Case Study: Building a Legal AI Assistant with NeuroGen

By The NeuroGen Team | November 25, 2024 | 11 min read

How a mid-size law firm transformed document review with AG2 multi-agent AI, reducing discovery costs by 87% and eliminating privilege review errors.

The Challenge: Discovery Drowning a Growing Firm

Morrison & Ellis LLP, a 50-attorney firm specializing in commercial litigation, faced a critical bottleneck that threatened their growth trajectory.

The Pain Points

• Massive Document Volumes: Average case = 75,000+ documents
• Crushing Costs: $450K annual spend on external eDiscovery vendors
• Glacial Timelines: 6-8 weeks per document review cycle
• Privilege Review Terror: Manual attorney-client privilege screening with 3-5% error rate
• Associate Burnout: Junior attorneys spending 60% of time on document review
• Client Dissatisfaction: Slow turnarounds impacting case strategies

"We were at an inflection point. Either we found a way to handle discovery more efficiently, or we'd have to turn away high-value cases. The economics just didn't work."

Previous Solutions Failed

The firm had tried multiple approaches:

• ❌ Traditional eDiscovery Vendors: Expensive, slow, opaque pricing
• ❌ In-House Keyword Search: 40% recall rate, too many false positives
• ❌ First-Gen AI Tools: Single-model systems, 75% accuracy (insufficient for privilege)
• ❌ Offshore Review Teams: Quality concerns, security risks, still expensive

The breaking point: A $2.3M case where manual privilege review missed 12 privileged documents, leading to a $180K settlement for inadvertent disclosure.

The Solution: NeuroGen AG2 Legal Discovery Intelligence

Morrison & Ellis chose NeuroGen for its unique AG2 multi-agent architecture—the only system offering cross-agent validation for privilege detection.

Why AG2 Multi-Agent Matters for Legal

Traditional AI uses a single model. NeuroGen deploys 7 specialized legal agents that collaborate:

1. Privilege Detector Agent

• Specialized in attorney-client communications
• 95%+ accuracy in privilege identification
• Detects subtle privilege markers (law firm email domains, legal terminology patterns)
• Flags edge cases for human review

2. Contract Analyzer Agent

• Extracts key contract terms and obligations
• Identifies critical dates and deadlines
• Maps party relationships and responsibilities
• Flags non-standard clauses

3. Risk Assessor Agent

• Evaluates litigation exposure
• Scores documents by relevance and risk
• Identifies smoking gun documents
• Provides strategic recommendations

4. Entity Extractor Agent

• Named entity recognition (people, companies, locations)
• Relationship mapping between entities
• Timeline construction from dates/events
• Cross-document entity linking

5-7. Supporting Agents

• Classification Agent: Document type categorization
• Relevance Scorer: Issue-based document ranking
• Summarization Agent: Executive summaries and key points

Cross-Agent Validation: The Game Changer

When agents disagree on privilege determinations:

1. Consensus Scoring: Multiple agents analyze independently
2. Conflict Resolution: Discrepancies flagged for senior attorney review
3. Confidence Amplification: Agreement between agents = higher confidence score
4. Learn & Improve: Human corrections train all agents

Result: 95%+ accuracy with built-in quality assurance—far exceeding single-model systems.

Implementation Journey: 8 Weeks to Full Deployment

Week 1-2: Pilot Case Selection

• Selected completed case with known privilege determinations (ground truth)
• 75,000 documents from commercial contract dispute
• Uploaded to NeuroGen Legal Discovery module
• Trained AG2 agents on firm's privilege criteria

Week 3-4: Accuracy Validation

• AG2 system analyzed all 75,000 documents
• Compared AI privilege determinations vs. human review
• Results: 96.3% accuracy rate (vs. 92% human baseline)
• Zero false negatives on privileged documents

Week 5-6: Integration & Training

• Connected NeuroGen to firm's Relativity eDiscovery platform
• Trained 15 attorneys on AG2 review workflows
• Established QC protocols for high-stakes documents
• Created privilege review templates

Week 7-8: First Production Case

• Deployed on active litigation with 120,000 documents
• AG2 processing: 18 hours (vs. 6 weeks manual)
• Senior attorney QC review: 3 days (vs. 2 weeks)
• Total timeline: 1 week vs. 8 weeks previous

The Results: Transformation Across Every Metric

Cost Savings Breakdown

Cost Category	Before NeuroGen	After NeuroGen	Annual Savings
External Vendor Costs	$450,000	$0	$450,000
Associate Review Time	$180,000	$20,000	$160,000
Privilege Review QC	$80,000	$15,000	$65,000
NeuroGen Subscription	$0	$36,000	($36,000)
Net Annual Savings	$710,000	$71,000	$639,000

