Algolia Acquires to Enable Users to ‘Search As They Think’

Data search platform Algolia announced it has acquired San Francisco-based neural search startup

Algolia is known for its keyword-based search and discovery platform that processes trillions of queries from hundreds of global data centers and is “API-first,” or optimized for client applications.’s core product is a vector search engine dubbed Neuralsearch that is built on an AI algorithmic processing engine that utilizes neural hashing on top of vectors. Algolia will combine its keyword search and Neuralsearch into a single API.

Algolia says it will stay true to its mission to make search and discovery intuitive, fast, and scalable with affordability and ease of use in mind. The company claims this acquisition will allow it to provide the first and only API-first search and discovery platform with a hybrid search engine comprised of both keyword and semantic search in one API.

“Our mission, vision, and purpose is powering discovery. We’ve done this to date largely with keyword search. With the addition of the vector search engine from, we’re going to disrupt the search market significantly,” said Algolia CEO Bernadette Nixon. “We’ll be the only product on the market that combines keyword search with vector-based semantic and image search, along with vector-based recommendations. Vendor consolidation is back in vogue, and being able to get best in class capabilities from one provider is powerful in today’s economic climate.”

Vector search is a technique in which an AI engine attempts to match an input term to what is known as a vector, or an array of features generated from an object catalog. The features are derived from the objects in the catalog through a machine learning model that converts these object features into a two-dimensional vector with up to hundreds of dimensions.’s Neuralsearch is a bit more complex than your typical vector search, however, as it utilizes vector search in combination with neural network-created hashes. The company claims this technique delivers speedier and more accurate results than vector search alone. Hashing is a type of data retrieval that relies on statistical properties of key and function interaction and is valued for its efficiency when it comes to compute and storage resources. In a blog post, CEO Hamish Ogilvy explains how neural networks can optimize hash functions that, compared to an original vector, can retain almost perfect information much more quickly and with a smaller storage footprint.

This example from’s website explains how Neuralsearch understands a customer’s intent when searching for a product. Source:

“Artificial intelligence has been built on the back of vector arithmetic. Recent advances show for certain AI applications this can actually be drastically outperformed (memory, speed, etc) by other binary representations (such as neural hashes) without significant accuracy trade-off,” said Ogilvy. “Once you work with things like neural hashes, it becomes apparent many areas of AI can move away from vectors to hash-based structures and trigger an enormous speed up in AI advancement.”

Algolia and’s combination of keyword search and vector-based semantic search will allow users to search either in specific keywords or natural human expressions, which Algolia says provides users with the ability to “search as they think.” Additionally, the company says it is addressing the problem of long tail search queries, or those keyword phrases that tend to be longer and highly specific and are associated with customers who are close to making a purchase. These queries have traditionally been harder to account for.

“Industry-wide, retailers are leaving money on the table because it’s challenging to capture revenue from long tail search queries (such as ‘stunning fall outfit for mother of the bride’), which could potentially represent up to 55% of all search queries today,” noted Nixon. “These low volume searches could collectively amount to millions of queries corresponding to millions of dollars in unfulfilled sales of less popular or searched for products. Our new Algolia hybrid search engine solves this long tail problem – truly putting search on autopilot at a price point that is 90% less than other vector-based search options.”

According to CEO Ogilvy, all employees were offered new roles within Algolia, which reflects Algolia’s current hiring spree. The company says it has created over 145 new jobs in Q2 and has doubled its employee count in the last year.

“Welcoming the team and launching our hybrid search engine represents the start of Algolia’s next chapter,” said Nixon. “Integrating vector search with our keyword search provides a groundbreaking launchpad for us to solve more of our customers’ search and discovery problems and deliver a price advantage they won’t see elsewhere.”

“We are delighted to be joining a world-class leader in search and discovery,” said Ogilvy. “Delivering on the promise of AI search has traditionally required tremendous internal expertise and engineering resources to work effectively. Beyond delivering better search experiences, this must also be done reliably, fast and cost effectively. Algolia has led the world in delivering highly redundant, globally distributed instant search using more than 100 data centers worldwide. This global search distribution network combined with vector-based semantic search using extremely fast and efficient neural hash technology is an exciting and truly unique solution.”

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Algolia Launches Additional AI Models To Boost User Engagement for Businesses

Author: Subham

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