Fusion applies the power of machine learning and AI at ingest time to enrich the index with clustering and classification, anomaly detection, entity recognition, and sentiment analysis.
Fusion leverages classification algorithms to categorize users and data based upon provided training data or documents fields, as well as clustering algorithms to discover new insights from your raw user signals and documents. Both can be used to create additional features for query interpretation and matching, relevancy boosting, customer segmentation, and search navigation.
Clustering algorithms group users into various demographic categories based on their behavior. Then machine learning can boost the most relevant documents for users within each cluster.
Classifiers augment incoming documents with additional fields that can be used for future queries and suggestions.
Named Entity Recognition (NER) enables extracting known terms, phrases, people, places, and other entities from content and queries allowing much more accurate query interpretation and document matching. With semantic phrases preserved and matched, it allows richer search experiences.
Part of Speech Tagging (POS) helps with “word sense disambiguation.” So when the word “present” is tagged as a noun, subsequent search results omit instances where “present” is a verb for public speaking.
Lucidworks offers pricing across three product tiers, either self-hosted by your team or as a Lucidworks managed service in the cloud with list prices calibrated to expected usage levels.
Our latest release extends Fusion’s cloud-native, microservices architecture to streamline development, simplify operations, and supercharge data science.
See it in action in our upcoming webinar!
Ready to create amazing AI-powered apps? Contact us today to learn how Fusion can help you and our team build search and data discovery applications that dazzle your customers and empower your employees.