The client is a prominent legal firm seeking to optimize their document creation process, enhance the quality of legal outputs, and ensure compliance with industry standards.
1. Reduce the time required to generate legal documents.
2. Improve the quality and accuracy of legal outputs.
3. Stay updated with the latest industrial standards and regulations.
1. Data Training: Network Science initiated the project by gathering a diverse dataset of legal documents, document types encompassing all the possibilities within the chosen practice area . This dataset served as the foundation for training the Generative AI model on the enterprise data.
2. Data Fine-tuning: The initial model was fine-tuned using the client’s specific legal terminology, styles, and templates. This step aimed to tailor the AI model to the client’s unique requirements, ensuring it would generate outputs aligned with their standards.
3. Release and Testing: The fine-tuned model underwent rigorous testing to evaluate its performance. This phase included generating sample documents, comparing them with manually created documents, and refining the model as needed.
4. Hand-off: Upon successful testing, Network Science handed over the operational AI-driven platform to the client, providing necessary training and resources for seamless integration into their workflow.
5. Additional Feature Design: Customized features were designed to augment the platform’s capabilities. These included functionalities for document collaboration, version control, and automated legal research.
6. Repeat the Cycle: Continuous feedback loops were established to incorporate client feedback, refine the model further, and adapt to changing legal standards and requirements.
Generative AI-driven Co-creation using Network Science’s Generative AI platform:
1. Data Collection and Correction: The platform facilitated efficient data collection, allowing the client to upload and organize their existing documents. Additionally, it enabled real-time correction of generated outputs, ensuring accuracy and compliance.
2. Data Tagging: The platform has capabilities of tagging legal documents with relevant metadata, making it easier for users to search, categorize, and retrieve documents efficiently.
3. Fine-tuning (Co-creation): The AI model, through continuous interaction with legal experts, evolved into a co-creation tool. It learned from user inputs, adapting and refining its outputs over time to align closely with the preferences and styles of the legal team.
4. Continuous Improvement: Regular updates and refinements were made to the model based on user feedback, evolving legal standards, and emerging best practices. This
ensured that the platform stayed at the forefront of legal document generation technology.
1. 50% reduction in the time required to create legal document drafts.
2. Improved quality and accuracy of legal outputs, resulting in a 30% decrease in error rates.
3. The client consistently maintained compliance with the latest industrial standards, thanks to the platform’s adaptive learning capabilities.
By harnessing the power of Generative AI and continuous improvement, Network Science empowered the legal firm to streamline their document creation process, elevate the quality of their legal outputs, and remain at the forefront of the legal industry. The successful implementation of the Generative AI platform stands as a testament to the potential of AI-driven solutions in the legal domain.
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