Next-Best X Platform: Starter Kit for AWS

Today's Challenges Many organizations struggle to deliver timely and personalized actions to customers, patients, or users across channels. Engagement is often static or siloed, driven by batch logic or rules-based systems that fail to adapt to real-time behavior or multivariate business goals. Without a scalable way to determine the best next step for each user in context, companies risk missed opportunities, reduced retention, and fragmented customer experiences.

Traditional recommendation engines often lack flexibility, explainability, or the ability to balance objectives such as conversion, compliance, or operational constraints. Building a custom solution requires data science, engineering, and orchestration logic that many teams cannot implement or maintain efficiently.

Grid Dynamics Solution The Next-Best X Platform Starter Kit helps organizations launch a production-grade orchestration layer for delivering real-time, context-aware decisions using AWS-native services.

This accelerator includes:

• A multi-model decision engine integrating Amazon Personalize with optional custom ML models in SageMaker
• Real-time inference and triggering via Lambda, EventBridge, and API Gateway
• Integration with CRM, web, mobile, or call center channels
• A feedback loop for reinforcement learning or online tuning
• Personalization and filtering logic that supports business goals such as relevance, diversity, risk control, or inventory optimization

The solution supports use cases such as next-best offer (NBO), next-best action (NBA), and next-best treatment (NBT) across industries including retail, healthcare, and financial services.

Getting Started

• Discovery & Workshop: Grid Dynamics will assess current data sources, engagement logic, and target outcomes. Together, we will define personalization goals and constraints.

• Platform Deployment: We will build a modular orchestration engine in your AWS environment using Personalize, Lambda, EventBridge, and optionally SageMaker for custom ML models.

• Operationalization: Output routing, reporting, and model optimization workflows will be configured to support business and technical users.