Exa Challenges Google With A Plan To Convert The Web Into A Database

Since Google entered our lives, there hasn’t really been a debate about the leading search engine. It’s been Google all the way, despite capable competitors such as Microsoft, Baidu, DuckDuckGo and others, as well as the continued presence of previous players such as Yahoo!, Yandex, etc.

As of 2024, Google still holds more than 90% of the market, with the closest challenger, Microsoft’s Bing, at 3.19%.

Now, in the age of artificial intelligence (AI) and generative artificial intelligence (GenAI), the borders between the AI and search engine sectors are blurring.

Google, Microsoft, and others are investing in GenAI technologies, as GenAI leaders such as OpenAI, Perplexity, and others are diving into the search engine industry.

About a month ago, OpenAI launched its search feature to compete with Google, a move that came well after the company had trialed a similar product called SearchGPT in July 2024.

Now, AI startup Exa is entering the sector with a new kind of search engine that blends both technologies, as it looks to challenge Google and other search engines.

Exa recently introduced its new product—Websets—which leverages LLM (large language model) technology to generate search results that are more accurate than existing capabilities, as per the company.

Exa also published a video introducing Websets.

The company already provides this service as a back-end service to companies looking to build their own applications.

Websets go beyond what current search engines can do, by allowing users to search for answers that are tougher to find, by compiling a list of more precise, highly specific, and better-quality results.

The aim is to convert a chaotic website ecosystem into a neatly organized database.

“The web is a collection of data, but it’s a mess. There's a Joe Rogan video over here, an Atlantic article over there. There's no organization. But the dream is for the web to feel like a database,” said Will Bryk, the cofounder and CEO of Exa.

Where search engines such as Google crawl websites to build an index of keywords that match up to a user’s query, Exa’s approach crawls the web and encodes web page content using a format called embeddings. These embeddings are then processed by LLMs, which allow the platform to understand the context of the content on webpages, rather than just rely on keywords.

However, this approach comes with its own set of drawbacks.

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For one, the platform is slow at generating results. Where Google takes mere seconds, some searches on Websets can take several minutes. Yet, users seem fine with waiting if the results are of high quality, as per the CEO.

“A lot of our customers started to ask for, like, thousands of results, or tens of thousands,” says Bryk “And they were okay with going to get a cup of coffee and coming back to a huge list.”

Andrew Gao, who used Websets, said, “I find Exa most useful when I don’t know exactly what I’m looking for. For instance, the query ‘an interesting blog post on LLMs in finance’ works better on Exa than Perplexity.”

Ahead of this, the process of encoding pages is also slow and expensive, as compared to keywording them. Currently, Exa has encoded some billion web pages, in contrast to Google, which has indexed around a trillion.

Furthermore, where the search engine has to match exact keywords, such as Will Bryk’s name, the results from embeddings will reflect “a lot of Polish-sounding people, because my last name is Polish and embeddings are bad at matching exact keywords,” as per the CEO.

This is where the platform blends in the use of keywords, that is until the gaps in the embedding method are filled up.

Earlier in the year, Exa also revealed that it had raised a total of $22 million in seed and Series A funding, led by Lightspeed Venture Partners, with participation from NVentures, NVIDIA's venture capital arm, and Y Combinator, to boost its cause.

Do you think Exa’s Websets will be able to challenge industry leaders such as Google, Bing, OpenAI and others?

Let us know in the comments below!

First published on Wed, Dec 4, 2024

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