Quick Definition
Technology-assisted review (TAR) is an AI-driven process that applies machine learning algorithms to classify and prioritize documents for legal and compliance review. It reduces the manual effort required in large-scale eDiscovery projects while maintaining accuracy and legal defensibility. TAR is essential for enterprises managing extensive document repositories and complex litigation workflows.
Why Technology-Assisted Review (TAR) Matters in 2026
Enterprise data volumes continue to grow at roughly 25% annually, intensifying the burden on legal discovery and compliance teams. TAR reduces costs and risk by automating document review, accelerating case readiness, and supporting adherence to retention policies. Consider the Internal Revenue Service, which manages millions of audit and tax records. Without TAR, manual review bottlenecks delay litigation and increase operational costs. Implementing TAR enables faster, more accurate review cycles, improving compliance outcomes and reducing overhead. IDC, 2025, Gartner, 2024
What Is Technology-Assisted Review (TAR)?
TAR leverages supervised machine learning models trained on a subset of documents reviewed by legal experts. These models then classify and prioritize the remaining documents for review, significantly reducing manual labor. Unlike simple keyword searches, TAR applies statistical and linguistic analysis to identify relevant documents with higher precision.
While often used interchangeably with predictive coding, TAR is a broader category encompassing multiple AI techniques. It integrates with eDiscovery workflows to maintain legal defensibility by providing transparent audit trails and validation protocols. From time at Veritas working alongside data protection and archiving teams, it is evident how technology-assisted review (TAR) significantly reduces legal discovery costs and risks by streamlining compliance and retention workflows.
TAR is critical in managing large-scale legal and compliance workflows where manual review is impractical. It supports organizations in meeting regulatory demands while controlling review costs and improving consistency across document sets.
Technology-Assisted Review (TAR) vs Related Terms
Technology-Assisted Review vs Manual Review
Manual review relies solely on human reviewers to read and classify documents, which is time-consuming, costly, and prone to inconsistency. TAR automates much of this process using machine learning, reducing human error and accelerating review speed. While manual review is established, TAR offers superior scalability and cost efficiency in large data volumes. See eDiscovery for context.
Technology-Assisted Review vs Predictive Coding
Predictive coding is a subset of TAR focused on ranking documents by relevance based on model predictions. TAR encompasses predictive coding and other AI-driven classification methods. Predictive coding prioritizes documents for review, whereas TAR may include broader classification and clustering techniques.
Technology-Assisted Review vs Continuous Active Learning (CAL)
Continuous active learning (CAL) is an advanced TAR approach that iteratively refines the machine learning model during the review process. CAL dynamically incorporates reviewer feedback to improve accuracy, outperforming static TAR models that rely on a fixed training set. CAL supports continuous validation and enhanced legal defensibility.
How Technology-Assisted Review (TAR) Works
- Data Ingestion and Sampling — Documents are collected from repositories such as Oracle databases, AWS S3 buckets, or legacy mainframe systems. A representative sample is extracted to train the model. Maintaining schema fidelity during ingestion is critical for downstream accuracy and retrieval success (Forrester, 2024).
- Model Training with Human Input — Legal experts review the sample documents, labeling them as relevant or not. This supervised training enables the machine learning model to learn patterns and predict relevance in unreviewed documents.
- Automated Document Classification and Prioritization — The trained model classifies the full document set, prioritizing those most likely relevant for expedited review. Consider the Internal Revenue Service, which initially faced failures in TAR deployment due to poor training data quality and integration challenges with legacy systems. Their manual review bottleneck caused inconsistent tagging and delayed case resolutions. By integrating TAR workflows with existing Oracle and AWS repositories and improving training protocols, the IRS accelerated review cycles and enhanced accuracy.
