Search.io Rebranded From Sajari, Launches Ecommerce Search Platform Based On New Algorithm

Search.io has rebranded from Sajari and has launched Neuralsearch, a platform to support ecommerce brands.

The platform is poised to help brands increase conversions, revenue, and repeat customers while improving the experience on their websites.

Along with the name, the company has overhauled the brand's look and feel. Changes to the company brand will eventually make their way into the product. For now, to minimize disruption, the company has only updated the logo inside the Sajari Search.io console and set up redirects for its website and key domains.

Search.io Chief Executive Officer and Co-Founder Hamish Ogilvy says until now “retailers have been hamstrung by the complexity of existing solutions on today’s market which requires tremendous internal expertise and resources to work effectively.”

The Australia-based company has a U.S. headquarters in San Francisco. The company works with retailers like Catch.com.au, BBC, Strandbags, MILKRUN, Sennheiser, and Kogan.com.

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Search.io’s Neuralsearch is built on a new type of artificial-intelligence algorithm and processing engine that use neural hashing that combines vector search with fast performance and self-learning capabilities. This hybrid engine delivers combined contextual and keyword-based results in one millisecond no matter the catalog size.

Site search software has been around for more than two decades, but for the most part dominated by a family of search products built on an open-source project called Lucene. Lucene spawned Elasticsearch and Solr, and many other search companies were built on top of those three open source projects.

Search.io took a different approach and built the engine from scratch. The engine needed to be very fast, support real-time index updates, as opposed to immutable indexes, use a distributed service architecture for efficiency and scalability, and it needed to be designed with machine learning and AI capabilities at the core, according to the company. The goal was always to support more embedded machine learning. Today, per the company, the approach is paying off. 

Neuralsearch removes the need for the majority of synonyms and language rules and allows highly relevant results to be delivered out of the box with lower ongoing management. Most search engines can either optimize for speed or accuracy, but with Neuralsearch, customers get both. 

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