The 3 Levels of Natural Language Search in Ecommerce (And Why Most Vendors Stop at Level 2)

There’s a buzzword flying around the ecommerce landscape that doesn’t always mean the same thing across search applications: Natural Language Search.

At its core, Natural Language Search seeks to understand what a shopper intends by their search query, using natural language processing (NLP) techniques, AI, and machine learning.

The range of capabilities between basic NLP, which has existed since the 1940s, and advanced AI-powered search is considerably wide, and many claims from search vendors are clever “AI-washing.” 

In this article, you’ll learn the difference between three levels of natural language search — from basic to advanced — and why it matters to the search and discovery experience.

Natural Language Search 1.0: The MVP

Minimum-viable natural language processing (which some solutions may position as AI) is simply rule-based pattern matching — specifically, keyword-based or pattern-based text processing.

This approach doesn't actually involve machine learning or a deep understanding of language. It uses predefined rules to detect and respond to certain words or phrases through:

• Keyword matching: Looking for specific words or phrases, e.g. if the query contains “jeans,” also return products tagged with “denim pants” or “denim trousers”
  
  
• Typo tolerance / spell correction: Matching errors to closest correct words based on the number of characters of difference between them, e.g. “nikey” to “nike”
  
  
• Entity recognition: Finding filters, such as “blue” or “women’s” within the query
  
  
• Regular expressions (regex): Using patterns to identify specific types of information, e.g. “42 inch TV” will extract “42” and match to screen size filter, or “shirts under $50” will match the phrase “under $x” and treat it as a price filter
  
  
• Basic part-of-speech tagging: Using lookup tables or simple grammars to assign word types like noun or verb (which may not accurately reflect user context or intent)
  
  
• Deterministic output: Behaving predictably based on fixed rules – there's no learning involved

All of this certainly helps improve relevance matching. But it still fails to provide the system with any real understanding of what’s going on and is brittle to any changes from the exact rules that were coded. (For example, imagine if any mention of a color were set to automatically apply a color filter, and then someone searches for the band “pink floyd" or “black sabbath.”)

A search system using just these tools can’t “think” beyond what it’s explicitly programmed to do. It’s still not true NLP. It’s merely keyword matching with lipstick, trying to pretend real AI is happening underneath the hood. 

Natural Language Search 2.0: Semantics + Machine Learning

At the 2.0 level, search capabilities evolve beyond rigid keyword matching into a more flexible, concept-driven model, powered by vector search.

Instead of looking for literal word matches, vector-based search maps both queries and product information into a shared semantic space, allowing the system to retrieve results based on meaning rather than exact phrasing.

This enables smarter, intent-adjacent matches. For example:

• A query for “butter” may return “margarine,” because they serve similar culinary roles.
  
  
• But it can also backfire. “Chips” might return “Idaho potatoes,” based on shared associations (both may contain potatoes), even if the user meant the snack made from potatoes and not raw potatoes themselves.

Vector search represents a powerful leap: it retrieves based on context, not just string similarity. But on its own, it can also introduce noise, especially when meaning is ambiguous, or the user's true intent isn't clear from the words alone.

Alongside this, NLS 2.0 systems often include:

• Query segmentation and intent parsing (e.g. parsing “men’s blue running shoes under $100” into filters and product type)
  
  
• Synonym expansion based on co-occurrence in query logs (e.g. “sofa” ↔ “couch”)
  
  
• Lightweight personalization and popularity-based re-ranking, helping to surface the most useful results
  
  

All of this makes 2.0 search feel smarter — more like a helpful assistant and less like a rigid rule-follower.

But vector search, while more nuanced than keyword search, is still just a retrieval method. To truly grasp shopper intent, apply business logic, or optimize for outcomes like conversion, you need to go further. 

That’s where AI-native product discovery begins.

Natural Language Search 3.0: Cognitive Embeddings 

The latest wave of natural language search takes language understanding, semantic embeddings, and adaptive learning even further, moving from recognizing meaning to reasoning through context.

With NLP 3.0, AI doesn’t just map words to meanings — it interprets intent through relationships, learning how query phrasing, product attributes, and behavioral signals interact to express what a shopper wants.

At Constructor, we call this Cognitive Embeddings Search (CES): a proprietary approach designed to capture the subtle signals that reveal what a shopper is truly looking for.

What powers NLP 3.0?

Cognitive embeddings are activated by two key technologies: transformers and parallel processing.

Transformers are the ‘T’ in ChatGPT. They take NLP to the next level with the ability to process words in parallel (rather than sequentially), giving them a stronger ability to interpret the meaning of search queries and shopper intent, especially for longer and more complex queries. Transformers also work their magic on the language used in your product catalog, including titles, descriptions, tags, metadata, and customer reviews.