- Review Validation and Quality Control — Quality control processes validate model predictions through spot checks and iterative feedback. Continuous validation ensures legal defensibility and mitigates risks of model bias or error.
| Attribute | Manual Review | Technology-Assisted Review (TAR) | Predictive Coding | Continuous Active Learning (CAL) |
|---|---|---|---|---|
| Accuracy | Variable; prone to human error and inconsistency | High; improves with quality training data | Focused on relevance ranking; accuracy depends on model training | Highest; model iteratively refined during review |
| Cost | Highest; labor-intensive and time-consuming | Lower; reduces manual effort significantly | Lower; automates prioritization but requires upfront training | Lowest; continuous feedback reduces rework |
| Speed | Slow; limited by human throughput | Faster; automates bulk classification | Faster; ranks documents for prioritized review | Fastest; adapts dynamically during review |
| Legal Defensibility | Established but costly to document process | Strong; requires transparent model validation | Strong; subset of TAR with explainability focus | Strongest; continuous validation supports defensibility |
Industry Use Cases
Government / Taxation
The Internal Revenue Service manages millions of audit and tax records stored across legacy mainframe systems, Oracle databases, and AWS S3. Implementing TAR automates classification and review of these documents, reducing backlog and improving litigation readiness. Initial failures due to poor training data and system integration were resolved by adopting robust governance and integrating TAR with existing repositories, accelerating compliance workflows and reducing operational costs.
Healthcare
The Centers for Medicare & Medicaid Services (CMS) applies TAR to claims archives to ensure regulatory compliance and expedite audit processes. TAR reduces manual review time while maintaining accuracy in identifying relevant claims and documentation.
Veterans Services
The Department of Veterans Affairs leverages TAR to streamline benefits claims review. Automated prioritization improves processing speed and consistency, supporting timely benefits delivery and compliance with legal holds.
Social Security / Benefits
The Social Security Administration uses TAR to enhance review of claims histories and correspondence. This supports faster adjudication and reduces risk of errors in benefits determinations.
Public Sector
Various public sector agencies implement TAR to manage legal hold notifications and compliance reviews. TAR supports scalable review processes across diverse document types and repositories, improving defensibility and audit readiness.
Key Enterprise Benefits
- Significant cost reduction by minimizing manual review labor.
- Improved review speed through automated document classification.
- Enhanced accuracy and consistency compared to manual review.
- Strong legal defensibility with transparent validation processes.
- Scalability to handle growing enterprise data volumes.
- Alignment with data retention and compliance policies.
Common Challenges and Mitigations
| Challenge | Mitigation |
|---|---|
| Poor training data quality leading to inaccurate models | Implement rigorous sample selection and expert review protocols |
| Model bias impacting relevance predictions | Use diverse training sets and continuous validation with human feedback |
| Integration difficulties with legacy document repositories | Develop connectors and APIs to bridge TAR tools with existing systems |
| Resistance from reviewers unfamiliar with AI workflows | Provide training and transparent model explanations to build trust |
| Regulatory acceptance and defensibility concerns | Maintain audit trails and adhere to established legal standards and guidelines |
How Solix Helps Enterprises Operationalize Technology-Assisted Review (TAR)
Solix ECS leverages integrated retention, legal hold, eDiscovery, and compliance workflows to optimize TAR implementations and reduce review costs. Its platform supports seamless integration with legacy repositories and cloud storage, enabling enterprises to operationalize TAR with governance and auditability. Learn more about Solix ECS.
Frequently Asked Questions
What is Technology-Assisted Review (TAR) used for?
TAR is used to automate the classification and prioritization of documents in legal discovery and compliance reviews. It helps reduce manual review effort, improve accuracy, and accelerate case preparation.
How does Technology-Assisted Review (TAR) work?
TAR works by training machine learning models on a sample of documents labeled by experts. The model then classifies the remaining documents, prioritizing those most likely relevant. Continuous validation ensures accuracy and defensibility.
What are the benefits of Technology-Assisted Review (TAR)?
TAR reduces costs, speeds up document review, improves accuracy, supports legal defensibility, and scales to large data volumes while aligning with compliance policies.
Technology-Assisted Review vs Predictive Coding?
Predictive coding is a subset of TAR focused on ranking documents by relevance. TAR includes predictive coding and other AI techniques for broader classification and prioritization tasks.
Related Glossary Terms
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About the author
Barry Kunst
Vice President Marketing, Solix Technologies Inc.
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