Altumatim: Electronic discovery
Uses Gemini on Vertex AI to analyze large document collections for electronic discovery. Task: Altumatim uses Gemini on Vertex AI to analyze millions of documents for electronic discovery (eDiscovery). The system processes large legal document collections to identify relevant materials, allowing attorneys to focus on building legal arguments rather than manual review. The specific human approval workflow is not detailed in the source.; tools: Google Cloud AI. Human role: Not stated in the source; limitations: Performance data is vendor-reported; validate on your own workload. The source does not detail the human-in-the-loop approval process or exception handling for low-confidence results. Integration with existing legal case management systems is not specified..
Tools
Published on Agent Work Atlas:
Overview
Uses Gemini on Vertex AI to analyze large document collections for electronic discovery.
Workflow
Altumatim uses Gemini on Vertex AI to analyze millions of documents for electronic discovery (eDiscovery). The system processes large legal document collections to identify relevant materials, allowing attorneys to focus on building legal arguments rather than manual review. The specific human approval workflow is not detailed in the source.
Reported result
Google Cloud reports that the process time was reduced from months to hours and accuracy improved to over 90%. These metrics are vendor-reported and attributed to Altumatim's implementation. No independent verification of these specific outcomes is provided in the source.
Human role
Not stated in the source
Limitations
Performance data is vendor-reported; validate on your own workload. The source does not detail the human-in-the-loop approval process or exception handling for low-confidence results. Integration with existing legal case management systems is not specified.
Try it
I want to pilot an eDiscovery workflow using Gemini on Vertex AI. My context is [replaceable: legal firm name] handling [replaceable: case type] with [replaceable: number] documents. The source files are located at [replaceable: URL or path]. Target users are [replaceable: attorney roles]. Output location is [replaceable: system or folder]. Steps: 1. Ingest a small, labeled sample of 50 documents. 2. Use Gemini on Vertex AI to classify relevance. 3. Compare AI output against human-labeled ground truth. 4. Calculate precision, recall, and missed relevant documents. 5. Present results to me for confirmation before scaling. Deliverables: A report with accuracy metrics and a list of false negatives. Success is defined as >90% recall on the sample. If inputs are missing, ask for the document sample and labeling criteria. Do not process real client data without explicit approval.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.