Case Study: Pharmaceutical Manufacturing Company achieves 70% reduction in document review time with OpenText Insight Predict

Corporation cuts review by 70 percent with OpenText Insight Predict’s continuous active learning

A generic pharmaceutical manufacturer defending a patent‑infringement suit faced a document review of about 40,800 items. The client only approved technology‑assisted review after the law firm had already manually reviewed roughly 18,200 documents, raising the question of whether TAR could still deliver value so late in the process.

OpenText Insight Predict used those already‑coded documents to train its continuous active learning engine, automatically batching and continuously re‑ranking the population as reviewers worked. The team reviewed an additional 6,800 documents and avoided reviewing 15,800 others—eliminating 70% of the remaining review (40% of the full set), achieving high statistical recall and saving the client nearly $70,000 even after TAR costs.