While technology has opened new possibilities for streamlining document review, the challenge lies in applying it effectively – without compromising accuracy or control.
In this article we outline three proven workflows that are helping legal teams manage document review more efficiently and defensibly.
These approaches combine technology with legal expertise to reduce review volume, improve consistency, and support better outcomes across litigation, investigations, and regulatory matters.
- Validated extraction: Accelerating early case insight
- Prioritised review: Reducing volume while maintaining defensibility
- AI-assisted review: Delivering scalable speed with expert oversight
Each workflow is designed to be practical, scalable, and adaptable, without requiring firms to invest in their own review platforms or AI infrastructure.
1. Accelerating Early Case Insight with Validated Extraction
When early clarity around facts and timelines is essential, structured extraction can significantly reduce manual effort and improve strategic decision-making.
This workflow uses Cicero AI to extract key data points, generate summaries, and build chronologies from large document sets. Law In Order’s Managed Document Review (MDR) team then manually validates the extracted data and updates as required, ensuring accuracy and defensibility.
All data processed through Cicero AI is securely hosted in a private cloud environment, compliant with ISO 42001 standards. MDR reviewers operate within Law In Order’s secure infrastructure, with strict access controls and audit trails in place.
This approach is particularly effective for investigations and commercial disputes, where factual clarity and data protection are equally essential.
2. Reducing Review Volume While Maintaining Defensibility
Prioritising the most relevant documents first allows legal teams to focus on high-value content and reduce overall review load, without compromising on quality or defensibility.
This workflow leverages Prioritised Review Queues or Relativity aiR for Review to surface key documents early.
All review activity is conducted within Law In Order’s secure hosting environment, with encryption protocols and retention policies aligned to privacy regulations and client confidentiality requirements.
3. Speeding Up Review with Expert Oversight and AI Support
Predictive coding can accelerate document review, but it requires expert validation to ensure legal defensibility. This workflow blends automation with human judgment to deliver consistent, reliable outcomes.
Using Relativity aiR for Review, validation begins with a Subject Matter Expert (SME) coding a sample set of documents to benchmark aiR’s predictions. The SME iteratively refines prompt criteria until aiR reliably ranks relevance and issues. MDR reviewers then run samples to compare aiR’s predictions (responsiveness scores, citations, and rationale) against manual coding decisions to identify false positives and negatives.
This quality control process ensures aiR’s outputs meet legal standards and supports defensible review. Reviewers also handle documents aiR cannot process, such as those with poor text or requiring family-level context. Final validation confirms aiR’s accuracy before broader deployment across the document set.
All AI and human review processes are conducted within secure, access-controlled environments, ensuring data integrity and compliance throughout the workflow.
Let’s Build the Right Workflow for Your Matter
If you’re exploring options for an upcoming matter, it’s worth considering how hybrid workflows like these could support your team. Law In Order’s expert consulting team works closely with clients to scope, calibrate, and deliver workflows that meet the specific needs of each matter, ensuring all sensitive data is handled appropriately, without adding operational complexity.
Schedule a legal data consult to discuss your current pain points and explore potential solutions or send us an enquiry for support with an upcoming project.
