What are AI-based eDiscovery platforms?

The eDiscovery market now includes two broad approaches to AI. Established platforms have added AI features - predictive coding, analytics, and more recently generative AI assistants - on top of their existing review workspaces. AI-based (or AI-native) platforms take the opposite path: the AI does the first pass of analysis, and the review workflow is designed around its output.

On an AI-based platform, core tasks typically look like this:

• Documents are classified for relevance, privilege, or issue categories as they enter the workspace.

• Long records and message threads arrive already summarized, with key facts extracted and linked to their sources.

• Reviewers spend their time confirming and refining AI output rather than reading every document cold.

The practical questions to ask when evaluating any platform in this category are the same ones that apply to all legal technology: whether the output is explainable and auditable, whether humans keep final control of coding decisions, how the vendor handles security and data residency, and how the platform performs on a representative sample of your own data. Fit with your existing tools matters too - some AI platforms replace the review environment entirely, while others plug into established systems your team already uses.

For a walkthrough of where AI fits at each stage of the discovery process, see How AI Fits into Modern eDiscovery.

Claira is an AI eDiscovery platform in this category, applying classification, summarization, and fact extraction across large review sets while integrating with existing review workflows. See how it works.