Revolutionize your search process using Rinalogy Adapta

See how Rinalogy Adapta can take the guess work out of predictive coding and find the documents you are looking for.

Rinalogy’s Adapta Continuous Active Learning (CAL) application demonstrates superior performance over Predict (TAR) for document review due to continuous input from users, continuous learning and continuous ranking. In addition, Adapta does not require a seed set which saves time, reduces cost and enables building a more accurate model.

Adapta uses the same unique query model as used in Rinalogy Search. The query model is used to launch an initial query to generate documents and to start a learning process. The query model allows for both finding the right documents and displaying them in the order of relevance faster and with higher quality of results.

Adapta incorporates machine learning capabilities and enables its users to actively train the software over time. Using Adapta users can classify documents in the database by classes or tags and see the model accuracy and quality in real-time.

Users can determine when to stop reviewing documents based of the model quality and run the model forward to classify all of the documents.

In the legal world Adapta is used for predictive coding.

Does Not Require Seed Set

The process starts with a search query.

View Model Quality in real time

The user can determine if the model is of sufficient quality.

Tagging Process

The user can assign tags to each document found by Adapta, the software will learn from the user feedback and generate new and better results.

Faster Results

As a cloud-based solution Adapta analyzes and filters big data quickly.

Plug-in Technology

Can be integrated with other technologies using Rinalogy API.

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