Sightline Continous Active Learning feature

The introduction of Continuous Active Learning (CAL) in Sightline aimed to address several key pain points that users encountered with TAR 1.0. Previously, users relied heavily on client success managers to configure and manage their models, leading to bottlenecks, delays, and a lack of real-time control over their own eDiscovery processes. Many users wanted more transparency into how the model was learning, the ability to adjust training data dynamically, and greater flexibility in prioritizing key documents. With the new version of TAR powered by CAL, users now have direct access to manage and train their own models without external dependencies. This self-service capability allows legal teams to fine-tune their model performance in real time, reducing turnaround times and increasing efficiency. The system continuously learns from reviewer feedback, improving accuracy as the review progresses. Additionally, built-in analytics and reporting provide insights into model performance, helping users make data-driven decisions. By integrating this enhanced CAL feature, Sightline empowers users with a more intuitive and efficient workflow, ensuring greater control, transparency, and cost-effectiveness in their eDiscovery processes.