LegalMind: Agentic AI-Driven Process Optimization and Cost Reduction in Legal Services Using DeepSeek
The legal industry struggles with inefficiencies, high costs, and manual-intensive workflows. Traditional AI lacks adaptability in optimizing legal operations. To address this, we propose LegalMind, an agentic AI-driven framework leveraging DeepSeek R1 for intelligent legal process automation and co...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11072348/ |
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| Summary: | The legal industry struggles with inefficiencies, high costs, and manual-intensive workflows. Traditional AI lacks adaptability in optimizing legal operations. To address this, we propose LegalMind, an agentic AI-driven framework leveraging DeepSeek R1 for intelligent legal process automation and cost reduction. LegalMind integrates a structured legal dataset and fine-tunes DeepSeek R1 to enhance decision-making and workflow efficiency. Experimental results show a 42.6% cost reduction and a 60.8% improvement in document processing speed over baseline AI models. Scalability tests confirm the system’s ability to handle 100,000 queries efficiently. Real-world case studies validate LegalMind’s effectiveness in law firms, corporate legal departments, and government agencies, demonstrating significant reductions in case preparation time and operational costs. These findings highlight the transformative potential of agentic AI in legal automation, optimizing workflows and improving decision support. |
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| ISSN: | 2169-3536 |