Generative AI for chat eDiscovery

Chat and messaging data - Slack, Microsoft Teams, WhatsApp, SMS - is now routine in discovery, and it behaves very differently from email. Conversations are fragmented, informal, full of emoji and shorthand, and run continuously rather than in tidy documents. Generative AI is well suited to making sense of this format.

Applied to chat eDiscovery, generative AI can:

• Summarize long or multi-participant threads so reviewers grasp them quickly

• Extract key facts, participants, and dates to support a chronology

• Answer natural-language questions about what was discussed across a data set

• Group related conversations and surface the messages most likely to be relevant

A few cautions matter. Chat review depends on sensible grouping of continuous conversations into reviewable units, and any AI output should stay linked to the underlying messages so it remains verifiable and defensible. As with all generative AI, results should be checked rather than trusted blindly, and the data should stay in a secure, access-controlled environment.

Used carefully, generative AI shifts chat review from scrolling through endless message logs to working with summarized, fact-tagged conversations - faster, and easier to defend.

For a practical walkthrough, see How to Include AI Chat History in eDiscovery.

Claira is an AI eDiscovery platform that applies these techniques to chat and document review, including summarization and fact extraction across large conversation sets. See it in action.