RAPID AI Assessment

Our RAPID AI Assessment helps organizations develop a comprehensive strategy to successfully adopt and implement Generative AI technologies to accelerate your adoption of AWS Generative AI solutions.

Review, strategize, and develop a Generative AI roadmap with Innovative and AWS

RAPID AI Assessment is a structured assessment program designed to evaluate your organization’s readiness for GenAI implementation and provide actionable roadmaps for successful deployment. We help organizations develop a comprehensive strategy to successfully adopt and implement Generative AI technologies with AWS solutions including Amazon Q Business Pro and other leading AI models.

Through this tailored engagement, Innovative helps organizations develop an AI Vision and Strategy that includes recommendations on data architecture, model selection, governance policies, and a comprehensive technology roadmap. The end result is a practical, actionable plan to guide the responsible and ethical adoption of Generative AI within your organization.

The assessment focuses on three key areas:

What You Can Expect

PHASE ONE

Discovery Workshops

Discovery involves conducting comprehensive workshops to understand your target environments, business value drivers, and strategic initiatives. This phase determines proper data storage models, governance policies, and identifies the most suitable GenAI use cases for your organization.

What is covered during this phase:

Workshop 1: Use Case Discovery (2-4 hours)
Workshop 2: Data Readiness Discovery (1-2 hours)
Workshop 3: Existing GenAI Application Assessment (if applicable)

PHASE TWO

Model Evaluation & Testing

Systematic evaluation and testing of generative AI models to determine the best fit for your specific use cases and requirements.

What is covered during this phase:

Assessment of existing datasets & testing scenarios
Model selection guidance based on use case requirements
Performance benchmarking analysis including latency, quality, and cost dimensions
Model performance comparison
Prompt optimization recommendations & latency optimization strategies
Model performance comparison documentation