AI for Document Review in Litigation & Investigations: Find the Needle Without Reading Every Piece of Hay
Legal cases mean documents. Thousands of them. Sometimes millions.
Emails. Attachments. PDFs. Chat logs. Text messages. Presentations. Spreadsheets. Photos with text. Contracts. Meeting notes.
All of it needs review. All of it needs classification. Relevant or not. Privileged or not. Responsive or not. Hot document or noise.
This is eDiscovery. It’s essential. It’s mind-numbing. And it’s expensive.
AI changes the math. It reads everything. It sorts the pile. It finds patterns. It flags what matters. It summarizes threads.
Your reviewers focus on the documents that actually need human judgment. The case moves faster. The costs stay manageable.
The Document Review Problem
Modern litigation produces massive document volumes. A mid-size case: 50,000 documents. A large case: millions.
Every document needs eyes on it. Is it relevant to the case? Does it contain privileged information? Is it responsive to discovery requests? Does it contain smoking gun evidence?
The traditional approach:
- Hire a team of contract attorneys
- Create review protocols
- Train reviewers on what to look for
- Review documents one by one
- Code each document (relevant/not relevant, privileged/not privileged, etc.)
- QC a sample to ensure quality
- Repeat for thousands of hours
Cost: $50-150 per hour per reviewer. Time: Weeks or months. Quality: Depends on reviewer fatigue and attention.
Miss something important? Case could be lost. Include something privileged? You just waived privilege.
The stakes are high. The volume is crushing. The deadlines are tight.
What AI Does for Document Review
AI doesn’t get tired. It doesn’t get bored. It reads every word of every document and applies consistent logic.
Here’s what actually happens:
1. Automatic Relevance Classification
The AI reads the case summary and review protocols. Then it reads every document.
For each document, it predicts:
- Relevant to the case or not
- Confidence level (high, medium, low)
- Key topics covered
- Why it might be relevant
Not perfect. But 80-90% accurate on straightforward relevance calls. That means 80% of documents can be confidently sorted before a human reviewer even looks.
The reviewers focus on the 20% where judgment is actually needed. Or QC the AI’s work on high-confidence documents.
2. Thematic Clustering
Documents don’t exist in isolation. They’re part of conversations. Part of projects. Part of decisions.
The AI groups related documents together:
- All emails in a thread
- All documents about the same topic
- All communications between the same people
- All documents from the same time period about the same event
Reviewers see documents in context. They understand the full picture. They catch connections that would be missed reviewing documents one by one in random order.
3. Sensitive Content Detection
Some documents need special attention:
- Potentially privileged communications (to/from counsel, legal advice)
- Personal data (names, SSNs, health info, financial data)
- Confidential business information
- Hot documents (evidence of wrongdoing or key facts)
- Compliance red flags
The AI flags these automatically. Privileged docs go to a separate review track. Personal data gets noted for redaction. Hot docs get escalated to senior reviewers.
Nothing slips through because a junior reviewer didn’t recognize its significance.
4. Intelligent Prioritization
Not all documents are equally important. The AI helps you review in the right order:
- High relevance confidence first
- Documents from key custodians first
- Documents from critical time periods first
- Documents flagged as potentially hot first
- Documents with sensitive content first
You find the important stuff early. You understand the case better, faster. You make better strategic decisions.
5. Email Thread Summarization
Email threads get long. 20, 50, 100 messages. Lots of “thanks” and “see below” and forwarding.
The AI reads the entire thread and creates a summary:
- Who’s involved
- What’s being discussed
- Key decisions or commitments made
- Open questions or action items
- Timeline of events
Reviewers understand context in 30 seconds instead of 10 minutes. They make better relevance calls.
6. Concept Search & Technology-Assisted Review (TAR)
Traditional search: Find documents containing “breach of contract.”
Problem: Relevant documents might say “violated the agreement” or “failed to perform obligations.”
AI concept search: Find documents about contract violations, regardless of exact phrasing.
It understands meaning, not just keywords. It finds documents you’d miss with traditional search.
TAR goes further: You review a seed set of documents. The AI learns what you consider relevant. Then it predicts relevance for the remaining documents. Continuous feedback makes it more accurate.
Wat dit voor jou betekent
For Decision Makers
Discovery costs drop 40-70%.
Fewer hours billed by contract reviewers. Faster review means less project management overhead. Better targeting means less over-production.
Cases resolve faster.
Understand the case better, earlier. Find key evidence sooner. Make informed settlement decisions faster. Respond to discovery faster.
Budget predictability.
AI costs are fixed. You know what the first-pass review will cost. No surprises from ballooning reviewer hours.
Better strategic decisions.
