Search Any Content
SearchAssist supports a wide range of data sources for building your search index. This includes website crawlers, structured data, unstructured documents, catalogs, FAQs and knowledge bases. SearchAssist also provides prebuilt integrations with popular commerce and content platforms with full support for automatic sync and document access control policies. SearchAssist’s federated search capabilities allow you to leverage the search results from any of your existing business systems.
Engage with Virtual Assistants
Enhance the search experience for your users by seamlessly engaging them with intelligent virtual assistant actions. The virtual assistants designed in Kore.ai’s Experience Optimization(XO) platform can be integrated effortlessly in a few clicks with nearly no coding. Drive engagement, offer self-service automation and increase conversions by contextually engaging the virtual assistant actions.
AI-powered Relevance Optimization & Recommendations
The powerful index workbench helps in organizing and transforming your data with AI-powered index stages. Extract entities, identify traits, transform natural language inputs to business rules and more. The dynamic faceted search help your customers easily find the relevant results. The SearchAssist recommendations engine helps you in proactively offering personalized products and services to your customers.
Engaging Search User Interface
The search user interface plays a very critical role in delivering great search experiences for your customers. With SearchAssist, you can build engaging search experiences using the traditional search bar mode or the assistant mode for your apps, websites or portals. SearchAssist provides predefined templates for various use cases and also allows you to fully customize the search widget and search results.
Understand what your customers are looking for and optimize the search results with SearchAssist’s actionable click and search analytics. Get a deeper understanding of works for your customers by launching the A/B experiments using variations of the index and search configurations.