Hybrid Search's Evolution and How It Can Benefit Your Website

The term “hybrid search” refers to the combination of search technologies, but the technology used to create hybrid search engines is evolving.

Here’s where it stands right now.

Hybrid search is used to refer to a combination of directory and crawler-based search engines.

The term is now used to describe a hybrid of artificial intelligence (AI) and neural hashing.

Traditional on-site search’s complex rules-based understanding is replaced by AI, and neural hashing makes vector-based search as fast as a keyword search.

It may appear complicated at first, but the end result is a simple user search.

With today’s hybrid search technology, a query such as “keep cool in the summer” on an electronic store’s website can return instant results for fans and air conditioners.

If you don’t use hybrid search, your options may be limited to products that contain the words you searched for.

Hybrid search is more accessible for businesses to implement on their own websites, in addition to providing faster and more relevant results.

I recently spoke with Hamish Ogilvy, CEO, and co-founder of Search.io, about how the company is charting a path toward a new future for hybrid search.

He told me about recent advancements in the field and how his company’s new tool makes it easier for customers to conduct searches on company websites. As a result, businesses may be able to increase their sales.

Modern Hybrid Search = AI + Neural Hashing

In search, AI uses dense vectors to encapsulate text meaning instead of strict keyword matching. It has been shown to be more relevant than keywords, but it comes at the cost of slower results.

The use of neural hashing, also known as ‘deep hashing,’ allows the vector-based search to be as fast as a keyword search. It gets its name from its ability to hash vectors using neural networks.

Mathematical expressions are used to compare terms in neural hashes. They compare the distance between words and concepts and assign meaning to the ones that are closest.

What does this imply in terms of application?

According to Ogilvy,

“In practical terms… AI language understanding can be easily implemented in search technology.” Surprisingly, despite the vastly improved relevance, it is often faster than keyword search for any queries.

Neural hashing outperforms dense vector search by 99 percent while being over 100 times faster and taking up a fraction of the space.”

Ogilvy demonstrated how modern solutions fail retailers and customers by showing me several examples of natural language queries with and without neural hashing.

On the retailer Best Buy’s website, a search for “something to keep my beer cold” returns a single, irrelevant result. The same query with neural hashes applied, on the other hand, would return a results page full of beer fridges.

This could mean the difference between a customer walking away empty-handed and a company making a quick sale.

For Businesses, Is Hybrid Search More Accessible Than Ever?

Neuralsearch, a new tool from Search.io, is said to combine the speed of traditional keyword search with the accuracy of vector-based search.

Simply put, it enables websites to return results similar to Google’s without having to hire an army of search engineers.

It eliminates the need for retailers to manually add synonyms to their search index, saving time that could be better spent elsewhere.

Following a private test with a few select organizations, Neuralsearch is now available in a public beta. With a 14-day trial, businesses can add it to their websites for free.

Ogilvy tells me that the following company’s websites are already using Neuralsearch as an example of how it works in practice: