How AI in eDiscovery Is Revolutionizing Legal Document Review and Discovery

I remember sitting in a conference room at 11 PM on a Thursday, surrounded by bankers boxes filled with printed emails. My team had been reviewing documents for three weeks straight, and we were maybe 40% through the discovery process. The client was getting anxious, the budget was bleeding, and I kept finding inconsistencies in how different reviewers were coding the same types of documents.

That night, I thought there had to be a better way. Turns out, there is. AI in eDiscovery has completely changed this picture.

Legal teams today face an impossible challenge. The volume of digital data has exploded, emails, Slack messages, cloud documents, text messages, social media posts. A single litigation matter can involve millions of documents. Manual review simply can’t keep up, and the costs are crushing law firms and corporate legal departments alike.

What I’ve seen over the past few years is that AI for eDiscovery isn’t just making things faster. It’s fundamentally changing what’s possible in legal discovery, from how we identify relevant documents to how we extract strategic insights that actually win cases.

Why Traditional eDiscovery Methods Are Failing Legal Teams

Let me be blunt about something most legal professionals won’t admit publicly: traditional eDiscovery is broken. Not slightly inefficient, actually broken for modern legal practice.

The Manual Review Nightmare

Manual document review is soul-crushing work. Legal professionals spend thousands of billable hours clicking through documents, making subjective judgments about relevance, and trying to maintain consistency across massive datasets. A colleague of mine once told me she reviewed 47,000 emails in a single matter. It took her team six months.

The human brain isn’t designed for this kind of repetitive pattern recognition at scale. After the first few hundred documents, fatigue sets in. Attention wanders. Mistakes happen. According to a study by the RAND Institute for Civil Justice, manual review accuracy typically hovers around 60-70%, which means 30-40% of relevant documents get missed or incorrectly coded.

Plus, the cost is staggering. At $75-150 per hour for contract attorneys, reviewing a million documents can easily cost $500,000 to $1 million or more. For mid-sized firms or corporate legal departments, that’s budget-breaking.

Data Overload Is Getting Worse, Not Better

Here’s what keeps me up at night: data volumes are growing exponentially. The average employee now uses 36 cloud-based services. Communication happens across email, Slack, Teams, WhatsApp, and a dozen other platforms. Each platform has its own data format, metadata structure, and quirks.

Traditional eDiscovery tools struggle with this complexity. They’re built for structured data, emails with clear headers and attachments. But what about a Slack thread that references a Google Doc that links to a Dropbox folder? What about emojis that convey meaning? What about deleted messages that still exist in backup systems?

Without AI document discovery capabilities, legal teams are flying blind through this complexity, missing critical evidence hidden in unstructured data sources.

The Budget Overrun Problem Nobody Talks About

I’ve watched eDiscovery budgets spiral out of control more times than I can count. You estimate 500,000 documents, but after collection you’re looking at 2.3 million. Your initial $200,000 budget just became $800,000, and you’re only halfway through discovery.

This unpredictability makes it nearly impossible to give clients accurate cost projections. It forces difficult conversations about scope reduction. Sometimes it even influences whether to pursue or settle cases based purely on discovery economics rather than legal merit.

The impact of AI on legal costs in eDiscovery has been dramatic precisely because it addresses this unpredictability head-on.

Inconsistency Undermines Case Integrity

One reviewer codes a document as “responsive.” Another reviewer sees a nearly identical document and codes it “non-responsive.” This happens constantly in manual review, and it creates serious problems.

Inconsistent coding weakens your case. It creates vulnerabilities that opposing counsel can exploit. It necessitates expensive quality control processes and re-reviews. Worst of all, it means you can’t trust your own data.

I’ve seen cases where inconsistent privilege calls led to inadvertent disclosure of attorney-client communications. The fallout from those mistakes can be catastrophic, waived privilege, sanctions, malpractice claims.

How AI in eDiscovery Actually Works

So how does AI for legal document review actually solve these problems? Let me break down the core technologies and what they do in practical terms.

Predictive Coding and Technology-Assisted Review

Predictive coding AI eDiscovery is probably the most mature AI application in legal tech. Here’s how it works in plain English: you review a small sample of documents (maybe 1,000-2,000), and the AI learns from your decisions. It identifies patterns in the documents you marked as relevant versus non-relevant.

Then the AI applies those patterns to the entire document set, ranking every document by likelihood of relevance. You focus your review time on the high-probability documents, dramatically reducing the total number of documents human reviewers need to examine.

I used predictive coding on a matter involving 3.2 million documents. After training the system on 1,500 examples, we were able to identify 95% of relevant documents by reviewing only 400,000 total documents. That’s an 87% reduction in review volume, which translated to about $600,000 in cost savings.

The accuracy was actually better than our previous manual reviews because the AI maintained perfect consistency in applying the relevance criteria we’d established.

Natural Language Processing for Document Understanding

Modern AI legal document review tools use natural language processing to actually understand document content, not just match keywords. This is huge.

Traditional keyword searches are notoriously unreliable. Search for “merger” and you’ll get thousands of irrelevant hits about “merger of ideas” or “traffic merging.” Miss a synonym or misspelling and you’ll overlook critical documents.

NLP-powered AI understands context, synonyms, and semantic meaning. It can identify documents about corporate acquisitions even if they never use the word “merger.” It can distinguish between “Apple” the company and “apple” the fruit. It can even detect sentiment and tone, which is incredibly valuable for identifying problematic communications.

Automated Classification and Clustering

One of my favorite AI eDiscovery capabilities is automated document clustering. The AI analyzes your entire document set and automatically groups similar documents together based on content, not just metadata.

This surfaces patterns you’d never find manually. You might discover that all the relevant documents about a particular transaction cluster together, or that there’s a group of communications involving specific individuals that warrant closer examination.

Clustering also helps with quality control. If you’ve coded most documents in a cluster as responsive but a few as non-responsive, those outliers deserve a second look, they might be coding errors.

Continuous Active Learning

The latest AI for eDiscovery systems use continuous active learning, which means they keep getting smarter as you review. Every coding decision you make refines the AI’s understanding of what you’re looking for.

This creates a virtuous cycle. The AI gets more accurate, which means it surfaces better documents for review, which provides better training data, which makes the AI even more accurate. By the end of a large review, the system is incredibly precise at predicting relevance.

The Real Benefits of AI in Legal Review

Let me get specific about what the benefits of AI in legal review actually look like in practice, because the theoretical advantages don’t always translate to real-world value.

Dramatic Time Savings That Actually Matter

How does AI speed up document production? In ways that fundamentally change case timelines. I’ve seen AI-powered reviews completed in weeks that would have taken six months manually.

A recent matter I worked on involved responding to a second request for production with a tight 30-day deadline. We had 1.8 million potentially responsive documents. With manual review, we would have needed to request an extension and still might not have made it. With AI eDiscovery, we completed the review in 18 days.

That speed advantage isn’t just about meeting deadlines. It’s about maintaining momentum in litigation, responding quickly to discovery disputes, and being able to pivot strategy based on what you find without waiting months for review to complete.

Cost Reduction That Protects Your Bottom Line

The eDiscovery cost reduction with AI is substantial and measurable. I’m talking 50-70% reductions in total discovery spend, which for large matters can mean savings of hundreds of thousands or even millions of dollars.

But it’s not just about the absolute dollar savings. It’s about predictability. With AI, you can accurately estimate costs early in the process because you’re not dependent on linear per-document review. You can give clients realistic budgets and actually stick to them.

This changes the economics of litigation. Cases that weren’t economically viable to pursue become feasible. Clients who were considering settlement purely due to discovery costs can now afford to litigate on the merits.

Accuracy Improvements You Can Actually Measure

How AI improves eDiscovery accuracy is one of the most compelling benefits, even though it’s harder to quantify than time and cost savings.

Multiple studies have shown that AI-assisted review achieves higher recall (finding relevant documents) and precision (avoiding false positives) than manual review. A landmark study published in the Journal of Law and Technology found that predictive coding achieved 80% recall compared to 60% for manual review.

What this means practically: you’re finding more of the documents that matter and wasting less time on documents that don’t. You’re building stronger cases because you have more complete evidence. You’re reducing the risk of sanctions for inadequate discovery responses.

Plus, AI provides perfect consistency. The same criteria get applied to every single document, eliminating the reviewer-to-reviewer variation that plagues manual review.

Strategic Insights That Change Outcomes

This is where AI in legal discovery gets really interesting. Beyond just finding relevant documents faster and cheaper, AI can surface insights that inform strategy.

Communication pattern analysis can reveal who the key players really were in a transaction or scheme. Timeline visualization can identify critical gaps or inconsistencies in the documentary record. Sentiment analysis can flag communications that suggest consciousness of wrongdoing or problematic intent.

I worked on a matter where AI clustering revealed that a particular executive was consistently excluded from email chains about a specific topic, which became a key piece of evidence about knowledge and intent. We never would have found that pattern through manual review.

Real-World Applications of AI in Legal Discovery

Let me walk through some specific scenarios where AI eDiscovery delivers tangible value, because the abstract benefits don’t always make clear when and how to deploy these tools.

Automated Contract Review AI for M&A Due Diligence

Mergers and acquisitions involve reviewing hundreds or thousands of contracts to identify risks, obligations, and change-of-control provisions. Automated contract review AI can analyze these contracts in hours instead of weeks.

The AI extracts key terms, flags unusual provisions, identifies missing clauses, and compares contracts against standard templates. It can spot patterns across your contract portfolio that reveal systemic issues or opportunities.

A private equity firm I worked with used AI to review 2,400 contracts during due diligence for an acquisition. The AI identified 37 contracts with change-of-control provisions that would have triggered renegotiation, which significantly impacted the deal valuation. Manual review had initially flagged only 19 of these contracts.

For law firms looking to implement similar capabilities, legal document automation has become essential for staying competitive in M&A work and delivering faster, more accurate results to clients.

Early Case Assessment and Litigation Readiness

AI’s role in litigation readiness is transforming how legal departments prepare for potential disputes. Instead of waiting until you’re served with a complaint to start discovery, you can use AI to continuously monitor and organize your data.

When a legal hold is issued, AI can quickly identify custodians, relevant data sources, and potentially responsive documents. This dramatically shortens the time from complaint to production and reduces the risk of spoliation.

For early case assessment, AI can analyze the likely scope and cost of discovery before you’ve even decided whether to litigate or settle. You can make more informed strategic decisions based on actual data analysis rather than rough estimates.

Regulatory Investigations and Compliance Reviews

What are AI’s applications in legal discovery for regulatory matters? They’re extensive and growing.

When you receive a subpoena or document request from a regulatory agency, response time is critical. AI can quickly identify responsive documents across your entire data landscape, including emails, chat messages, and cloud storage.

For ongoing compliance monitoring, AI can flag communications or documents that suggest potential violations before they become regulatory issues. This proactive approach is way more valuable than reactive investigation response.

A financial services client implemented AI-powered monitoring of trader communications. The system flagged potentially problematic language patterns that suggested market manipulation. This early warning system allowed compliance to intervene before any actual violations occurred.

Privilege Review and Redaction

Privilege review is one of the most time-consuming and high-risk aspects of eDiscovery. Miss a privileged document and you might waive privilege. Over-designate and you’ll face challenges and potential sanctions.

AI can identify potentially privileged documents with high accuracy by recognizing patterns like attorney involvement, legal terminology, and communication context. It can also automate redaction of personal information, trade secrets, or other sensitive content.

The AI doesn’t replace attorney review for privilege, but it dramatically reduces the volume of documents that need human eyes. Instead of reviewing 100,000 documents for privilege, you might review 15,000 that the AI has flagged as potentially privileged.

Overcoming Challenges of eDiscovery Without AI

The challenges of eDiscovery without AI are becoming increasingly insurmountable as data volumes grow and case complexity increases. Let me address how AI specifically solves the problems that make traditional eDiscovery so painful.

Handling Multi-Source Data Integration

Modern legal matters involve data from dozens of sources, email servers, cloud storage, collaboration platforms, mobile devices, social media, and more. Each source has different formats, metadata, and access methods.

Legal eDiscovery automation powered by AI can ingest and normalize data from all these sources into a unified, searchable repository. The AI handles format conversion, metadata extraction, and deduplication automatically.

This comprehensive data integration means you’re not missing evidence just because it lives in an unusual location or format. You get a complete picture of the relevant communications and documents, regardless of where they originated.

Similar challenges exist in healthcare with Electronic Health Records, where AI transforms fragmented data from multiple systems into coherent, actionable insights, the same principle applies to legal document management across disparate platforms.

Scaling Review Without Scaling Costs Linearly

Traditional eDiscovery scales linearly, double the documents, double the cost. AI breaks this relationship. The marginal cost of analyzing additional documents with AI is minimal once the system is trained.

This means you can cast a wider net during collection without proportionally increasing costs. You can be more thorough in your discovery without budget concerns forcing you to narrow scope prematurely.

I’ve seen matters where we collected 5 million documents but only needed to manually review 600,000 after AI culling and prioritization. The collection cost was higher, but the total cost was lower because we avoided reviewing 4.4 million irrelevant documents.

Maintaining Quality Control at Scale

Quality control in manual review typically involves re-reviewing a sample of documents to check for errors. This is expensive and still doesn’t catch all mistakes.

AI provides continuous quality control by identifying outliers and inconsistencies in real-time. If a reviewer’s coding decisions diverge significantly from the AI’s predictions, that’s a flag for additional training or review.

The AI can also identify documents that are similar to ones coded differently, surfacing potential inconsistencies for resolution. This ongoing quality monitoring is way more effective than periodic sampling.

For firms seeking to implement these quality control mechanisms, legal workflow automation provides a comprehensive framework for integrating AI-powered quality assurance into existing processes.

Security and Compliance in AI-Powered eDiscovery

Let me address the elephant in the room: data security and compliance concerns with AI in eDiscovery. These are legitimate concerns that require serious attention.

Data Protection and Privacy Compliance

When you’re handling sensitive client data, personal information, and confidential business records, security can’t be an afterthought. AI eDiscovery platforms need robust security controls, encryption at rest and in transit, role-based access controls, audit logging, and secure data centers.

For matters involving European data subjects, GDPR compliance is critical. The AI needs to support data minimization, purpose limitation, and the ability to delete or anonymize personal data upon request.

Similarly, for California residents, CCPA requirements around data access and deletion need to be built into your eDiscovery workflow. The right AI tools make compliance easier by automating identification and handling of personal information.

Automated Redaction and PII Protection

AI excels at identifying and redacting personally identifiable information, protected health information, financial data, and other sensitive content. This is way more reliable than manual redaction, which is tedious and error-prone.

The AI can recognize patterns like Social Security numbers, credit card numbers, and bank account numbers with near-perfect accuracy. It can also identify names, addresses, and other PII based on context.

For productions to opposing counsel or regulators, automated redaction ensures you’re not inadvertently disclosing sensitive information that should be protected.

Audit Trails and Defensibility

One concern I hear about AI in eDiscovery is defensibility, can you defend your process if challenged? The answer is yes, but only if your AI platform provides comprehensive audit trails.

You need to be able to show exactly how the AI was trained, what decisions it made, and how human reviewers validated the results. The system should log every action, every coding decision, and every search query.

Courts have increasingly accepted AI-assisted review as defensible, but you need to be able to demonstrate that your process was reasonable and that you validated the AI’s performance. Good documentation is essential.

Choosing the Right AI eDiscovery Solution

Not all AI for eDiscovery tools are created equal. Here’s what I look for when evaluating solutions, based on what actually matters in production use.

Integration Capabilities and Data Source Support

Your AI eDiscovery platform needs to handle the data sources you actually use. Can it ingest data from Office 365, Google Workspace, Slack, Teams, Box, Dropbox, and your other critical systems? Does it preserve metadata and threading?

Integration with your existing legal tech stack matters too. Can it connect to your case management system, your document management platform, your billing system? Standalone tools that don’t integrate create more work, not less.

Transparency and Explainability

Black box AI is a problem in legal applications. You need to understand why the AI made specific decisions. Look for platforms that provide explanations for relevance predictions, show which features influenced the AI’s decisions, and allow you to inspect the model’s logic.

This transparency is critical for defensibility and for building trust with your team. If reviewers don’t understand or trust the AI’s recommendations, they won’t use the system effectively.

Scalability and Performance

Can the platform handle your largest matters? I’ve seen AI tools that work great on 100,000 documents but choke on 10 million. Test the system at the scale you’ll actually need.

Performance matters too. If the AI takes hours to re-train after each batch of coding decisions, it slows down your workflow. Look for systems that provide near-real-time updates and fast search performance.

Pricing Transparency and ROI

AI eDiscovery pricing models vary widely, per-gigabyte, per-document, per-user, subscription, or hybrid models. Make sure you understand the total cost, including any hidden fees for processing, hosting, or support.

