Rinalogy Search - 2025 Pricing, Features, Reviews & Alternatives

Rinalogy Search is an NLP/ML empowered enterprise search and data discovery solution that finds critical information with speed and accuracy for organizations of all sizes. Our approach uses machine learning during each session, as the software creates models based on user feedback to identify relevant documents. Using familiar feedback inputs, thumbs up/thumbs down to label relevant/not relevant results, users take control of the search process to find exactly what they are looking for.

Rinalogy Search makes identifying highly relevant content easier than ever.

Using connectors to unique data and knowledge sources, Rinalogy users enter a single query that can be executed across multiple external and/or internal content sources simultaneously. Complex Boolean queries are not necessary – a simple one does the job.

Rinalogy is integrated with popular data sources such as Google, Bing, Sharepoint, Elasticsearch, MS SQL, Search.gov (such as DOJ, DOL, HHS, DHS, FDA, and others), US States Attorney’s Offices news feeds (93 sites), Google Patents, Windows and Linux network file servers, and others. A fully customizable, scalable solution, Rinalogy Search can be tailored to any business need, including the development of additional data connectors to integrate other data sources based on client needs.

Rinalogy Search does not need to ingest, copy, index or move data from its location and it does not store document content. Users have the ability to find similar documents by content by uploading one or more existing sample documents. They also have control over content location on local networks to search all folders or a selected subset of folders, or to exclude a set of folders from the search process. Rinalogy can be synchronized with Active Directory or a license server to securely manage user authentication and access to data.

Further refinement of the search process can be achieved by assigning weights to query terms.

Rinalogy Search saves search history for future reference or collaboration. User-created models are stored and can be accessed by the user or a group of licensed users.