When you find hot documents early and understand the case better, you make smarter decisions about settlement, motion practice, and trial strategy.
For Lawyers
Focus on high-value review.
Let AI handle the obviously irrelevant documents. You focus on the documents that need legal judgment.
Find the needle faster.
Prioritization and clustering help you identify key documents early. You build your case faster.
Better privilege protection.
Automated flagging catches potentially privileged documents. Fewer privilege waiver risks.
Defensible process.
Every decision is logged. Every prediction is documented. You can defend your review process if challenged.
For Compliance & Investigations
Faster internal investigations.
When an issue arises, AI helps you review relevant communications quickly. You understand what happened faster.
Better documentation.
Clear record of what was reviewed, what was found, what decisions were made. Audit-ready from day one.
Early issue spotting.
AI flags potential compliance issues across the document set. You see patterns of concerning behavior.
Real-World Example: Employment Dispute
An employee sues for wrongful termination and harassment. Discovery includes 3 years of emails, chat logs, and HR documents. 80,000 documents total.
Without AI:
- Review team: 5 contract attorneys
- Review time: 6 weeks
- First hot document found: Week 4
- Total review cost: $120,000
- Documents reviewed unnecessarily: Roughly 50,000 clearly irrelevant docs
With AI:
- AI pre-classifies documents (2 hours processing time)
- 40,000 documents classified as clearly not relevant (high confidence)
- 25,000 documents flagged as likely relevant or unclear
- 15,000 documents flagged as potentially sensitive (privilege, hot docs)
- Hot documents prioritized to top of review queue
- Review team: 2 attorneys + 2 contract reviewers
- Review time: 2.5 weeks
- First hot document found: Day 3
- Total cost: $45,000
- Better case understanding, earlier settlement discussion
Same quality. 60% cost savings. Faster resolution.
What AI Doesn’t Do
Let’s be honest about limitations.
AI doesn’t understand complex legal privilege calls.
It flags documents that might be privileged. A lawyer still makes the privilege determination.
AI doesn’t replace human reviewers.
It reduces the volume they need to review. It helps them make better decisions. They still review and make final calls.
AI doesn’t handle novel cases perfectly.
Standard disputes (employment, contracts, IP)? Excellent. Unprecedented legal theories with no training data? Less helpful.
AI needs feedback to improve.
The more you review and correct its predictions, the more accurate it becomes. It’s not set-and-forget.
AI doesn’t make strategic decisions.
It tells you what’s in the documents. You decide what that means for the case.
AI is a powerful tool. It’s not a replacement for experienced litigators.
How to Get Started
1. Identify a pilot case.
Choose a case with meaningful document volume (10,000+ documents) but not your most complex litigation. Standard dispute types work best.
2. Define review criteria clearly.
What makes a document relevant? What are you looking for? The clearer your criteria, the better the AI performs.
3. Start with a seed set.
Review 500-1,000 documents manually. Code them. The AI learns from your decisions.
4. Let AI classify the rest.
The AI predicts relevance for remaining documents based on what it learned.
5. Review with AI assistance.
Focus on high-priority documents. QC a sample of AI classifications. Provide feedback.
6. Refine and expand.
The AI improves with feedback. Apply lessons learned to the next case.
Common Questions
Is AI-assisted review accepted by courts?
Yes. Courts have repeatedly approved TAR and AI-assisted review. It’s now standard practice in complex litigation.
What about privilege review?
AI can flag potentially privileged documents, but a lawyer must make the final privilege call. Most teams use AI for first-pass flagging.
Can it handle different file types?
Yes. Emails, PDFs, Word docs, Excel, images with text (OCR), chat logs, and more. If text can be extracted, AI can analyze it.
What about foreign languages?
AI can handle multiple languages. Accuracy varies by language. English, Spanish, French, German work best.
How do you ensure quality?
QC sampling. Continuous feedback. Validation against a control set. Same quality assurance practices used in traditional review.
What about data security?
We work with your existing eDiscovery platform or deploy in your secure environment. Your documents stay under your control.
De kern van de zaak
Document review doesn’t have to be a cost and time sink.
AI handles the first pass. It sorts the pile. It finds patterns. It flags what matters. Your reviewers focus on documents that need human judgment.
The result is faster case resolution, lower discovery costs, better strategic decisions, and reviewers doing work that actually requires expertise.
No magic. Just practical automation that makes litigation more efficient.
Ready to Reduce Discovery Costs?
Every case is different. Different document types. Different issues. Different review protocols.
We don’t sell one-size-fits-all solutions. We look at your specific cases and discovery challenges. We identify where AI delivers the most value. We build a workflow that fits your process.
Let’s talk about your document review challenges and where AI can make a real impact.
