In an effort to streamline the often cumbersome process of creating case chronologies, Austin-based ChronoTracer has launched a platform aimed at automating this task for litigation and investigation teams. Founded by Richard Gorelick, a former attorney, and Dexter Weiss, a software engineer, the startup seeks to address the inefficiencies that arise when legal teams manually compile fact chronologies from vast amounts of digital evidence.
ChronoTracer is designed to import digital evidence in various formats such as emails, phone records, text messages, social media posts, voicemails, financial records, and web search histories. It transforms all of these into structured, time-ordered databases, which can then be filtered and searched simultaneously. The platform extracts multiple chronological events from individual documents and offers features like automatic translation and transcription, event-level deduplication, and identity matching.
Gorelick, whose pedigree includes a tenure at Cadwalader, Wickersham & Taft and co-founding AI-driven trading firm RGM Advisors, explained that existing chronology tools often have limitations requiring users to manually tag items during document review. Unlike these, ChronoTracer promises a comprehensive chronology built from all processed evidence, making it easy to narrow down events that meet specific criteria during legal proceedings.
ChronoTracer is already in use among Am Law 100 firms and other legal boutiques for various stages of litigation, including early case assessment and discovery. It aims to complement existing e-discovery platforms like Relativity and Disco by accepting data exports in multiple formats.
The company’s pricing strategy involves setup fees based on evidence complexity and volume, along with monthly fees for hosting and support. While currently employing manual tagging, ChronoTracer plans to incorporate more AI-driven features in the near future to enhance its capabilities in data processing and analysis.
This launch illustrates the wider trend in legal tech towards automating tasks that have traditionally relied on manual labor, offering legal professionals more efficient ways to manage and analyze large volumes of data for better litigation outcomes.