#DeepJuris
Deep Juris offers Maestro DAO, a multi-agent orchestration platform for legal workflows that automates research, document analysis, calculations, and drafting under structured human supervision. The platform uses specialized AI agents with a human-in-the-loop model, ensuring every source is validated before final delivery. It also features a Model Router that automatically selects the optimal LLM for each task, controlling costs without sacrificing quality.
- Artificial Intelligence
- AI Agents
- Legal Technology
- Software Only
Funding
Founders
Product
Problem
Legal professionals spend up to 80% of their time on operational tasks such as manual legal research, document synthesis, and judicial calculations—work that does not require their expertise. This leaves little room for strategic thinking and delays case delivery, especially as demand grows without proportional team expansion.
Solution
Deep Juris provides Maestro DAO, a multi-agent orchestration platform built for the legal workflow. It automates legal research across official sources and jurisprudence, performs exact judicial calculations, analyzes case files via OCR, and drafts petitions, contracts, and opinions with traceable reasoning. The platform enforces a structured human-in-the-loop process where users validate sources before drafting and approve final documents, ensuring full control and auditability. It is model-agnostic, allowing seamless switching between LLMs, and supports on-premise deployment for data sovereignty.
Target Audience
Primary customers are law firms and legal departments that handle high volumes of research, document review, and judicial calculations and need to increase productivity without losing oversight or accuracy.
Features
- Multi-agent orchestration with specialized agents for research, calculations, document analysis, and drafting
- Human-in-the-loop workflow with source validation and approval checkpoints before final delivery
- Judicial calculation engine with traceable calculation memory for monetary correction, interest, and labor claims
- Predictive "Virtual Judge" that estimates case success probability based on historical court decisions
- Anomaly detection for predatory litigation patterns and abusive contractual clauses
- Internal RAG for semantic search across the firm's own library of theses, opinions, and precedents
- Model Router that automatically selects the optimal LLM per subtask, balancing speed, quality, and cost
- Dirty Flags for automatic document integrity verification and on-premise deployment option