Centific proudly presents our GenAI Foundational Workshop, crafted to help healthcare and life sciences organizations implement Generative AI solutions that transform critical processes on the AWS platform. With Centific’s deep industry expertise, this workshop supports teams in deploying real-world AI applications on AWS that streamline regulatory documentation, accelerate fraud detection, and create synthetic data for research.

Our experts guide participants through the components of an AI data foundry platform built specifically to meet the stringent requirements of healthcare and life sciences on AWS. This platform integrates advanced tools to support documentation automation and synthetic data generation. With Centific’s approach to rapid deployment and tailored AI model customization on AWS, your organization will gain agility in managing diverse healthcare data sources and stay competitive with a solution that meets industry-specific needs.

Workshop structure

The GenAI Workshop consists of a series of joint architecture design meetings, ranging from 1 to 2 hours per session (up to 8 hours total), focusing on the following healthcare-specific domains:

• Data Modernization: Prepare data platforms for scalable AI by integrating structured and unstructured data from various sources, including patient records, clinical trial data, and regulatory documentation.

• Data Supply: Build secure data pipelines that efficiently manage large datasets while maintaining patient privacy, ensuring compliance, and supporting advanced data processing needs for R&D and clinical applications.

Model Fine-Tuning: Customize AI models for tasks like automated compliance checks, clinical documentation, and fraud detection. This fine-tuning ensures that models meet the accuracy and compliance standards required in highly regulated healthcare environments.

• Infrastructure & Deployment: Establish best practices for deploying GenAI in secure, regulatory-compliant environments, exploring options like secure cloud and local hosting to ensure high standards of data security and privacy.

• GenAI Agents & Knowledge Assistance: Deploy AI-powered agents to support R&D, manage compliance processes, and assist healthcare professionals in decision-making. These agents provide fast, reliable responses and facilitate efficient knowledge discovery, improving operational workflows.

Each session emphasizes discovery, design, and education, showing how an AI data foundry platform can accelerate documentation, compliance, and data synthesis in healthcare while ensuring data security and regulatory compliance.

Deliverables

• Technical Deep-Dive Discovery Sessions: Collaborative workshops with Centific’s AI experts to align your GenAI strategy with healthcare goals such as streamlined compliance, secure data handling, and improved operational efficiency.

• Future-State Architecture Diagram: A detailed framework designed for healthcare, covering secure data management, regulatory automation, and R&D support, enabling faster delivery of treatments and efficient clinical trials.

• Recommendations Report: A summary of key insights and strategic choices to prioritize AI use cases such as clinical documentation automation, fraud detection management, and synthetic data generation for R&D.

• Cost Estimation: Forecast the Total Cost of Ownership (TCO) for your GenAI deployment using Centific’s tools, ensuring resource efficiency while adhering to data privacy and compliance requirements.

• Implementation Proposal: A comprehensive plan outlining the scope and costs to safely deploy and scale AI for healthcare, with action steps for successful implementation.

Timeline

The workshop is structured to be completed in approximately two weeks, depending on the availability of your project sponsor and technical teams. For convenience, we also offer the option of conducting the Workshop as an 8-hour onsite, in-person session.

Next Steps

Typical next steps include proof-of-concept deployments to demonstrate the AI solution’s capabilities, followed by full production implementation. These steps establish a framework for effectively deploying AI in healthcare, addressing needs in documentation, compliance, and operational efficiency.