eDiscovery, rebuilt for AI.
AI-native document review and analysis.
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The Problem
They added “AI.” They didn't rethink anything.
Relativity, Everlaw, and others have spent years rebranding decade-old techniques as AI. Their actual LLM features are expensive add-ons, sloppily bolted onto architectures built before large language models existed.
Classic ML disguised as AI
Predictive coding and TAR use supervised learning to sort documents into binary buckets — relevant or not. They've existed for over a decade. Legacy platforms market them as AI, but they can't read a contract, synthesize testimony, or surface the five documents that actually matter.
LLM features at legacy prices
Platforms that do offer generative AI charge a premium for it. Relativity bills per document processed. Industry-wide, AI-assisted review costs $0.20–$0.95 per doc — often matching the cost of a contract reviewer. The technology changed. The bill didn't.
Weeks of setup, not minutes
Deploying AI review on legacy platforms means 13+ rounds of prompt tuning, statistical validation on sample sets, and consultant-led configuration — before a single document is reviewed at scale. The review itself still runs at 40–50 docs per hour.
Built Different
Generate any column with AI
Looking for documents that indicate anti-trust issues? We scan every document and give you a score, quotes, and findings — as a column in your review table. Define whatever you need. The AI does the rest.
AI built into the architecture
Other platforms added AI features on top of legacy systems. We built the platform around AI from day one. Semantic search, document analysis, and custom extraction aren't separate modules — they're how the system works.
Frontier models, no add-on fees
We run frontier-class models for analysis, search, and extraction. On other platforms, AI features often come with separate pricing and consulting engagements. Ours are included.
Vision-language OCR
We use models like GLM-OCR instead of traditional OCR engines. They handle handwriting, poor scans, and complex layouts that conventional extraction misses. Better OCR means better everything downstream.
Public Archives
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