ROI: 1,775% annual return on NeuroGen investment

Time Efficiency Gains

• Document Processing: 8 weeks → 18 hours (99.1% faster)
• Privilege Review: 3 weeks → 3 days (85% faster)
• Case Prep Time: 12 weeks → 2 weeks (83% faster)
• Client Deliverables: 4 weeks → 3 days (94% faster)

Quality & Risk Improvements

• ✅ Zero Privilege Breaches: 0% error rate vs. 3-5% manual baseline
• ✅ 96.3% Accuracy: Exceeds human reviewer performance
• ✅ 100% Audit Trail: Complete documentation for all determinations
• ✅ Consistent Quality: No fatigue-related errors

Strategic Business Impact

"NeuroGen didn't just save us money—it transformed our competitive position. We can now take on cases our competitors can't afford to handle. Our attorneys focus on strategy, not document drudgery."

• Case Volume: Increased capacity by 40% without hiring
• Win Rate: Improved 12% due to faster case prep and better insights
• Client Satisfaction: NPS score increased from 32 to 68
• Associate Retention: Reduced turnover from 35% to 18%
• Competitive Advantage: Won 8 new clients citing AI capabilities

Real-World Scenarios: How AG2 Performed

Scenario 1: Complex Privilege Determination

Document: Email chain with attorney CC'd but primarily business discussion

• Privilege Detector: 65% confidence (privileged) - attorney present
• Contract Analyzer: 30% confidence (not privileged) - business terms discussed
• Risk Assessor: Flagged for human review - borderline case
• Outcome: Senior attorney reviewed, determined not privileged. AG2 correctly identified edge case.

Scenario 2: Hidden Smoking Gun

Document: Internal memo buried in 120,000 document set

• Risk Assessor: 98% relevance to case issues
• Entity Extractor: Identified key executive involvement
• Summarization Agent: Flagged contradictory statements vs. depositions
• Outcome: Document became centerpiece of settlement negotiations. Saved client $2.1M in trial costs.

Scenario 3: Cross-Document Pattern Recognition

Challenge: Find all communications related to specific contract negotiation

• Entity Extractor: Identified 47 relevant parties across 8 companies
• Contract Analyzer: Linked documents by contract terms and clauses
• Timeline Construction: Built negotiation chronology automatically
• Outcome: Complete negotiation history in 2 hours vs. 2 weeks manual search

Implementation Best Practices

1. Start with Pilot Case

• Choose completed case with known outcomes
• Validate AI accuracy against human baseline
• Build confidence before production deployment
• Use results to secure stakeholder buy-in

2. Train Agents on Firm Standards

• Upload privilege policy documents
• Provide example privileged communications
• Define firm-specific criteria and edge cases
• Continuous learning from attorney corrections

3. Establish QC Protocols

• Senior attorney review for borderline cases
• Random sampling for quality assurance
• 100% review of hot documents
• Documented escalation procedures

4. Change Management

• Involve associates in pilot testing
• Demonstrate time savings with real cases
• Position as attorney augmentation, not replacement
• Celebrate early wins publicly

Lessons Learned

What Worked

• ✅ Pilot validation: Proving 96%+ accuracy built trust
• ✅ AG2 multi-agent: Cross-validation eliminated single-point failures
• ✅ Attorney involvement: Training improved with expert feedback
• ✅ Quick wins: First case results converted skeptics

Challenges Overcome

• ⚠️ Initial resistance: Addressed through pilot success metrics
• ⚠️ Integration complexity: NeuroGen support resolved in 1 week
• ⚠️ Workflow changes: Gradual rollout eased transition

The Future: Expanding AI Capabilities

Building on their success, Morrison & Ellis is expanding NeuroGen usage:

Phase 2 Roadmap (Next 6 Months)

• Contract Management: Automate contract review and drafting
• Legal Research: AI-powered case law analysis
• Client Deliverables: Automated report generation
• Business Intelligence: Win/loss analysis and strategy optimization

Projected additional savings: $300K annually

Conclusion: AI as Competitive Advantage

Morrison & Ellis's journey demonstrates that legal AI is no longer experimental—it's a competitive necessity:

• ✅ $639K annual savings (87% cost reduction)
• ✅ 95%+ faster processing (8 weeks → 18 hours)
• ✅ Zero privilege breaches (vs. 3-5% manual error rate)
• ✅ 40% capacity increase without headcount growth
• ✅ Competitive differentiation winning new clients

"We're not just saving money—we're delivering better outcomes for clients. That's the real power of AG2 multi-agent AI."

Ready to transform your legal practice? Start your free trial of NeuroGen today!