HCLTech’s Rapid Azure GenAI Experimentation offering allows customers to evaluate various LLM's by embracing product design, sprint, and lean start-up principles to rapidly experiment and validate ideas, Powering their Innovation Journey.

Below is the 'Approach' for LLM experimentations. All/some of these processes can be customized and implemented for the experimentation depending on the complexity of the use case, the business expectations, and the timescale.

• Set Objectives: Define the business objectives and goals for Azure Gen AI adoption

• Identify Use cases: Assess current systems and operations to identify specific use cases where AI can be implemented

• Develop Proof of Concept: Select a high-impact use case and develop a PoC to test the feasibility and effectiveness of the chosen AI technology

• Establish a Data Strategy: Develop a comprehensive data strategy to ensure the availability of high-quality, clean, and secure data for AI systems

• Build a Skilled Team: Assemble a team of AI experts, data scientists, and domain experts to develop, implement, and maintain AI solutions

• Uptrain/Fine-Tune LLM Model: Uptrain the LLM using labeled domain data and Prompt engineering to fine-tune output

• Integrate AI Systems: Integrate AI existing systems and processes to Ensure seamless integration with other enterprise systems

• Monitor and Optimize: Track key performance metrics and evaluate the impact of AI on operations, customer experience, and financial outcomes

Deliverables:

Business Framing (Week 1-2):

• Problem Statement Definition • Business Case Creation • Perceived value estimate • Analyze and Understand current platform capabilities • Evaluate data availability or cost to procure data • Cost & timeline estimate for experimentation

Solution Design & Set-up (Week 3-4)

• Develop solution design and design alternatives • Enable GenAI platform, ensuring proper integrations and workflows

Deliver Proof Of Concept (Week 5-10)

• Define success parameters • Acquire data • Rapid Exploratory Analysis & Model Building • Prompt engineering & Retrieval augmentation • Validation & Evaluation

POC Outcome (Week 11-12)

• Successful experimentation with LLM models to demonstrate the feasibility of using generative AI • Business Case Validation • Well-defined AI performance metrics • Roadmap for implementing production environment

Deliverables:

• Feasibility report with summarized findings from the POC/ experiment • Performance Metrics & Benchmarking Results • Roadmap for implementing in production environment

Specific to Life Science & Healthcare below are some proven use cases for Experimentation:

Patient Dropout and Site Prediction using Adverse Event - Utilizing Adverse Event data along with GenAI to predict patient dropouts and site-related issues in clinical trials, improving trial management and patient retention.

Smart Labelling- GenAI solution that can detect and link Summary of Product Characteristics (SmPC) to the appropriate sections of a corresponding version of a Patient Information Leaflet (PIL) and update the PIL with patient friendly language content that is compliant with regulatory agency standards such as QRD template, Excipient guidelines etc.

Reg Intelligence - Harnessing GenAI to gather, process, and analyze regulatory information, providing insights and intelligence related to regulatory affairs in the pharmaceutical and healthcare sectors.

Audit Automation - AI-driven document auditing system that identifies gaps with enhanced accuracy and quality of output, like summarization compared to the existing system.

Microsoft Cloud for Manufacturing

Результаты для Производство и мобильность : Приложения