Lasya Marla, Director of Fusion AI at Lucidworks, a search technology company that utilizes AI to better understand human intent, provides a thorough overview of the data optimization improvements AI-based technology is delivering. Lucidworks’ platform is comprised of three major areas, Fusion AI, Fusion App Studio, and Fusion Server. Fusion AI facilitates customer experiences and is driven by signal capture and relevance boosting using advanced AI machine learning. Fusion App Studio helps to create made-to-order, data-heavy searches and applications for the web and mobile technology products. Finally, Fusion Server is a search engine and NoSQL data store that provides immediate access to a person’s or company’s data, available when it is needed, and it can be scaled for the needs of the user.  The technology director delivers an overview of ecommerce and retail website searches and queries, as well as other types of customer-based websites, and how advance machine learning can greatly enhance their users’ experiences. Marla explains some of the reasons why search results can be less than desired, such as one of the dominating issues—the engine does not know what the user is searching for thus it cannot perform efficiently. She gives us an insightful look into the inner-workings of a typical search, and the problems that could arise that compromise or devalue the end results. One such issue, Marla explains, could be within the algorithm itself, and its ranking of less desired results on a higher level. For example, when one searches for a particular product, a search engine may rank accessories for that product higher in the search results due to the fact that the product name may be listed more often in the product’s accessories’ descriptions. This could result in a user receiving higher ranked product accessory links than the links to the actual product itself.  The Fusion AI director discusses the demands that users place on search engines and how users’ expectations are driving the need to develop engines that are intelligent. Marla states users expect much more; users want recommendations, and they want the engine to understand their intent, to be able to understand misspellings, etc. and still deliver relevant results. She expounds upon the methods an individual or company can use to train a made-to-order search engine model to meet the specific needs of their site’s users. One such method would be to utilize modified data that is then applied and implemented into the associated query pipeline. Marla explains how companies like Amazon have raised the bar for user search experiences, but many of the features that users love on sites like Amazon, such as user recommendations can be implemented for any business site. Companies and businesses can run recommendation algorithms on their sites’ search engines to increase user enjoyment, efficiency of experience, and ultimately keep the user on the site longer. Marla explains the process an individual would go through in order to utilize Lucidworks’ advanced technology algorithms. For example, if an individual wanted to introduce specific algorithms onto their site and search engine, perhaps a head/tail analysis, query intent classifier, personalization module, recommendations module, etc., the individual would follow simple steps in order to retool their engine. Essentially, to migrate an ecommerce or other site into Lucidworks’ platform an individual would first provide Lucidworks with all their actionable data, including valid signal data comprised of relevant user interactions. Next, they would select the desired algorithms to be applied, and then align their front-end site with the API endpoints generated by Lucidworks’ proprietary technology. By streamlining the process, Lucidworks can facilitate a company’s needs by allowing the retooling to be somewhat automated, in that a search developer won’t need to necessarily understand what is, ‘under the hood,’ in order to kee