Using a site search platform at present entails far more than embedding a search bar on your website. The modern search infrastructure should be able to perform indexing, establishing ranks of relevance, correction of typos, applying semantic search, providing auto-completion, filtering, personalization, merchandising, analytics, and even producing AI-generated answers.

Algolia, Yext, and ExpertRec are three players in the field of site search whose solutions differ significantly from each other.

Algolia is all about creating its state-of-the-art search infrastructure controlled by the developers and greatly relying on APIs. Yext provides site search engines based on its knowledge graph and its digital customer experience solution. ExpertRec is something different as it derives most of its capabilities from search engine technology while being deeply involved in site and e-commerce search.

Algolia vs Yext vs ExpertRec: At a Glance

Feature availability can vary by product, plan, integration, and configuration, so businesses should validate exact requirements before implementation.

Search Architecture: The Major Technical Difference

The most prominent distinction among the platforms lies in the means of handling the search data.

Algolia: Application of Search as a Service in the System

In Algolia, the responsibility of indexing data, establishing relevance, and accessing the search service lies with the applications. Developers are accountable for all steps from indexing to seeking through filters and ranking to designing the search UI.

The following diagram presents one possible oversimplified architecture.

Product Database → Indexing Pipeline → Algolia Index → Search Service→ Online Store

All engineering teams can implement this model and control the search process.

Yext: Application of Knowledge Graphs and Entities in Architecture

In Yext, the emphasis is put on structured entities and Knowledge Graphs instead of on documents.

The company uses structured entity information in working towards its search objectives.

This is especially true for companies that need to manage the following:

• Locations
• Products
• Services
• Providers
• Questions and Answers
• Brands
• Structured business information

ExpertRec: Search and eCommerce discovery platform

ExpertRec has been designed according to the real-life search needs of sites and online shops.

The indexing mechanism used in ExpertRec supports site crawling, product feeds, APIs, and integrations. The search customization allows using titles, SKUs, categories, tags, and custom fields as searchable fields.

This means that eCommerce businesses have the following process:

Shopify/Product feed → Product index → Search and Semantic retrieval → Filters and merchandising → Search results → Number of clicks/add to cart

This structure helps to have a search platform directly related to product search and conversion optimization.

AI-powered eCommerce search and product discovery

Improve product discovery with ExpertRec

ExpertRec brings semantic search, searchable product fields, filters, merchandising, autocomplete, and search analytics together to help eCommerce teams build more relevant search experiences.

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AI search • Semantic retrieval • eCommerce discovery • Search analytics

Relevance and Ranking: What Happens After a Query?

Let us take the example of the query: “black running shoes under 5000 rupees for men”

A basic keyword search engine will search for words that provide a match.

A modern search engine has to know the following:

• Product category – running shoes
• Color – black
• Target audience– men
• Price restrictions – under 5000 rupees
• Potential synonyms – sneakers, trainers
• Search intent – discovery of the product

Algolia has advanced relevance capabilities, including typo detection, synonyms, and rules for ranking products.

ExpertRec has various weights for many fields and allows merchants to assign weights based on importance. It can also be programmed for basic filtering and allows complex operations.

For example, appropriate expert configuration will give priority to:

• SKU → High priority
• Product name → High priority
• Brand → Medium priority
• Description → Medium priority
• Tags → Lower priority

This is important when the catalog consists of thousands of products with similar descriptions.

AI and Semantic Search

Lexical searching relies mainly on finding the same words in a query and the indexed material.

Semantic search, however, incorporates meaning and intention into the search. For example,

Consider the phrase “shoes for long commute at work.” The person searching might never type the exact words in the name of your product, but the semantic search engine can identify it based on its attributes, which may include:

• Softness
• Light weight
• Walking shoes
• Comfort
• Ergonomics

Algolia combines keyword search with neural search, AI ranking, personalization, and synonyms through artificial intelligence.

Yext uses its Search API to enable different AI-based experiences. Yext’s AI-powered Knowledge Graph supports its website search functionality.

ExpertRec has semantic search, intent finding, agent search, generative answering, and AI tool-based discovery features in addition to conventional keyword search.

What you should be concerned about is not just AI search claims but the actual retrieval system it supports:

Lexical search + Semantic search + Mixed ranking + Structured filtering + Behavioral signals

eCommerce Search and Merchandising

In the realm of eCommerce, search quality is just part of the whole.

The other part consists of product visibility and commercial promotion.

Let’s take an example of one Indian clothing retailer and its parameters:

• 50,000 products stocked up
• Average order value of ₹2,500
• Search-to-click ratio of 8%
• Search-to-purchase ratio of 3%

Thus, the search engine cannot just deliver the relevant information.

