RelativityOne/Relativity aiR: Complete Review

RelativityOne/Relativity aiR AI Capabilities & Performance Evidence

Core AI Functionality

RelativityOne/Relativity aiR delivers three primary AI-powered modules designed for distinct legal workflow automation needs. aiR for Review rapidly analyzes documents for relevance, issues, and importance while predicting relevant documents for productions, with vendor claims of analyzing thousands of documents per hour[40]. aiR for Privilege streamlines privilege review processes by identifying key indicators of legal content to distinguish privileged communications, with reported time reductions of up to 80% in some implementations[40][50]. aiR for Case Strategy simplifies case narrative construction through auto-generated facts and visualized fact chronologies[40][42].

The platform operates within Microsoft Azure's OpenAI services framework, providing access to advanced GPT models with continuous updates and improvements[42]. Integration with RelativityOne's existing security infrastructure ensures customer data processing remains within organizational instances without retention by Relativity or Microsoft[40][42][50].

Performance Validation Through Customer Evidence

Customer implementations provide concrete evidence of aiR's performance capabilities across different organizational contexts. JND's government agency project demonstrates significant scale capabilities, completing review of 650,000 documents in one week with only three attorneys for subject matter expertise[47]. The implementation achieved an 80% reduction in project time compared to traditional methods, with aiR for Review analyzing over half a million documents on the first day[47].

Cimplifi's law firm client implementation achieved 50% reduction in review time across 14,000 documents while saving 250+ hours of linear review time[48]. Validation testing through an elusion test found that aiR for Review missed zero responsive documents in a sample reviewed by the expert attorney, though the sample size and testing methodology were not specified[48]. Teneo's forensic investigation project completed a typical multi-month project in just 18 days, achieving reported 70% cost savings using a single reviewer approach[53].

Vendor performance claims indicate greater than 95% recall and greater than 70% precision in document review[40]. However, independent third-party analysis reveals that aiR for Review provides no built-in validation tools, requiring users to pull statistical samples and calculate precision and recall manually[56], making independent verification of these marketing metrics challenging without substantial additional effort.

Competitive Positioning Analysis

RelativityOne/Relativity aiR differentiates from standalone AI legal tools through deep integration with established e-discovery workflows and comprehensive security frameworks[40][42][44]. Unlike general AI tools like ChatGPT that lack legal-specific controls and pose data confidentiality risks, aiR operates within RelativityOne's secure Microsoft Azure environment with SOC 2 Type II, FedRAMP Moderate ATO, HIPAA compliance, and ISO 27001:2022 certification[51].

The platform's integration advantages distinguish it from specialized legal AI vendors like Lex Machina or Pre/Dicta that require separate system implementations. RelativityOne customers can deploy aiR capabilities within existing workflows without additional security reviews or data migration requirements[40][42]. This approach minimizes implementation disruption while leveraging familiar user interfaces and established data processing pipelines.

However, third-party analysis suggests that specialized predictive analytics vendors may demonstrate higher error rates compared to aiR's focused document review capabilities, though specific comparative statistics require independent verification[56]. The platform's effectiveness heavily depends on detailed case knowledge provided in prompting phases, making it less suitable for initial data insights or cases with limited background information[56].

Customer Evidence & Implementation Reality

Customer Success Patterns

Customer evidence reveals consistent success patterns across government agencies, large law firms, and legal service providers. JND's government agency implementation required rapid identification of critical case material from massive document sets with limited resources and tight timelines[47]. The success pattern involved using multiple RelativityOne capabilities to reduce the initial document set by 50% before applying aiR for Review to the remaining 650,000 documents[47].

Legal services providers demonstrate successful implementation patterns for client services delivery. Cimplifi's approach involved working closely with subject matter expert attorneys to develop optimal prompt criteria and validate results through rigorous testing[48]. Teneo's implementation success resulted from iterative workflow development, expanding aiR for Review usage over larger document batches while using quality review output to fine-tune prompt criteria[53].

International adoption patterns show growing global acceptance, with Gilbert + Tobin's Australian implementation highlighting successful expansion beyond U.S. markets[55]. According to Adrian Agius, Legal Informatics Manager at Gilbert + Tobin, "One of the most powerful benefits of using aiR for Review within RelativityOne is that our data is securely housed in Microsoft Azure, a trusted environment for both us and our clients"[55].

