A legal firm faced significant challenges reviewing large volumes of claimant documents for mass tort cases. Legal teams had to manually search through extensive records to identify disease mentions, detect relevant medical conditions, and connect them to supporting settlement evidence. This process was time-consuming, inconsistent, and difficult to scale as case volumes continued to grow.
Manual review also created challenges in maintaining fairness, accuracy, and efficiency across claim evaluations. The firm needed a faster and more reliable way to identify relevant evidence while reducing the burden on legal review teams.
To streamline claims review, Infoganan introduced an AI-powered document automation solution using Named Entity Recognition (NER) and evidence scoring models. The system automatically scanned claimant documents, identified disease-related terms and medical conditions, and converted unstructured text into structured, machine-readable data.
Using co-reference resolution, the solution linked relevant evidence across documents and connected related information to support claim evaluation. Scoring models were then applied to assess and rank the strength of evidence connections, helping legal teams prioritize high-value claims and review materials more efficiently.
The AI-powered solution increased evidence identification accuracy to 91% and accelerated review speed by 5X. With the ability to process more than 200,000 pages each month, the firm significantly improved the scalability and consistency of its claims review process.
As a result, the firm saved more than 10,000 review hours annually, enabling legal teams to focus on strategic case analysis instead of repetitive document review. The solution improved operational efficiency, strengthened evidence evaluation, and supported faster, more reliable mass tort claim processing.
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