Your users don't search in a vacuum. They are seeking information in order to complete a task or transaction, and more often than not, at some point they need to collaborate with colleagues. Dahu Surface supports collaboration by enabling users to perform many actions directly on the results list.
Users can share insights or follow a workflow by applying comments to hits from a result list, either for their own use or shared with other users. Comments are presented inline in time-order, and can optionally be added to the searchable text for hits to make them easier to find.
One very common search pattern is to re-find a specific document - maybe because its location in a file-system or DMS is not so easy to remember. Users can mark hits with flags, which are indexed, so they can immediately re-find important documents. Flagging can also be used for classifying hits - for example tag hits as 'important' to boost them in future searches. If you are using search to support a data migration project, a combination of tagging with a 'migrate' flag followed by exporting to CSV gives you an instant report of content to migrate.
Surface lets users define one or more 'working sets' of data - much like a shopping basket feature. As you run searches and find interesting matches, gather them in one of your own curated stored hit lists - especially useful when creating an export of data for Freedom of Information or GDPR Subject Access requests.
Share your queries with others via email directly from the search application.
The best search engines support a huge range of functions that go way beyond the traditional search model of "enter query, examine results, select one".
Smart query suggestions can combine single-word autocomplete/spell corrections with query recommendations, making use of the best collaborative filtering and AI driven recommendations that your search engine provides.
Users seamlessly mix retrieval with browsing, choosing the most effective refinement types for the given schema - flat lists, multi-select lists, hierarchical file plans, even visualisations such as pie and bar charts. Users can even switch the display of individual facets, for example from a flat list to a pie chart, depending on their current task.
Surface always displays a comprehensive breadcrumb trail showing the search terms, refinements, filters and constraints that have been applied to the current search context so you never get lost in your data.
Surface is designed awith reference to the latest research in search-driven User Experience and Design.
Dahu Surface works with your existing search technology if you have one, or one of a range of leading search technology platforms. Our back-end server can be configured to make use of the latet and greatest search features, preserving your investment if you've already made one. If you are thinking of a new project, then we don't tie you a particular platform.
Apache Solr is an open-source search server has been around for almost a decade and a half, making it a mature product with a huge user community. With full-text search, faceting, near real-time indexing & high availability and massive scaleability, Solr is a fine choice for an Enterprise Search engine.
With a powerful query language, support for high availability, geo-search and horizontal scaling, Elastic Search in both its commercial and community versions can support the most demanding Enterprise Search requirements.
Unlike traditional keyword search, Yext leverages multiple advanced NLP algorithms to deliver a modern, exceptional search experience. It can provide answers to complex user questions against unstructured long-form documents.
Algolia is a powerful enterprise search engine hosted in the cloud. Its easy to setup, requires minimum on-going resources to manage and provides all the features you'd expect of an Enterprise Search engine.