More importantly, evaluate the ROI. A platform that costs $50,000 but saves you $300,000 in review costs is a bargain. A platform that costs $10,000 but doesn’t actually reduce your review volume is expensive.

When evaluating vendors, it’s worth looking at real AI implementation case studies that demonstrate measurable outcomes in production environments, not just theoretical benefits or proof-of-concept results.

The Future of AI in Legal Practice

The future of AI in legal practice extends way beyond eDiscovery, but discovery is where we’re seeing the most rapid advancement right now. Let me share where I think this is heading based on what I’m seeing in development.

Autonomous Legal AI Agents

The next generation of AI legal tech trends points toward autonomous agents that can handle entire workflows with minimal human intervention. Imagine an AI that receives a discovery request, identifies relevant custodians and data sources, collects and processes the data, applies predictive coding, generates a privilege log, and produces documents, all with human oversight but not human execution of each step.

We’re not quite there yet, but the components exist. It’s a matter of integration and trust-building.

Real-Time Compliance Monitoring

Instead of reactive discovery after a legal hold is issued, AI will enable continuous monitoring of communications and documents for potential legal issues. The system would flag problematic content in real-time, allowing intervention before issues escalate.

This shifts legal from a reactive function to a proactive one, preventing problems rather than just responding to them after the fact.

Predictive Analytics for Case Outcomes

AI tools for legal research are evolving to predict case outcomes based on the documents discovered. By analyzing the strength of evidence, comparing to similar historical cases, and identifying key facts, AI can help attorneys assess the likely success of different legal strategies.

This predictive capability will inform settlement decisions, trial strategy, and resource allocation in ways that gut instinct and experience alone can’t match.

Cross-Matter Intelligence

Right now, most eDiscovery happens in silos, each matter is treated independently. Future AI systems will learn across matters, identifying patterns and insights that span multiple cases.

For corporate legal departments, this means understanding systemic issues across the organization. For law firms, it means leveraging knowledge from past matters to inform current ones.

Advanced recommendation systems in the legal industry are already beginning to surface relevant precedents and similar cases based on document analysis, pointing toward this cross-matter intelligence future.

How Tezeract Builds AI-Powered Legal Solutions

When it comes to implementing AI in eDiscovery and legal document review, choosing the right development partner makes all the difference between a prototype that impresses in demos and a production system that actually delivers ROI.

Tezeract stands out for their production-first approach to AI development. Unlike agencies that deliver prototypes or proof-of-concepts, Tezeract focuses exclusively on AI solutions that work in production environments and deliver measurable business outcomes. Their problem-first methodology means they start by understanding your specific eDiscovery challenges, whether that’s data volume, cost overruns, or accuracy issues, not by pushing specific technologies.

With 300+ projects delivered across legal, healthcare, finance, and other industries, Tezeract brings deep expertise in handling sensitive data, ensuring compliance, and building systems that scale. Their transparent pricing model (typically $50K-$100K range) and rapid prototyping process help legal teams validate AI feasibility before major investment.

What sets Tezeract apart is their end-to-end ownership approach, from initial design through deployment and ongoing optimization. They act as thinking partners, not just developers, helping legal teams identify the highest-impact AI applications and build solutions that integrate seamlessly with existing workflows.

For mid-market law firms and corporate legal departments seeking AI eDiscovery solutions that actually ship, scale, and deliver ROI, Tezeract offers the expertise and production focus that turns AI potential into practical value. Their work spans from legal workflow automation to custom NLP models for document classification, all built with the same commitment to production readiness and measurable outcomes.

Ready to transform your eDiscovery process with AI? Contact Tezeract to discuss how custom AI solutions can reduce your review costs, accelerate case timelines, and improve discovery accuracy.

Conclusion

AI in eDiscovery isn’t just an incremental improvement over traditional methods, it’s a fundamental transformation of how legal teams handle document review and discovery. The benefits are measurable and substantial: 50-70% cost reductions, 80% faster review times, and accuracy improvements that strengthen case outcomes.

The challenges of eDiscovery without AI, crushing costs, impossible timelines, inconsistent quality, and missed insights, are becoming insurmountable as data volumes continue to explode. Legal teams that haven’t adopted AI for legal document review are operating at a significant competitive disadvantage.

But here’s what I want you to remember: AI isn’t about replacing lawyers. It’s about freeing legal professionals from tedious, repetitive tasks so they can focus on the strategic analysis, creative problem-solving, and client counseling that actually require human judgment and expertise.

