Relativity

Relativity's RelativityOne platform serves the world's largest law firms, corporations, and government agencies managing massive volumes of litigation evidence — from executive emails and financial spreadsheets to WhatsApp messages and body camera footage. As generative AI matured, clients demanded faster, more accurate document review, but legal professionals face strict obligations to regulatory bodies around how potential evidence is handled. The first question in every client conversation was where data would be processed and who could access it. Without guarantees on regional data residency and model training exclusions, AI adoption was effectively blocked before it began — leaving attorneys to manually review 30–50 documents per hour on cases involving hundreds of millions of records.

Relativity partnered with Microsoft Azure to embed large language models and generative AI directly into RelativityOne, building two flagship products: aiR for Review, which uses Azure OpenAI models in Microsoft Foundry to analyze and classify documents at scale, and aiR for Case Strategy, which extracts key facts from evidence and organizes them into timelines, summaries, and draft work product. Azure's global infrastructure enforced customer-selected data processing regions — guaranteeing data never crossed regional boundaries — while Microsoft committed to never training models on customer data. Azure Data Lake Storage served as the foundational data layer, handling native document storage and the metadata powering core services for matters with hundreds of millions of records. This architecture removed the trust objections that had previously blocked AI adoption conversations entirely.

aiR for Review processes legal documents up to 300x faster than human attorneys, with higher accuracy and greater consistency across high-volume matters. Since launch, the platform has supported over 100 million legal decisions, enabling legal teams to take on cases previously turned down due to document volume or timeline constraints. Relativity simultaneously reduced its Azure infrastructure costs by 73% over five years while increasing throughput and import speeds — demonstrating that capability expansion and cost discipline are mutually achievable with disciplined data architecture. The platform now serves clients across the US, EU, Canada, UK, Australia, Japan, South Africa, Brazil, and additional regions, with Azure's global footprint enabling compliant deployment in each jurisdiction.

• Data residency is the threshold requirement for legal AI adoption — establishing regional processing guarantees before any capability discussion removes the objection that otherwise dominates every sales conversation.
• Speed and accuracy compound each other's value: a 300x throughput gain only converts to business impact when it's paired with accuracy rates attorneys can stake their professional obligations on.
• Infrastructure cost reduction and capability growth are compatible goals — Azure Data Lake's disciplined data layer enabled a 73% cost reduction alongside rising throughput, not in spite of it.
• Agentic AI roadmaps require a trust foundation first: Relativity's ability to explore GPT-5 reasoning and further agentic features rests on the compliance and security architecture established in the initial deployment.