Reviewing collaborative chat data for litigation is operationally distinct from email or document discovery. The challenge is reconstructing fragmented channels, threads, and direct messages into coherent narratives while identifying key participants and privileged communications. A custom automation workflow addresses this by programmatically ingesting JSON/export data, applying metadata normalization, and using graph models to map conversational flow and participant influence. This eliminates the manual stitching that consumes paralegal and junior attorney hours, directly reducing first-pass review costs by targeting the most labor-intensive pre-processing phase.