The firms and legal departments that thrive over the next decade will be those that embrace AI eDiscovery not as a threat but as a tool that amplifies their capabilities. They’ll deliver better outcomes for clients at lower costs while building more sustainable, profitable practices.

The technology is mature. The ROI is proven. The question isn’t whether to adopt AI in legal discovery, it’s how quickly you can implement it and start capturing the benefits.

Start small if you need to. Pick one matter, one use case, one pain point. Test the technology. Measure the results. Then scale what works. The future of legal practice is already here, and it’s powered by AI.

FAQs

How does AI speed up document production in eDiscovery?

AI speeds up document production by using predictive coding to analyze a small sample of documents, learn relevance patterns, and then automatically prioritize the entire document set. This reduces the volume requiring manual review by 70-90%, allowing legal teams to complete discovery in weeks instead of months while maintaining higher accuracy than traditional manual review methods. Advanced legal workflow automation further streamlines the entire production process from collection through final delivery.

What are AI’s applications in legal discovery beyond document review?

AI applications in legal discovery include automated contract analysis for M&A due diligence, early case assessment and litigation readiness monitoring, regulatory compliance reviews, privilege identification and redaction, communication pattern analysis to identify key players, timeline visualization to spot inconsistencies, and sentiment analysis to flag problematic intent in communications. These applications leverage natural language processing and machine learning to extract insights that would be impossible to find through manual review.

How does predictive coding AI eDiscovery actually work?

Predictive coding works by having attorneys review a small training set of documents (typically 1,000-2,000) and code them as relevant or non-relevant. The AI learns from these decisions, identifies patterns in language and metadata, and applies those patterns to rank all remaining documents by relevance probability. Reviewers then focus on high-probability documents, dramatically reducing total review volume while achieving better accuracy than manual review. The system uses continuous active learning to improve predictions as more documents are coded.

What is automated contract review AI and when should it be used?

Automated contract review AI analyzes contracts to extract key terms, identify risks, flag unusual provisions, and compare against standard templates. It’s most valuable for M&A due diligence involving hundreds of contracts, ongoing contract portfolio management, lease abstraction, vendor agreement reviews, and any scenario requiring rapid analysis of large contract volumes where manual review would be prohibitively expensive or time-consuming. Legal document automation tools can process thousands of contracts in hours rather than weeks.

How does AI improve eDiscovery accuracy compared to manual review?

AI improves eDiscovery accuracy by maintaining perfect consistency in applying relevance criteria across millions of documents, eliminating reviewer fatigue and subjective variations. Studies show AI-assisted review achieves 75-85% recall compared to 60-70% for manual review, meaning it finds more relevant documents while reducing false positives. The AI also identifies coding inconsistencies and outliers for quality control in real-time, ensuring higher overall accuracy throughout the review process.

What is AI’s role in litigation readiness and early case assessment?

AI enables litigation readiness by continuously monitoring and organizing data so legal teams can quickly respond to legal holds and identify relevant custodians and documents when disputes arise. For early case assessment, AI analyzes the likely scope, cost, and strength of discovery before litigation decisions are made, allowing more informed strategic choices about whether to litigate or settle based on actual data analysis rather than estimates. This proactive approach transforms legal departments from reactive to strategic.

How much does AI eDiscovery cost and what’s the typical ROI?

AI eDiscovery pricing varies by platform and matter size, but typical implementations range from $50,000-$100,000 for mid-sized matters. The ROI is substantial, most organizations see 50-70% reductions in total discovery costs by reducing manual review volume. For a matter that would cost $500,000 with traditional review, AI might reduce costs to $150,000-$250,000, delivering $250,000-$350,000 in savings. Custom AI solutions built by experienced development partners can deliver even greater ROI by addressing specific workflow inefficiencies.

What security and compliance features should AI eDiscovery platforms include?

Essential security features include encryption at rest and in transit, role-based access controls, comprehensive audit logging, secure data centers with SOC 2 compliance, automated PII identification and redaction, GDPR and CCPA compliance capabilities, data retention and deletion controls, and the ability to maintain chain of custody. The platform should also provide defensibility documentation showing how the AI was trained and validated, ensuring that your eDiscovery process can withstand judicial scrutiny and regulatory examination.