to keep up. Retailers deal with massive catalogs, shifting inventory, changing shopper preferences, different merchandising goals, and increasingly fluid shopping behavior. On top of that, consumer expectations have been shaped by search engines, recommendation platforms, social discovery, and conversational AI.
In that environment, discovery systems need to be more responsive. They need to adapt quickly, learn from signals, and help retailers make better decisions without requiring constant manual intervention.
That is why the AI-native label matters in this conversation around John Andrews and Cimulate. It signals a belief that commerce infrastructure itself needs to be rebuilt around a more intelligent layer, not just lightly upgraded.
E-commerce Search Is Becoming a Discovery Experience
One of the most useful ways to frame this change is to stop thinking about search as an isolated box on a website.
Search is increasingly part of a wider discovery journey.
A customer may arrive with a broad idea, refine it through browsing, compare options, interact with recommendations, and continue moving through the site based on what the system learns about their preferences. In many cases, the most important job is not just answering a query. It is guiding the next best step.
That is why the future of search looks much more conversational, contextual, and interactive than older e-commerce models allowed for.
A shopper might begin with a vague question. The system can then narrow possibilities, surface more relevant products, highlight tradeoffs, and help the customer get closer to the right choice. That is a very different experience from simply returning a long list of matching SKUs.
This broader view also helps explain why product discovery now overlaps with recommendations, browse optimization, intelligent merchandising, and virtual shopping assistants. These are no longer separate conversations. They are connected parts of how retailers help shoppers find what they want.
In that sense, Andrews and Cimulate are operating in a part of commerce that is becoming increasingly strategic. Better discovery does not just improve UX. It affects conversion, product visibility, average order value, and customer confidence.
Why Retailers Are Under Pressure to Adapt
Retailers are not dealing with this shift in a vacuum. Customer expectations are changing fast, and commerce platforms are being pushed to respond.
People have become used to digital experiences that feel more intuitive. They expect systems to understand natural language, anticipate what they mean, and present useful options without forcing them to learn a platform’s internal logic. In e-commerce, that means the burden is moving away from the shopper and onto the technology.
At the same time, AI is changing the way discovery happens across the internet. Search behavior is becoming more conversational. Recommendations are getting smarter. Shopping journeys are becoming less linear. Agentic commerce is also entering the discussion, where AI systems can help guide or even automate parts of the purchasing process.
That makes keyword-only thinking feel increasingly outdated.
Retailers that still depend too heavily on rigid search structures may find that their experience feels slower, less relevant, and more frustrating than what shoppers now expect. Even if the catalog is strong and the brand is trusted, poor discovery can quietly drag performance down.
This is why companies working on intent-aware search, personalized recommendations, semantic understanding, and commerce AI are attracting more attention. They are not just improving a small feature. They are responding to a bigger shift in how digital shopping works.
How Better Discovery Connects to Revenue
There is a practical reason this topic matters so much. Better product discovery is not only about making the site feel smarter. It also affects business outcomes.
When shoppers find relevant products faster, they are more likely to stay engaged. When results align more closely with intent, they are more likely to click, explore, and convert. When browsing feels helpful instead of overwhelming, the path to purchase becomes easier.
That creates a direct link between relevance and revenue.
For retail teams, this can show up in several ways. Conversion rates can improve because customers are seeing products that fit their needs more closely. Search abandonment can decline because the experience feels less frustrating. Merchandising efficiency can improve because teams spend less time patching weak search behavior with manual rules. Even product visibility can become more balanced because discovery systems can surface items based on contextual relevance rather than blunt popularity signals alone.
This is why search relevance, personalization, and intelligent merchandising are no longer niche technical discussions. They are commercial priorities.
John Andrews’ broader contribution here is helping frame discovery as a growth lever rather than a backend utility. That may seem obvious in hindsight, but it changes how retailers evaluate the role of search technology.
Giving Merchandisers Better Tools Instead of More Busywork
One of the most overlooked parts of e-commerce search is the operational burden it puts on internal teams.
When a system lacks strong contextual understanding, the people behind the store have to compensate. They create rules, monitor weak queries, adjust ranking logic, maintain synonyms, and constantly tweak the experience to keep it usable. That work adds up, especially for retailers with large assortments, seasonal shifts, and changing promotional priorities.
The promise of smarter commerce infrastructure is not that humans disappear from the process. It is that they can spend less time correcting obvious search problems and more time shaping strategy.
That distinction matters because merchandising is still deeply important. Retailers want control over product visibility, brand priorities, campaign timing, margin goals, and seasonal storytelling. They do not want to hand all of that over to a black box.
The better path is a system that reduces unnecessary manual work while still supporting human judgment.
That is one reason the conversation around AI-native product discovery resonates with merchandising teams. The value is not just in automation. It is in creating a more responsive foundation that frees teams from constant cleanup.
The Bigger Industry Direction Behind Cimulate
The most interesting part of this story is that it reaches beyond one company.
What John Andrews represents in this space is a broader shift inside retail technology. The industry is moving from retrieval to understanding. From matching terms to modeling intent. From static rules to adaptive systems. From isolated search boxes to connected discovery experiences.
That direction lines up with several major forces in digital commerce.
One is semantic understanding, where systems interpret meaning rather than depend only on exact language. Another is real-time personalization, where product ranking and recommendations respond to active behavior instead of generic assumptions. Another is conversational commerce, where users interact with shopping systems in a way that feels more like dialogue than search syntax. And then there is agentic commerce, which pushes this even further by imagining shopping experiences shaped by AI agents acting on behalf of users or brands.
All of these trends point toward the same core idea. Discovery is becoming smarter, more fluid, and more aligned with how people naturally make buying decisions.
That is why the move beyond keyword search matters. It is not just an upgrade to a familiar tool. It is part of a larger rethinking of how e-commerce works.
What This Means for the Future of Retail Search
Retailers do not need to abandon search. They need to redefine what search is supposed to do.
In the past, search mainly served as a navigation shortcut. It helped customers jump directly to a product or category. Going forward, it is becoming a richer layer of interpretation, assistance, and relevance.
That means the best systems will likely do several things at once. They will understand natural language. They will respond to shopper context. They will connect search with recommendations and browse behavior. They will support merchandising goals without requiring endless manual fixes. And they will fit into an e-commerce environment where AI is playing a larger role in every part of the customer journey.
Seen through that lens, John Andrews is contributing to a very current retail conversation. He is part of a group of commerce leaders trying to build for the next version of discovery, one where understanding intent matters more than matching isolated words.
For retailers, that is more than a technical improvement. It is a competitive shift.