Implementation Experiences and Support Quality

Partner organizations provide evidence of implementation support quality through service delivery success. Morae's case study documents a 75% cost reduction for clients using Relativity aiR for Review while meeting tight one-week deadlines[46]. As the longest-running RelativityOne partner globally, Morae's experience provides credible validation of platform capabilities and ongoing support quality[46].

Miller Thomson's Review Center implementation, while not specific to aiR, demonstrates Relativity's broader support capabilities in legal workflow integration[54]. The firm consolidated 13 separate reviews into a single workstream for over 400,000 documents, improving document classification accuracy by 33% and reducing review time by more than 50% compared to manual methods[54].

However, implementation experiences reveal significant complexity requirements. RelativityOne/Relativity aiR implementation requires completion of specific contracting processes and application installation by Relativity to customer instances[50]. The conceptual workflow involves Project Managers creating searches and projects, Annotators confirming AI predictions, and Case Subject Matter Experts finalizing decisions on privilege classifications[50].

Common Implementation Challenges

Third-party analysis reveals substantial implementation challenges requiring careful consideration. The tool's effectiveness heavily depends on detailed case knowledge provided in prompting phases, making it less suitable for initial data insights or cases with limited background information[56]. Users with minimal case details may experience expensive and rudimentary insights, requiring multiple prompt iterations that increase overall costs due to per-document analysis fees[56].

Technical limitations present ongoing operational challenges. Documents over 150KB will likely error and not be analyzed by aiR for Review, representing a significant constraint compared to the 30MB exclusion for other Relativity analytics tools[56]. This size limitation affects comprehensive document analysis and requires manual review for larger documents[56].

Data compatibility issues create additional implementation overhead. Generative AI tools in e-discovery rely on traditional analytics foundations, and documents with poorly extracted text, including images, spreadsheets, and database files, are unlikely to return good insights or accurate classifications[56]. The tool lacks preview capabilities, requiring users to run prompts over chosen populations and incur per-document fees for every analysis iteration[56].

RelativityOne/Relativity aiR Pricing & Commercial Considerations

Investment Analysis and Cost Structure

RelativityOne/Relativity aiR operates on a contact-for-pricing model rather than published pricing tiers[49]. The platform offers flexible licensing options including pay-as-you-go arrangements for quick project starts and one- or three-year commitment options with additional discounts and platform customization capabilities[49]. Variable data tiers help optimize costs at different case stages, though specific pricing details require direct vendor engagement[49].

Per-document analysis fees apply to aiR for Review usage, creating variable costs based on document volumes and analysis iterations[56]. Organizations must budget for multiple analysis runs during prompt optimization phases, as the tool lacks preview capabilities requiring full analysis execution to evaluate prompt effectiveness[56]. This pricing model benefits organizations with predictable document volumes but may create budget uncertainty for variable usage patterns.

Customer evidence suggests significant cost savings potential offsetting licensing fees. Teneo achieved reported 70% cost savings on a one million document project, though the calculation methodology and whether this includes implementation overhead, training costs, and per-document analysis fees was not specified[53]. Morae documented 75% client cost reduction using aiR for Review[46].

ROI Evidence and Timeline Expectations

Documented customer outcomes provide evidence of measurable return on investment from RelativityOne/Relativity aiR implementations. JND's government agency project achieved 80% reduction in project time, completing 650,000 document review in one week versus traditional multi-week timelines[47]. The efficiency improvement enabled deployment of only three attorneys for subject matter expertise compared to larger traditional review teams[47].

Cimplifi's implementation saved 250+ hours of linear review time across 14,000 documents while achieving zero missed responsive documents in validation testing[48]. The time savings translate to significant attorney cost reductions and faster case resolution timelines[48]. Teneo's forensic investigation project completion in 18 days versus typical multi-month timelines demonstrates substantial time-to-insight improvements[53].

However, ROI realization requires substantial upfront investment in implementation and training. Organizations require investment in prompt engineering expertise, quality validation processes, and change management programs to achieve documented success outcomes[56]. Customer implementations suggest project management oversight and subject matter expert involvement throughout deployment phases[47][48][53].

Total Cost of Ownership Considerations

RelativityOne/Relativity aiR total cost of ownership extends beyond software licensing to include implementation, training, and ongoing operational expenses. Organizations require investment in prompt engineering expertise, quality validation processes, and change management programs to achieve documented success outcomes[56]. Technical infrastructure costs remain minimal for existing RelativityOne customers due to cloud-native architecture and Azure integration[40][44].