The merchants, in addition to search features, have to use the following:

• Highlight new products
• Promote highly profitable products
• Remove items that are no longer available from the search
• Promote the available products
• Create special ranking for a product collection
• Apply price filters
• Filter the products by brand, shape, size, color, etc.
• Change the search results for specific queries
• Analyze searches without results.

ExpertRec provides search, merchandising, filtering options, search and conversion analytics algorithms, etc.

Algolia allows making rules for merchandising and filtering, tracking analytics, and personalizing the search query results.

Thus, for an eCommerce specialist, the cycle will be as follows: retrieval –> filtering –> ranking –> merchandising –> conversion calculation.

APIs and Headless Search

Online shopping websites can greatly benefit from utilizing an API layer. For instance, Algolia provides search APIs, customer interaction libraries, integration tools, and JavaScript libraries for search purposes. Their system entails the separation of indexing the data itself, search criteria configuration, and interface variety.

Yext provides management APIs as well as content distribution APIs and search APIs. Their innovation is hosting a content distribution API to let users get access to the content of apps, while at the same time using the search API for managing the search process.

ExpertRec is also responsible for developing a REST API and a built-in JavaScript SDK, as well as doing integrations for WooCommerce, Shopify, Magento (Adobe Commerce), BigCommerce, etc.

In a headless environment, the flow is as follows:

When designing a relevant search mechanism, an engineer has to address:

• API latency
• Index frequency
• Throughput of queries
• Authentication procedures
• Filtering syntax
• Ranking parameters
• Strategy for reindexing
• Scalability parameters

Search Analytics: From Queries to Revenue

Search analysis should give answers to more than just:

What did users search for?

A good search analysis tool should show:

• Most searched queries
• Queries resulting in no hits
• Queries with no clicks
• Search CTR (Click-Through Rate)
• Changes in searched queries
• Conversions after searches
• Add-to-cart activity
• Contribution from search to revenues
• Popular searches

ExpertRec provides reports on search queries and zero-hits analysis, and also tracks clicks and conversions and calculates profits from search.

Algolia does the same thing: provides search analytics and tools for analysis of user searches.

For instance, let’s say a shop on Shopify receives 10,000 searches per month.

If the number of unproductive inquiries in this store is 1500, then the search department has an area for improvement to work on.

As a result of improving the use of synonyms, the searchable fields, and product data, the effectiveness of the searches went from 85% to 96%.

Now let’s turn to the business question: whether having more searches that succeeded resulted in more visitors clicking on the links, creating their carts, and making purchases. That is where search analytics tools go beyond simply measuring the speed of the query response from the API.

Technical Example: A ₹5,000 Product Search

Think of a store that offers a product like:

“Sony Wireless Noise Cancelling Headphones – ₹4799”

A shopper will inquire:

“wireless headset less than 5000 with noise cancellation”

A technologically advanced search process can provide the answer in multiple stages.

Understanding of the query

Aspects of “wireless headset” – the customer’s intention in the product or category

“less than 5000” – price constraint

“noise cancellation” – quality of the product

Retrieval

Finding suitable products based on keywords and/or semantic search

Filtering

Setting:

price ≤ ₹5000

Ranking

Considering the product relevance, availability, attributes, and business rules

Analytics

Collecting data:

query → result → click → add-to-cart → purchase

This is what makes the search box different from search-based product discovery systems.

Which Architecture Fits Your Use Case?

The three potential solutions can be examined considering the issue at hand.

Algolia is useful when technical teams need programmable search capabilities enabled by APIs, customized relevance, libraries, reports, and AI search engine technologies.

Yext is important for working with semantic entities and Knowledge Graph data that involves queries about places, services, products, and other entities.

ExpertRec can be found useful by companies that have a need for a search layer with eCommerce search capabilities, flexible product fields, semantic search capabilities, various filters, marketing, analysis, and integration possibilities.

For instance, when assessing options for a Shopify seller, it is essential to find out:

1. How product information is being indexed
2. How soon the index is updated
3. Whether it is possible to search for SKU and metafields
4. How semantic extraction is used
5. How ranking is connected with filtering
6. Understanding of marketing rules
7. How conversions take place
8. Whether assistance of developers is necessary

Conclusion

The same conclusion can be drawn about comparisons between Algolia, Yext, and ExpertRec. Algolia represents programmable searching and relevance management; Yext leverages knowledge in structured form and offers entity-based user experience; finally, ExpertRec embodies the combination of traditional search functions together with AI-search engine capabilities (eCommerce search management, search semantic analysis, likes of product filtering and merchandising, recommendations, and conversion analytics).

When selecting the right search engine for your purpose, it is crucial to understand that technical assessment cannot be limited to just counting features. Be sure to test your search engine with some actual queries.

It would get you some sample data based on successfully testing 100-500 queries through the following steps:

Query → Relevant Result → Click → Add to Cart → Purchase

Thus, we give our engineering and eCommerce teams much more valuable information for making search quality assessments compared to just looking at available features.