Accelerating legal discovery through generative AI and semantic intelligence.
Altumatim represents the next generation of e-discovery platforms, leveraging deep learning and Large Language Models (LLMs) to transcend traditional keyword-based search. Its technical architecture is built on a proprietary 'Deep Discovery' engine that performs semantic analysis across massive unstructured datasets, including email, chat, and cloud-hosted documents. Unlike legacy TAR (Technology Assisted Review) systems, Altumatim utilizes generative AI to map facts directly to legal theories, providing attorneys with an instant narrative rather than just a list of keywords. By 2026, the platform has positioned itself as a market leader in 'Agentic Legal Discovery,' where AI agents autonomously identify privilege, categorize risk, and draft initial review summaries with high precision. The platform's ability to handle multi-modal data and provide cross-matter intelligence makes it a critical tool for global law firms and corporate legal departments facing increasingly complex data volumes and regulatory requirements.
Altumatim represents the next generation of e-discovery platforms, leveraging deep learning and Large Language Models (LLMs) to transcend traditional keyword-based search.
Explore all tools that specialize in semantic document search. This domain focus ensures Altumatim delivers optimized results for this specific requirement.
Explore all tools that specialize in analyze and manage legal contracts. This domain focus ensures Altumatim delivers optimized results for this specific requirement.
Uses vector embeddings to find documents related by concept and context rather than just specific keywords.
AI-driven identification of attorney-client privilege with automated generation of detailed logs.
Generative AI summarizes key events across thousands of documents into a cohesive timeline.
Anonymized learning across different matters to identify recurring themes or high-risk custodians.
Real-time identification and masking of sensitive data (SSN, DOB, Credit Cards) using NER models.
Native-language processing for over 100 languages without the need for pre-translation.
Continuous Active Learning (CAL) models that prioritize the most relevant documents for human review.
Secure enterprise instance deployment via cloud or hybrid environment.
Configuration of data connectors (O365, Slack, G-Suite).
Initial ingestion and indexing of the matter data corpus.
AI model calibration based on specific case taxonomy.
Semantic search and concept clustering initialization.
Implementation of Predictive Coding 4.0 protocols.
Setting up automated redaction rules for PII/PHI.
Reviewer team workspace and permission hierarchy setup.
Integration with Relativity or other legacy CMS if required.
Final validation of the production-ready review environment.
All Set
Ready to go
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"Highly regarded for its ease of use compared to legacy systems, though enterprise setup requires dedicated technical support."
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