SearchUnify’s algorithms blend user data, machine learning and natural language understanding to learn on the go and present the most relevant results
Machine learning algorithms study search data across users, geo-locations and touchpoints, and the engine self-learns which results will lead to the most conversions for a given search query. The more you search, the smarter it gets!
Artificial Intelligence takes into account users’ access permissions, search history and journey on a platform, ensuring that the content they are served by our search engine is tailored to their needs. Users are more likely to click on results that are targeted to them.
SearchUnify uses NLU (Natural Language Understanding) to ascertain the context of each search query, rather than mapping results to search strings using only keywords. A robust library of synonyms, that is manually configurable as well as self-educating, drives cognitive processes that can identify precisely what you mean, just like a human being.
Our AI-driven chatbot, Sarah, can drive contextual conversations with customers and solve frequent queries effectively, making service operations cost-effective. Users can access search results in their chat windows too – benefitting from enhanced relevance in their interactions with Sarah.
Relevant suggestions and an enhanced auto-complete functionality ensure that users spend minimal effort to get to the answers they are looking for, enhancing their discovery experience.
A semantic knowledge graph is a segment of information, alongside search results, highlighting the most relevant search result at the top with other related queries and information at the bottom.
