Retail and eCommerce GenAI Modernization Assessment on AWS

Retail and eCommerce organizations are rapidly adopting generative AI to improve customer experience, optimize product discovery, automate content creation, and increase conversion rates. However, these initiatives often evolve in silos across marketing, digital commerce, and operations teams, resulting in inconsistent experiences, duplicated tooling, and limited scalability.

Without a unified architecture, retailers struggle to operationalize GenAI across key use cases such as product description generation, personalized recommendations, search optimization, and customer engagement, leading to missed revenue opportunities and increased operational costs.

Compass UOL helps retailers assess and modernize their GenAI landscape on AWS by identifying fragmentation across commerce platforms, customer data, and AI workloads. This assessment defines a scalable, AWS-native architecture that connects data, personalization engines, and GenAI services such as Amazon Bedrock.

The result is a structured roadmap to scale GenAI across digital commerce channels, improve conversion rates, reduce manual content operations, and enable real-time personalized shopping experiences while increasing AWS consumption in a controlled and efficient way.

Buyer Problem / Business Trigger

Fragmented GenAI initiatives across marketing, eCommerce, and customer experience platforms High manual effort in product catalog creation, content generation, and campaign execution Low conversion rates due to weak personalization and product discovery experiences Difficulty scaling GenAI across omnichannel commerce environments

Delivery Model

Discovery of current GenAI use cases and digital commerce workflows Assessment of data, customer platforms, and AI architecture Definition of AWS-native GenAI reference architecture Roadmap for scaling personalization and automation

Assessment / Engagement Scope

Evaluation of eCommerce workflows (catalog management, search, recommendations, campaigns) Mapping of GenAI use cases (product content generation, personalization, chat commerce) Assessment of customer data platforms and analytics pipelines Review of scalability, latency, and cost of AI workloads Design of AWS-native architecture (Bedrock, data lake, personalization services) Identification of inefficiencies, fragmentation, and missed automation opportunities

Expected Output / Deliverables

GenAI modernization assessment report AWS reference architecture for retail GenAI workloads Prioritized use case roadmap aligned to revenue and conversion impact Recommendations for cost optimization and operational efficiency Implementation roadmap for scaling GenAI across commerce channels

Customer Decision Questions This offer helps the customer answer:

How do we scale GenAI across eCommerce and customer experience without fragmentation? Which AWS architecture enables real-time personalization and search optimization? Where can GenAI improve conversion rates and reduce operational effort?