New customers face broader RelativityOne platform adoption costs including data migration, user training, and workflow integration beyond aiR-specific expenses[49]. The integrated approach provides cost advantages for existing customers but may present higher entry barriers for organizations using alternative e-discovery platforms.

Ongoing operational costs include per-document analysis fees, model updates, and continued training requirements as AI capabilities evolve[56]. Organizations must budget for iterative prompt development and quality validation processes requiring ongoing attorney involvement and statistical analysis capabilities[56]. These operational expenses may offset initial efficiency gains without proper cost management and usage optimization.

Competitive Analysis: RelativityOne/Relativity aiR vs. Alternatives

Competitive Strengths

RelativityOne/Relativity aiR demonstrates clear competitive advantages for organizations already using RelativityOne e-discovery infrastructure. The platform's deep integration with established workflows eliminates the security reviews and data migration requirements associated with standalone AI vendors[40][42]. This integration approach provides immediate deployment capability within familiar user interfaces while maintaining comprehensive compliance certifications including SOC 2 Type II, FedRAMP authorization, and HIPAA compliance[51].

Partnership with Microsoft provides access to advanced GPT models with continuous updates and improvements, offering technology development advantages over smaller AI vendors[42]. The platform's modular architecture enables incremental capability additions without disrupting existing implementations, contrasting with point solutions requiring separate vendor relationships and integration complexity[40][42].

Customer evidence demonstrates competitive performance in large-scale document review scenarios. JND's 650,000 document project completion in one week and Teneo's one million document analysis in 18 days establish scale capabilities that exceed many specialized AI vendors focused on smaller document sets[47][53].

Competitive Limitations

Independent third-party analysis reveals significant limitations compared to specialized alternatives. Unlike established AI tools that provide built-in validation capabilities, aiR for Review requires users to pull statistical samples and calculate precision and recall manually[56]. This limitation increases operational overhead and makes performance validation more complex than alternatives with integrated quality metrics.

Document size limitations present competitive disadvantages compared to other Relativity analytics tools and specialized document analysis vendors. The 150KB limit for aiR for Review analysis affects comprehensive document coverage and requires manual review for larger documents[56]. Specialized legal AI vendors like Lex Machina focus on specific use cases with higher accuracy rates in their domain expertise areas[57].

The platform's effectiveness depends heavily on detailed case knowledge and prompt engineering expertise, contrasting with marketing claims of user-friendly operation[56]. Organizations with limited case background information may experience less value compared to specialized vendors offering pre-trained models for specific legal contexts.

Selection Criteria Framework

Organizations should evaluate RelativityOne/Relativity aiR against alternatives based on specific implementation contexts and requirements. Existing RelativityOne customers benefit significantly from integration advantages and reduced implementation complexity, making aiR the logical choice for AI capability enhancement within established workflows[40][42].

Organizations using alternative e-discovery platforms should compare total implementation costs including platform migration versus standalone AI vendor deployment. Specialized vendors like Lex Machina may provide superior capabilities for specific use cases like judicial analytics, while aiR offers broader document review automation[57].

Document volume and complexity requirements influence vendor selection decisions. RelativityOne/Relativity aiR excels in high-volume scenarios with established e-discovery workflows, while specialized vendors may provide better value for focused applications or specific legal practice areas[47][53].

Implementation Guidance & Success Factors

Implementation Requirements Assessment

Successful RelativityOne/Relativity aiR implementation requires completion of specific contracting processes and application installation by Relativity to customer instances[50]. Organizations must install the aiR for Privilege application to their workspace and should install the Import/Export application to accelerate setup processes[50]. The platform operates within RelativityOne's cloud-native architecture, eliminating on-premise infrastructure requirements[40][44].

Technical requirements include establishing saved searches of documents for analysis and creating aiR projects with specific role assignments[50]. Implementation success depends on proper role configuration including Project Managers, Annotators, and Case Subject Matter Experts, requiring organizational workflow alignment[50].

Resource investment requirements extend beyond software licensing to include collaboration between legal teams, IT departments, and compliance organizations[47][48][53]. Project timelines range from one week for focused document review projects to several months for comprehensive workflow integration[47][53].

Critical Success Enablers

Customer evidence reveals consistent success patterns requiring specific organizational capabilities. Substantial case knowledge and prompt engineering expertise emerge as critical requirements, contradicting marketing claims of user-friendly operation[56]. Organizations must establish clear boundaries for AI usage and maintain human validation for critical legal decisions.