Parallel processing enables transformers to generate smarter vector embeddings, which we refer to as cognitive embeddings. This means your vector map reflects more nuanced relationships between user language and your product catalog for enhanced product retrieval and ranking.

CES is a major leap from earlier NLP, which treated queries as a bag of words. Transformers understand relationships between words in context. For example, “water bottle” ≠ “bottled water” and “dress for a wedding” ≠ “wedding dress.”

Transformers in conversational commerce

As mentioned, transformers are a key component of LLMs like ChatGPT and Constructor’s conversational search solutions: AI Shopping Agent and Product Insight Agent. Because shoppers engage with these solutions in a more conversational manner, it’s all the more important to identify true intent from natural language. Transformers and Cognitive Embeddings drive this intelligence.

Here’s an illustration. When a user asks, “I want the best moisturizer for oily skin with SPF,” Constructor converts the entire query into an embedding that captures the semantic meaning, including:

• Intent (“recommendation” and “problem-solving”)
  
  
• Modifiers (“for oily skin” and “with SPF”)
  
  
• Implied product categories (facial moisturizer, not hand, body or hair)

Constructor then finds the best-matching products within the vector map.

But that’s just the first step. Results should also be ranked by a mix of popularity (full verified clickstream data across users) and personal appeal to the shopper (behavioral signals from the user).

Transformers in personalization

Constructor pairs the embeddings generated by CES’ transformers with clickstream and behavioral data to create a continuous feedback loop that self-optimizes in real time — something generic vector search cannot do.

The model also factors inventory, promotions, fulfillment options, and the searchandizing logic (business rules) you set up in your admin so that personalized search rankings not only serve customer needs, but also your business objectives.

Beyond search ranking, our transformer model also drives personalization across:

• Autocomplete / query suggestions: Predicting likely completions based on intent
  
  
• Browse / category pages: Using the same semantic logic to power PLP ranking
  
  
• Product recommendations: Understanding relationships beyond co-purchase data
  
  
• Analytics and feedback: Diagnosing what queries are underperforming and why

Summing It Up: Standard Vector Search vs Cognitive Embeddings

Standard vector search technically falls under the AI umbrella, specifically under machine learning-based semantic search. However, it doesn’t fully utilize the adaptive, interactive intelligence deep embeddings powered by transformers.

In sum:

How to Evaluate a Vendor’s “Natural Language Search” Claims

Now that you know what separates keyword-based NLP from true transformer-powered search, here’s how to pressure-test a vendor’s claims before you buy:

1. Run messy, real-world queries.
   
   Don’t rely on canned demos. Give the vendor your catalog and a list of 20–30 queries that represent real shopper behavior, especially the kinds that trip up legacy engines:

• Misspellings / phonetic errors: “nik air force” or “nife set”
  
  
• Semantic intent: “running shoes for bad knees”
  
  
• Over-stemming: “dress for weddings” vs. “wedding dress”
  
  
• Unusual word order: “black leather men’s jacket” vs. “men’s black jacket leather”
  
  
• Ambiguity: “apple accessories” vs. “apple snacks”
  
  
• Longform / conversational: “something lightweight to wear hiking in summer”
  
  Ask whether the system understands meaning or just matches words. A true NLS engine should gracefully interpret intent, not break when phrasing changes.

2. Watch how results are ranked
   
   Is NLP used only to parse the query, or does it influence which products surface and why? Ask whether semantic understanding and behavioral data feed into ranking decisions, or if results are still ordered by static popularity.
   
   
3. Check how the model learns
   
   Does it self-optimize from shopper interactions (clicks, conversions, zero-result behavior)? How often does retraining occur? Modern systems retrain continuously using real-time behavioral data. Anything less is legacy.
   
   
4. Inspect zero-result and fallback behavior
   
   For expressive or imperfect queries, does the system show relevant alternatives — synonyms, near-matches, in-stock substitutions — or just “no results”? True NLS should understand intent even when the catalog doesn’t have an exact match.
   
   
5. Test across devices and modalities
   
   Voice and mobile searches tend to be longer and more conversational in nature. Try spoken queries like “show me cozy fall sweaters under $100.” A robust engine should interpret these naturally, not crumble under sentence-length input.

Bonus tip: Ask how much of this is transformer-based.

If the model can’t explain how its embeddings are generated or adapted for ecommerce data, you’re probably looking at legacy vector search, not true AI-native discovery.

Ready to Transform Your Search & Discovery Experience?

Transformer-powered AI, when paired with real-time user behavior, is reshaping what’s possible in ecommerce search and product discovery.

Constructor brings these technologies together in a single, commerce-native platform — using advanced language models and full verified clickstream data to surface results that align with shopper intent and convert more effectively.

Curious how your current search experience stacks up? Request a complimentary Search Experience Audit and discover actionable ways to improve relevance, engagement, and revenue.