AI-Driven Search Powered by Solr

Create Powerful, Enterprise-wide Cognitive Search Solutions

Five Ways BA Insight for Solr Delivers High Quality Search:

Your choice of integration with Microsoft Cognitive Services, Google Cloud Machine Learning and the Rasa Stack provide intelligence and internet-like capabilities, which include innovative Natural Language Processing to automate extracting key information from documents. The information is then used to generate summaries and categorize and tag all of your enterprise information, including images and videos.

Connections to over 70 enterprise systems through our highly scalable, purpose-built platform upon which we build ingestion connectors. It can also be used by our customers to develop their own connectors.

Personalized experience through capabilities such as Natural Language Query, Bots, and Machine Learning-based recommendations.  Users are presented with results specifically tailored to their attributes and behaviors.

In-App Search takes AI-driven search directly to the users from within the apps they use every day, such as ServiceNow, Teams, Salesforce, NetDocuments, Dynamics, and Outlook.  By seamlessly integrating into their existing work practices, users don’t need to learn a new application or change their processes. They simply get better information – faster and easier.

With a single query, find relevant documents as well as relevant pages within those documents. Remove the need to download, open, and search again, saving valuable time.  This interface works the same for all users across documents, emails, attachments, and more.

How BA Insight Enhances and Extends Solr

Feature Description Solr   standalone BA Insight for Solr
Search Bot The search bot leverages analytics data to suggest a query previously executed by other users, suggest removal of a filter, or suggest a more restrictive query depending on the situation.

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Natural Language Query Users can find information by making requests as if they were speaking to a person. This eliminates the need for them to learn complex query syntax in order to find the specific information they seek.

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Personalized and Relevant Information Delivery Tap into each user’s context to provide personalized dashboards and highly relevant results.

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Machine Learning-Based Recommendations. Includes capabilities such as Autocomplete, similar searches, and recommendations to make search smarter.

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Out-of-the-box connectors Connect to content repositories, applications, and databases with 70+ out of the box connectors.

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Document Summary Generate a document summary so users can quickly determine whether a document which appears in their search results is relevant to their current needs.

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Similar Documents AI-based linguistic analysis is used to determine the key concepts contained within documents, which is then leveraged to deliver additional documents which contain the same concepts.

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Natural Language Processing

Entity Extraction.  Automatically generates a list of key ideas to be used for search refinement and to identify similar documents.

Document Summary Generation.  Automatically summarizes all types of unstructured content so that users can quickly understand the meaning of search results.

Sentiment Analysis.  Detects the tone with which text is written to determine if it is positive, negative, or neutral.

Language Detection.  Tags content with the language in which it is written.

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Taxonomy Training Machine Learning is used to analyze sample documents associated with taxonomy nodes and create training sets, which are used to automatically tag content. Machine Learning-based tagging can be used either in conjunction with or in place of rules-based tagging so that it can be optimized for each use case.

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Image and Video Tagging Automatic Tagging.  Automatic tagging and classification of media content.

Transcript Generation.  Video transcript generation converts speech within videos into searchable text.

Image Extraction.  Extraction of images and videos from within documents for analysis so that embedded media can be analyzed and tagged.

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Internet-Like User Experience A complete UI framework provides many features including facets, intelligent type-ahead, and configurable results presentation.

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Type-Ahead Dynamically generate type-ahead suggestions of multiple types. Category Matches will search available metadata and suggest matching values, applying Machine Learning to analytics data to deliver suggested queries based on previous searches. Suggestions of People or Content results match what the user is typing. Extensible for delivering suggestions of other types as well.

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Responsive Delivery Responsive out-of-the-box UI for optimized delivery on mobile devices, including phones and tablets.

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Document Previews With a single query, find relevant documents as well as relevant pages within those documents. Each page is fully hit highlighted, using color coding for different terms. Search inside or navigate using a dynamic hit-map to rapidly evaluate content for relevance and locate information deep inside documents.

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Workspace Save results from a search session to the workspace and tag pages for reference and future work. Arrange,share, and export selections from the persistent workspace; allowing work to continue  seamlessly from the office PC to your iPad at home.

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Content Assembly Content assembly allows the creation of composite documents. Create Word documents, PowerPoint presentations, PDFs, Excel manifests, and ZIP files using search derived content, while preserving images and native formatting and maintaining references to the original source content.

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Analytics Derive real-time reports from user interactions including searches, interactions with content, queries, and more to provide administrators with insight that will help them improve user experience and engagement.

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