E-Discovery Machine Learning That Revolutionizes Litigation

Accelerate document review by 85% with AI-powered e-discovery. Reduce litigation costs, improve accuracy, and gain strategic advantages with predictive coding and technology-assisted review.

Trusted by 300+ law firms | 50M+ documents processed | 92% review cost reduction

The E-Discovery Cost Crisis

Traditional document review methods drain litigation budgets and create strategic disadvantages in complex cases.

Review Bottlenecks

Manual reviewers process only 50-75 documents per hour, creating months-long timelines for multi-million document cases.

Exploding Costs

E-discovery costs average $18,000 per GB of data reviewed, making litigation prohibitively expensive for many cases.

Human Error

Reviewer fatigue and inconsistency lead to 20-30% error rates, missing critical evidence and creating defensibility issues.

Data Volume Growth

E-discovery volumes increase 60% annually with email, Slack, Teams, and cloud storage adding complexity.

AI-Powered E-Discovery Platform

Our machine learning platform combines predictive coding, continuous active learning, and advanced analytics to transform litigation support workflows.

1

Intelligent Data Collection & Processing

Automated collection from 200+ data sources including email servers (Exchange, Gmail, Office 365), cloud storage (Box, Dropbox, SharePoint), messaging platforms (Slack, Teams, WhatsApp), and legacy systems. Advanced deduplication eliminates redundant documents across custodians, reducing review sets by 40-60%. Near-deduplication identifies substantially similar documents for efficient review. Threading reconstructs email conversations to understand context. OCR processing extracts text from images and PDFs with 99.2% accuracy. Metadata extraction captures timestamps, authorship, and communication patterns critical for timeline analysis.

2

Predictive Coding & Technology-Assisted Review

Machine learning models analyze attorney-coded documents to understand relevance criteria, then apply that learning to predict relevance across millions of unreviewed documents. Continuous active learning (CAL 2.0) improves accuracy with each attorney decision, prioritizing the most informative documents for review. Achieve 80% recall reviewing only 15-20% of documents—a 5x efficiency gain over linear review. Support for multi-issue coding, privilege determination, and hot document identification. Transparent model explanations show why documents were classified, ensuring defensibility in court challenges.

3

Concept Clustering & Semantic Analysis

Unsupervised machine learning automatically groups documents by conceptual similarity, revealing case themes and key topics without manual review. Concept clusters identify document families discussing similar issues, enabling targeted review strategies. Visual analytics display document relationships, helping attorneys understand case narrative structure. Anomaly detection surfaces unusual documents that may contain critical evidence. Cross-custodian analysis reveals communication patterns and potential witness credibility issues. Named entity recognition extracts people, organizations, locations, and dates for timeline reconstruction and fact analysis.

4

Advanced Analytics & Visualization

Interactive dashboards provide real-time insights into document populations, review progress, and case strategy. Communication network graphs visualize email patterns, identifying key players and potential witnesses. Timeline analysis plots document creation and communication chronologically, revealing critical case events. Sentiment analysis detects emotionally charged language that may indicate problematic conduct. Production analytics track responsiveness rates, privilege assertions, and opposing party patterns. Quality control metrics monitor reviewer consistency and identify documents requiring senior attorney escalation.

5

Defensible Workflow & Audit Trails

Comprehensive audit trails document every decision, search, and export for complete defensibility in court challenges. Validation testing measures AI model accuracy using statistically sound methodologies accepted by courts. Detailed reports explain machine learning methodology, training set composition, and quality metrics for expert testimony support. Role-based access controls ensure proper information barriers for privilege review. Secure collaboration enables geographically distributed review teams with consistent quality standards. Export productions in industry-standard formats (EDRM XML, Concordance, Relativity load files) for seamless downstream use.

See E-Discovery AI in Action on Your Data

Upload a sample document set and watch our AI identify key documents, cluster themes, and predict relevance in real-time. Schedule a personalized demonstration today.

Request Pilot Project

Proven E-Discovery Results

Measurable litigation support improvements from law firms using our machine learning platform.

85%
Review Acceleration

Faster document review completion using predictive coding

92%
Cost Reduction

Lower e-discovery expenses compared to linear review

96%
Recall Achievement

Relevant document identification accuracy rate

50M+
Documents Processed

Total volume analyzed across client engagements

Litigation Success Story

"Boaweb AI's e-discovery platform was game-changing for our patent infringement case involving 8 million documents. Predictive coding identified the 200,000 most relevant documents with 97% accuracy, completing first-pass review in 6 weeks instead of the projected 9 months. The AI's concept clustering revealed prior art our opponents missed. We won summary judgment based on evidence we likely wouldn't have found through manual review. The $2.3M we saved in review costs alone exceeded the entire litigation budget."

— David Park, Partner, Intellectual Property Litigation, Morrison & Associates

Frequently Asked Questions

How defensible is predictive coding in court?

Predictive coding has been approved by courts in hundreds of cases since the landmark Da Silva Moore decision in 2012. Our platform follows industry best practices including transparent methodology, validation testing, and comprehensive audit trails. We provide detailed reports documenting AI accuracy, training set composition, and quality control measures suitable for expert testimony. Most courts now recognize TAR as more defensible than manual review given human inconsistency. We offer litigation support to defend protocol challenges.

What data sources can you collect from?

We support 200+ data sources including email platforms (Office 365, Gmail, Exchange, Lotus Notes), cloud storage (Box, Dropbox, OneDrive, SharePoint), collaboration tools (Slack, Teams, Confluence, Jira), social media, mobile devices, and legacy backup systems. Forensic collection preserves metadata and chain of custody. Remote collection minimizes travel costs and accelerates timelines. We handle structured data (databases, spreadsheets) and unstructured content (documents, presentations, images). Custom connectors available for proprietary systems.

How do you ensure data security during e-discovery?

We maintain SOC 2 Type II certification and ISO 27001 compliance with bank-level security. All data is encrypted in transit (TLS 1.3) and at rest (AES-256) in geographically redundant data centers. Role-based access controls with multi-factor authentication protect against unauthorized access. Detailed audit logs track all user activity. We support attorney-client privilege firewalls and ethical walls for conflicted matters. On-premise deployment available for maximum security. Annual penetration testing and security audits ensure ongoing protection.

What's the learning curve for litigation teams?

Most attorneys become proficient within 1-2 days of hands-on training. Our interface is designed for legal professionals, not data scientists—no coding or technical expertise required. Comprehensive training programs include live webinars, video tutorials, and certification courses. Dedicated project managers guide workflows for first few matters. Built-in wizards walk users through TAR protocols step-by-step. Phone and email support available 24/7 during active cases. Many firms assign junior attorneys as platform champions to support colleagues.

How does pricing work for e-discovery projects?

We offer both per-GB and subscription pricing models. Per-GB pricing starts at $35/GB for processing and $12/GB/month for hosting (significantly below industry averages). Subscription licenses provide unlimited processing and hosting for predictable costs—ideal for organizations with ongoing litigation. No minimum commitments or long-term contracts. Review attorney seats charged separately at $150-200/user/month depending on volume. Custom pricing available for large matters over 5TB. Early case assessment pilots often completed at no charge to demonstrate value.

Transform Your Litigation Strategy with AI E-Discovery

Join 300+ law firms cutting e-discovery costs by 92% while improving accuracy. Start with a free early case assessment on your next matter.

No data uploads required for initial consultation | Court-approved methodology | 24/7 litigation support