Aidena

— AI STACK RECOMMENDATION

AI eDiscovery Platform for Litigation Document Analysis

Scalable document processing, intelligent extraction, and semantic search for millions of litigation documents with cost-effective inference and enterprise-grade observability.

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AI eDiscovery Platform for Litigation Document Analysis

Scalable document processing, intelligent extraction, and semantic search for millions of litigation documents with cost-effective inference and enterprise-grade observability.

high confidence

Core Stack ℹ︎

AWS Textract

Primary

Extracts text, tables, and form fields from scanned PDFs and documents at scale. Essential for converting millions of litigation documents into machine-readable format with high accuracy for legal compliance.

$1.50/1k pages

Cohere Embed API

Primary

Multimodal embeddings for semantic search across document collections. Enables finding relevant documents by meaning rather than keywords, critical for discovery workflows analyzing millions of documents.

$0/month (free tier available)

Elasticsearch Vector Search

Primary

Hybrid vector + BM25 search at scale. Handles millions of document embeddings with fast retrieval, essential for eDiscovery platforms needing both semantic and keyword-based search across massive datasets.

$0-500/month (self-hosted or managed)

Claude Opus 4

Primary

Most capable model for complex legal analysis, contract interpretation, and nuanced document classification. Handles long documents and maintains context for litigation-specific reasoning.

$15-50/month (depending on volume)

Complete the Stack ℹ︎

Datadog LLM Observability

Alternative

Enterprise monitoring for LLM costs, latency, and quality across millions of document analyses. Critical for tracking compliance, audit trails, and cost management in litigation workflows.

$299+/month

Airbyte

Alternative

Ingests documents from multiple sources (cloud storage, databases, file systems) into centralized pipeline. Handles the data integration complexity of aggregating millions of documents from various litigation sources.

$0/month (open-source)

Getting started

  1. 1Deploy AWS Textract to batch-process incoming litigation documents (PDFs, scans, images) into structured text and metadata.
  2. 2Use Airbyte to orchestrate document ingestion from multiple sources (email, SharePoint, cloud storage) into a centralized data lake.
  3. 3Generate embeddings with Cohere Embed API for all extracted documents and store in Elasticsearch with vector search enabled.
  4. 4Build search interface allowing attorneys to query by semantic meaning (e.g., 'contracts mentioning liability caps') and keyword filters.
  5. 5Use Claude Opus for document classification, privilege assessment, and relevance scoring on candidate document sets.
  6. 6Implement Datadog LLM Observability to track analysis costs, latency, and maintain audit logs for litigation compliance.
  7. 7Scale Elasticsearch cluster horizontally as document volume grows; use AWS Textract's batch API for cost-efficient processing of millions of pages.
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