Subject matter expert involvement throughout implementation phases distinguishes successful deployments from failed experiments. Cimplifi's approach of working closely with expert attorneys to develop optimal prompt criteria and validate results through rigorous testing provides a model for effective implementation[48]. Teneo's iterative workflow development demonstrates the importance of continuous refinement based on quality review output[53].

Quality control processes become essential due to the lack of built-in validation tools. Organizations must establish statistical sampling and precision/recall calculation capabilities to monitor AI performance independently[56]. Training programs must address both technical usage and ethical considerations to ensure responsible AI deployment.

Risk Mitigation Strategies

Data security and privacy risks receive mitigation through RelativityOne's comprehensive compliance framework and Microsoft Azure integration[51]. The platform maintains extensive industry certifications and processes customer data within organizational instances without retention by vendors[50]. However, organizations must establish clear AI governance policies addressing transparency and bias concerns.

AI reliability risks require ongoing human oversight and validation processes. Third-party analysis emphasizes that aiR for Review should not be considered an out-of-the-box solution providing data insights without substantial case knowledge and prompt engineering[56]. Organizations must budget for multiple analysis runs during prompt development and ongoing per-document charges for production usage.

Cost escalation risks result from per-document analysis fees and iteration requirements for prompt optimization[56]. Organizations must establish usage monitoring and budget controls to prevent unexpected expenses from prompt refinement cycles and production analysis volumes.

Verdict: When RelativityOne/Relativity aiR Is (and Isn't) the Right Choice

Best Fit Scenarios

RelativityOne/Relativity aiR represents the optimal choice for existing RelativityOne customers requiring AI-enhanced document review capabilities within established e-discovery workflows. Government agencies and large law firms managing high-volume document review projects provide the ideal use case profile, as demonstrated by JND's 650,000 document project and Teneo's one million document analysis[47][53].

Organizations with substantial prompt engineering capabilities and dedicated subject matter expertise will achieve greatest value from aiR implementations. The platform excels in scenarios requiring rapid document analysis at scale while maintaining security compliance and integration with existing legal workflows[40][42][50].

Legal service providers and consulting organizations serving multiple clients can leverage aiR's efficiency improvements to deliver enhanced service quality while reducing operational costs. Cimplifi's 50% review time reduction and Morae's 75% cost reduction demonstrate the platform's value for service delivery enhancement[48][46].

Alternative Considerations

Organizations using alternative e-discovery platforms should carefully evaluate total implementation costs including potential platform migration versus specialized AI vendor deployment. The integration advantages that make aiR attractive for RelativityOne customers become disadvantages for organizations committed to other platforms.

Specialized use cases requiring specific AI capabilities may benefit from focused vendors rather than aiR's comprehensive approach. Lex Machina's judicial analytics or Pre/Dicta's motion outcome forecasting provide deeper expertise in specific legal domains compared to aiR's broader document review focus[57].

Small firms with limited IT resources and case volumes may find standalone AI tools more cost-effective than aiR's enterprise-focused approach. The platform's per-document pricing model and implementation complexity may not align with smaller organization requirements and resource constraints.

Decision Framework

Legal organizations should evaluate RelativityOne/Relativity aiR through a systematic assessment framework considering current infrastructure, use case requirements, and resource availability. Existing RelativityOne customers benefit from clear integration advantages and reduced implementation complexity, making aiR evaluation primarily a question of ROI validation rather than platform selection.

Organizations must assess their prompt engineering capabilities and subject matter expertise availability, as these factors critically influence implementation success[56]. The documented customer success stories provide clear evidence of transformative potential while highlighting the substantial organizational commitment required for optimal results.

Budget evaluation should include comprehensive total cost of ownership analysis incorporating per-document analysis fees, training requirements, and ongoing operational expenses beyond initial licensing costs[56]. Organizations with predictable document volumes and established AI governance frameworks will achieve greatest value from aiR deployment.

Next Steps: Organizations considering RelativityOne/Relativity aiR should request detailed pricing based on expected document volumes, evaluate pilot project opportunities to validate ROI assumptions, and assess internal prompt engineering capabilities required for successful implementation. Existing RelativityOne customers should prioritize aiR evaluation given clear integration advantages, while organizations using alternative platforms should compare total implementation costs against specialized AI vendor alternatives.