From Prototype AI to Operational Legal Workflows
AI-assisted legal workflows are integrated combinations of software, data, and human review that apply artificial intelligence to recurring legal tasks such as contract analysis and document review, with the goal of making work faster, more accurate, and more defensible while keeping attorneys in control of key decisions. That idea is moving from slideware to reality as major legal technology vendors shift focus from experimental tools to production-ready AI legal software. Relativity’s aiR Assist and Cimplifi’s expanded AI consulting services both target the same gap: legal teams do not just need smarter algorithms, they need reliable and auditable workflows for legal eDiscovery automation and AI contract analysis. For in-house departments under pressure to control risk and cost, these announcements signal that large vendors now want to be partners in how AI is embedded into everyday document and contract work, not only feature providers.
Relativity’s aiR Assist Brings Native AI Into Review
Relativity has moved aiR Assist into general availability within RelativityOne, shortly after acquiring contract AI company Gavel, and is also adding custom analyses in Relativity aiR for Review. aiR Assist lets legal teams ask plain-language questions of their data and receive precise, cited answers, designed to surface key facts as soon as data enters a matter. According to Relativity, aiR Assist can handle up to 300,000 documents per index and 1.5 million documents per workspace, and sits as a native capability rather than a bolt-on tool. CEO Phil Saunders said the platform aims to be “the single, auditable source of truth for every piece of data in your matter,” with outputs “defensible by design.” For in-house teams, that matters as AI moves closer to the core of discovery strategy and case assessment.
Custom Analyses and Contract AI Tools in Review
Relativity’s custom analyses extend aiR for Review beyond prebuilt AI models, letting legal teams define document review analyses in plain language without coding or technical setup. That matters for teams dealing with diverse issues ranging from internal investigations to contract AI tools that classify clauses or flag risk language. Custom analyses are aimed at identifying and classifying key content across millions of documents, with outputs that reviewers can check and defend. This is where AI contract analysis and discovery converge: the same environment that supports linear review, analytics, and technology-assisted review now embeds configurable AI logic in everyday workflows. For in-house legal teams, this reduces dependence on bespoke scripts or separate AI pilots, and instead pulls experimentation into the main platform, where governance, permissions, and audit trails are already in place.
Cimplifi Focuses on AI Orchestration and Defensibility
Cimplifi is expanding its AI consulting, advisory, and legal engineering services to help organizations move from AI experimentation to real-world results in eDiscovery and contract analytics. The company positions itself as an AI orchestration partner, covering strategy, workflow design, model deployment, integration, and managed service delivery. General counsel Marla Crawford said legal teams “need more than technology alone, they need solutions grounded in the realities of legal work.” A multidisciplinary team supports clients with technology-assisted review, predictive modeling, defensible data culling, and enterprise-grade contract intelligence models. By pairing AI with experienced legal professionals and managed review experts, Cimplifi aims to make AI outputs transparent and defensible. For in-house teams, this addresses not only tool selection but also the ongoing management needed to keep AI legal software aligned with litigation, regulatory, and transactional standards.
What In-House Legal Teams Should Do Next
Taken together, Relativity’s product rollout and Cimplifi’s consulting push show that AI in legal work is shifting from isolated pilots to orchestrated workflows. Enterprise legal teams now have vendor-backed frameworks to embed AI into document review, legal eDiscovery automation, and AI contract analysis, with an emphasis on governance and auditability. Practical next steps include mapping which review and contract processes are repetitive enough for AI support, clarifying defensibility requirements, and deciding where platform-native tools like aiR Assist suffice versus where external experts such as Cimplifi are needed. Legal leaders should also build playbooks that define when human review overrides AI suggestions and how to document quality checks. The new wave of AI legal software is less about replacing lawyers and more about constructing reliable, repeatable systems that combine machine speed with legal judgment.
