AI-Powered E-Discovery
Reduce e-discovery review time and costs with AI that processes documents, emails, video depositions, audio recordings, and image evidence in a single multimodal pipeline. Find relevant evidence faster with semantic search.
Law firms, corporate legal departments, litigation support providers, and e-discovery vendors processing 100,000+ documents per matter across civil litigation, regulatory investigations, and internal compliance reviews
E-discovery review is the most expensive phase of litigation, with large matters requiring teams of contract reviewers spending months reading millions of documents at $50-200 per hour. Video depositions, audio recordings, and image evidence are reviewed separately with different tools, creating gaps and inconsistencies in privilege and relevance determinations.
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Before & After Mixpeek
Before
Review throughput
40-60 documents per reviewer per hour
Evidence recall
30-50% with keyword search
Video deposition review
Separate workflow, manual timestamps
After
Review throughput
200-400 documents per reviewer per hour with AI prioritization
Evidence recall
90-95% with semantic search
Video deposition review
Unified search across all evidence types
Review cost per document
83% reduction
Evidence recall rate
+133%
Time to first production
75% faster
Why Mixpeek
Unifies document review, video deposition analysis, and audio evidence processing in a single platform. Semantic understanding finds conceptually relevant evidence that keyword searches miss, while multimodal processing eliminates the gaps created by reviewing text, video, and audio in separate workflows.
Overview
AI-powered e-discovery transforms the most time-consuming and expensive phase of litigation. Traditional review requires armies of contract attorneys reading documents one by one, supplemented by keyword searches that miss conceptually relevant evidence and separate workflows for video and audio. Mixpeek processes the entire evidence corpus through a unified multimodal pipeline, enabling semantic search, predictive relevance coding, and cross-modal evidence discovery.
Challenges This Solves
Review Cost Explosion
Large litigation matters generate millions of documents requiring human review at $50-200 per reviewer hour
Impact: E-discovery costs regularly exceed $1M per matter, with document review consuming 60-80% of total litigation spend
Keyword Search Limitations
Boolean keyword searches miss synonyms, conceptual relevance, and evidence expressed in non-text formats
Impact: Recall rates of 30-50% mean responsive documents are missed, creating defensibility risks and potential sanctions
Multi-Modal Evidence Gaps
Video depositions, voicemails, and image evidence are processed in separate workflows with different tools and review teams
Impact: Cross-modal connections (an email referencing a recorded call) are missed, weakening case theories and privilege determinations
Recipe Composition
This use case is composed of the following recipes, connected as a pipeline.
Feature Extractors Used
Retriever Stages Used
semantic search
filter aggregate
Expected Outcomes
83% lower cost per document
Review cost reduction
93% recall vs. 40% baseline
Evidence recall
75% faster first production
Time to production
5x documents per hour
Reviewer throughput
Accelerate E-Discovery Review
Clone the e-discovery pipeline and connect your document repository to start processing evidence across all modalities.
Frequently Asked Questions
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