Relativity is making aiR for Review broadly available with custom analyses that legal teams can build using no-code, natural language prompts to drive AI-assisted document review.

aiR Assist and custom analyses will be standard in RelativityOne by the end of June, deeply integrating AI into the platform to enable scalable analysis across large legal data sets.

Custom analyses in aiR for Review support classification, data extraction, and image analysis across millions of documents, with an iterative prompt experience to test outputs before large-scale runs.

At RelFest London, Relativity outlined a broader platform strategy focused on governance, accessibility across surfaces, and upcoming integrations like AI-native drafting in Microsoft Word that connect work back to RelativityOne data.

Relativity emphasizes collaboration between AI and human expertise within a single governed environment, keeping data inside RelativityOne and planning Word-based drafting that reflects AI outputs.

The company aims to own end-to-end legal workflows by integrating AI features with existing tools, including AI-native drafting in Word with real-time synchronization to RelativityOne after the Gavel acquisition.

Solutions are designed to be native to RelativityOne, ensuring AI outputs live in a single governed environment with auditable governance and traceability.

Relativity frames AI as a native, auditable, governable component of RelativityOne, transforming how legal teams access and act on data.

aiR Assist shifts interaction with data into RelativityOne’s native environment, making AI accessible to non-technical legal professionals with defendable outputs.

aiR Assist works on data already governed in RelativityOne and supports up to 300,000 documents per index and 1.5 million per workspace.

aiR Assist provides natural-language querying with citation-backed answers that link documents, relationships, and evidence, maintaining traceability and defensibility for courts and regulators.

Plain-language questions in aiR Assist generate results that connect documents and evidence across the dataset, within the platform’s governed environment.