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    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.

    Who It's For

    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

    Problem Solved

    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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    One-click templatesStart from a templateEach template deploys a working search app on your own files.Browse templates

    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

    $1.50$0.25

    83% reduction

    Evidence recall rate

    40%93%

    +133%

    Time to first production

    8 weeks2 weeks

    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. A unified multimodal pipeline processes the entire evidence corpus, 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.

    1
    Feature Extraction

    Turn raw media into structured intelligence

    2
    Semantic Multimodal Search

    Find anything across video, image, audio, and documents

    3
    Multimodal RAG

    LLMs that cite real clips, frames, and documents

    Feature Extractors Used

    text extraction

    Multimodal Extractor

    Unified embeddings for video, audio, image, and text: scene/silence chunking, Whisper transcription, thumbnails, and Gemini vision.

    ocr

    document classification

    face detection

    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

    Build this in the docs

    The exact stages and extractors this use case runs on, with API reference and worked examples.

    Document extractorParse productions and exhibits into retrievable records.PermissionsMatter-level access control, enforced on retrieval.

    Accelerate E-Discovery Review

    Clone the e-discovery pipeline and connect your document repository to start processing evidence across all modalities.

    Estimated setup: 3 hours
    Run this on your own data, from $25/moBook a demoDocumentation

    Frequently Asked Questions

    Is Mixpeek defensible for use in litigation e-discovery?
    Yes. Mixpeek provides full audit trails for every processing step, search query, and relevance determination. The system logs extraction methods, confidence scores, and reviewer interactions to support defensibility challenges. Processing methodology documentation is available for inclusion in discovery protocols and search methodology reports required by courts.
    How does Mixpeek handle privilege review across multimodal evidence?
    Privilege detection models identify attorney-client communications, work product, and privileged content across text documents, email threads, and audio/video recordings. The system flags potential privilege based on participant identification, content analysis, and communication context. All privilege determinations are presented for human attorney review before production.
    Can Mixpeek process video depositions and identify key testimony?
    Yes. Video depositions are processed through speech transcription with speaker diarization, enabling search by topic, speaker, or specific testimony. Facial expression and vocal tone analysis highlight moments of heightened significance. Attorneys search across all depositions in a matter simultaneously rather than reviewing recordings sequentially.
    How does semantic search improve recall over traditional keyword search?
    Semantic search understands conceptual meaning rather than exact word matches. A search for "efforts to conceal financial losses" finds relevant documents discussing "hiding red ink," "obscuring quarterly shortfalls," and "creative accounting to mask deficits" that keyword searches would miss entirely. This dramatically improves recall while maintaining precision through relevance scoring.
    What document formats are supported for e-discovery processing?
    Mixpeek processes all standard e-discovery formats: Microsoft Office documents, PDFs (native and image-based), email formats (PST, MBOX, EML), chat logs (Slack, Teams, SMS exports), images (TIFF, JPEG, PNG), video (MP4, MOV, AVI), audio (MP3, WAV, M4A), and database exports (CSV, structured data). Container files (ZIP, RAR) are automatically extracted and processed.
    How does Mixpeek handle foreign language documents in multi-jurisdictional matters?
    The system supports 30+ languages for text extraction, OCR, and speech transcription with automatic language detection. Cross-language semantic search enables English queries to surface relevant documents in other languages. Machine translation is available for review prioritization, with human translation reserved for key responsive documents.
    What is the processing capacity for large-scale litigation matters?
    Mixpeek scales horizontally to handle matters of any size. Typical processing throughput is 500,000-1,000,000 documents per day for text-based content and 1,000-5,000 hours of audio/video per day. A 5-million document matter with 10,000 hours of recordings processes in under two weeks on standard cloud infrastructure.
    Can Mixpeek integrate with existing e-discovery platforms like Relativity or Nuix?
    Yes. Mixpeek provides REST APIs for integration with major e-discovery platforms. Extracted metadata, relevance scores, and privilege flags can be exported in standard load file formats (Concordance, Summation, Relativity) for import into existing review platforms. This enables organizations to enhance their current workflow with multimodal AI without replacing existing tools.

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    Ready to Implement This Use Case?

    Our team can help you get started with AI-Powered E-Discovery in your organization.

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