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Conversational Product Discovery

Search should understand
why customers buy

Turn conversational, ambiguous and contextual requests into comparable product criteria, then respond in a voice that fits your brand.

Why AI Search

Move from keyword matching to intent understanding

Traditional search asks customers to guess exact product names and categories. Cobinyiu understands everyday language, synonyms, typos, scenarios and multiple conditions, helping customers reach relevant products with less effort.

AI Search ⌕

Natural-language queries

Let the system learn customers' everyday language

  • 01 Smart Interactive Interface Like a sales consultant familiar with the products, understand needs and provide clear direction for choices.
  • 02 Understand vague and colloquial expressions Able to identify context, synonyms and spelling errors, reducing friction when customers cannot find the results.
  • 03 Shortening the distance from selection to decision Transforming vague needs into comparable product conditions helps customers find suitable options faster.
Traditional Search ⌨

Context and attribute extraction

Requiring customers to use the brand's official language

  • 01 Rigid keyword search Customers usually have to enter precise product names, categories, or keywords already set on the website.
  • 02 Customers need to learn first Having to guess how brands name and categorize products easily shifts search costs to users.
  • 03 Difficulty in understanding real needs When faced with colloquialisms, typos, or multi-condition searches, zero results or irrelevant content are easily obtained.
The Shift Learning brand language from customers → "System understanding of customer language"
How It Works

Four layers for dependable answers

We combine intent analysis, product-data retrieval, business rules and response generation. The result is not only fluent—it stays connected to real product information and brand priorities.

Baymard usability research 31%

Intent and entity analysis

In the study, nearly one-third of users were unable to complete the task or gave up due to frustration when trying to find products through the site search.

Google Cloud / Harris Poll 76%

Semantic and metadata retrieval

Surveyed US consumers indicated that unsuccessful searches had caused websites to lose sales, with 48% switching to other places to buy.

Google Cloud / Harris Poll 69%

Ranking and business controls

Respondents indicated that after successfully finding a product, they would also purchase other items from the same website besides the original search target.

Source: Baymard Ecommerce Search Usability Google Cloud／Harris Poll（2021） Cross-market research is only for industry trend reference; actual effectiveness is verified by the brand's own data.
Business Value

Improve discovery and learn from demand

Search data reveals how customers describe needs, where product information is missing and which questions have commercial intent. Teams can use those signals to improve merchandising, content and service.

Customers' Colloquial Searches

"It's very hot, I want to take my child to the forest to escape the heat, what should I prepare for safety?"

Keyword Match No Exactly Matching Product Found
0 results

Traditional searches rely more on product names, categories, and pre-defined keywords. When entering a complete sentence, the system may not be able to simultaneously understand the relationship between "hot," "children," "forest," and "safety."

It's very hot. Bringing a child. Seeking refuge in the forest. What to prepare? Safety is paramount.
View traditional search examples ↗
How it works

Four Levels for More Reliable Answers

Not just putting a generic chatbot on the website, but designing models, product information, and business rules together.

Understanding Intent

Identifying the target audience, budget, occasion, preferences, and unacceptable conditions.

Structured Conditions

Converting natural language into product attributes, categories, and filterable conditions.

Ranking and Constraints

Combining relevance, inventory, gross profit, promotions, and brand recommendation rules.

Explaining the Answer

Explaining why a recommendation is made to customers reduces uncertainty and increases confidence in decision-making.

01 / Discovery

Natural Language and Semantic Search

Supports synonyms, colloquialisms, misspellings, cross-language search, and usage context. Even if the exact same word doesn't appear on the product page, results can be found through product attributes and semantic understanding.

Chinese Colloquialism Requirements Synonyms and Misspellings Compound Conditions Zero-Result Remediation
02 / Control

Controllable Business Ranking

AI relevance does not equal business priority. The formal system can incorporate new products, inventory, promotions, and brand strategies into the ranking, while avoiding unsuitable products being forcibly promoted.

Real-time Inventory Filtering Promotion Weighting Brand Rules Sensitive Content Protection
03 / Learning

Continuous Improvement Based on Real Behavior

Track queries, clicks, add-to-cart actions, and conversions to identify the language, content gaps, and product information issues actually used by customers, forming a continuous optimization loop.

Opportunity Simulator

Search for Revenue Opportunities
Contextual Calculation

Input the brand's current search and transaction data to estimate "potentially recaptured revenue opportunities" with a conservative improvement rate, serving as the starting point for PoC priority discussions.

Monthly Potential Revenue Opportunities NT$ 54,000
Annual Scenario Conversion NT$ 648,000
Calculation Method:Monthly Search Count × Search Failure Rate × Estimated Improvement Potential × Conversion Rate After Improvement × Average Order Value
This is a scenario estimate based on input assumptions and does not represent Cobinyiu's guarantee of actual revenue, conversion rate, or return on investment. Formal evaluation should use brand search records, product data, and conversion events for PoC verification.

Ready to test AI Search with your own catalog?

Start with a focused proof of concept using real products, queries and conversion events.

Talk to us →