Pilot Program for Dillard's

 

Pilot Program for Dillard's: Bridging Gen AI and Agentic AI with Equitus.us KGNN, Wallaroo.ai, and IBM Power11/ Spyre

Executive Summary:

This document outlines a pilot program for Dillard's Department Stores to leverage the unique capabilities of Equitus.us's Knowledge Graph Neural Network (KGNN), Wallaroo.ai's MLOps platform, and IBM's Power11 server. 

The program is designed to create a synergistic environment where generative AI (Gen AI) and agentic AI functions can work in tandem to address key business challenges in the retail sector. By using the on-premise, secure, and AI-optimized architecture of the Power11, the pilot will demonstrate a powerful, efficient, and scalable solution for deploying advanced AI models.

Pilot Program:

In the proposed powerful ecosystem, Equitus KGNN provides the "brain" by turning disconnected data into actionable intelligence. Wallaroo.ai provides the "nervous system" by managing the real-time AI and ensuring operational excellence. And IBM Power11 with Spyre cards provides the "muscle" to execute these AI tasks at scale, securely, and with a level of performance that is difficult to match in a traditional data center.

1. Program Objectives

The primary goal of this pilot is to demonstrate the value of a combined Gen AI and Agentic AI approach in a retail environment. The pilot aims to:

• Enhance Customer Experience: Provide hyper-personalized product recommendations and a more intuitive, conversational shopping experience.

• Optimize Inventory and Supply Chain: Automate inventory management by anticipating demand, identifying trends, and suggesting optimized stock levels.

• Improve Operational Efficiency: Reduce manual labor for data analysis and content generation by automating tasks such as product description creation and marketing copy.

• Establish a Secure and Scalable AI Infrastructure: Validate the IBM Power11 as a robust, on-premise foundation for AI workloads, ensuring data privacy and security.

• Bridge the Gap between Gen AI and Agentic AI: Showcase how an agentic AI system (powered by Equitus.us's KGNN) can proactively use a Gen AI model (managed by Wallaroo.ai) to achieve a multi-step business objective.

2. The Technology Stack

This pilot relies on a carefully selected and integrated technology stack:

• Equitus.us KGNN (Knowledge Graph Neural Network): Equitus.us's KGNN will serve as the core agentic AI component. Its function is to transform Dillard's raw, disconnected data (e.g., customer purchase history, browsing behavior, product information, and supply chain data) into a knowledge graph. This graph will enable the agent to "understand" complex relationships and dependencies, providing the context and agency needed for proactive decision-making. The KGNN is optimized to run natively on IBM Power11 without the need for GPUs, offering a cost-effective and high-performance solution.

• Wallaroo.ai (MLOps Platform): Wallaroo.ai's platform will be used to manage, deploy, and monitor the Gen AI models. It will provide the necessary infrastructure for:

  • Model Deployment: Rapidly deploying and scaling a Gen AI model trained on Dillard's specific data (e.g., product descriptions, brand voice, and marketing materials).

  • Inference Management: Efficiently running the Gen AI model to generate new content or respond to customer queries.

  • Performance Monitoring: Tracking the Gen AI model's performance and accuracy in real time, and providing feedback to improve its output.

• IBM Power11 Server: The entire program will run on-premise on an IBM Power11 server. This choice is critical for several reasons:

  • AI-Optimized Architecture: Power11 is built with on-chip AI acceleration, including the MMA AI accelerator, which is specifically designed for inference workloads.

  • Security and Data Sovereignty: Running the models on-premise ensures that Dillard's sensitive customer and business data remains within its own secure infrastructure.

  • Cost Efficiency: The Power11's ability to run AI workloads without a dependence on external GPUs can result in significant cost savings.

  • High Availability: The Power11's robust design and features like zero planned downtime ensure continuous operation for mission-critical applications.

3. Proposed Pilot Program Structure

The pilot will be structured in three distinct phases over a 90-day period.

Phase 1: Planning and Infrastructure Setup (Days 1-30)

• Team Formation: Assemble a cross-functional team with representatives from Dillard's IT, E-commerce, Marketing, and Supply Chain departments. The team will also include experts from Equitus.us, Wallaroo.ai, and IBM.

• Data Preparation:

  • Identify and secure relevant data sources (e.g., product databases, CRM data, customer transaction logs, inventory data).

  • Work with Equitus.us to cleanse, integrate, and structure the data for ingestion into the KGNN.

• Infrastructure Configuration:

  • Install and configure the IBM Power11 server.

  • Deploy Equitus.us KGNN and Wallaroo.ai platforms on the Power11.

  • Establish secure API connections between the KGNN, Wallaroo.ai, and Dillard's existing systems.

• Use Case Selection:

  • Focus on a high-impact, low-risk use case. The proposed initial use case is "Hyper-Personalized Product Recommendations and Conversational Assistance." This involves using the agentic AI to understand a customer's profile and needs and the Gen AI to create tailored recommendations and a natural language response.

Phase 2: Execution and Iteration (Days 31-75)

• Model Training and Deployment:

  • Equitus.us KGNN: Ingest the prepared data into the KGNN, allowing the system to build a comprehensive knowledge graph of Dillard's products, customers, and their relationships. This graph will serve as the "brain" of the agentic AI.

  • Gen AI Model (via Wallaroo.ai): Fine-tune a pre-trained Gen AI model on Dillard's product data and brand voice. Wallaroo.ai will then deploy this model for real-time inference on the Power11.

• Integration and Workflow Automation:

  • The agentic AI (KGNN) will proactively analyze a customer's profile as they browse the website.

  • Based on its analysis, the KGNN will formulate a specific query for the Gen AI model. For example, "Generate a concise, friendly product description for a customer who likes classic, high-end brands, for the [specific product]."

  • The Gen AI model will generate the content, and the agentic AI will present it to the customer via a conversational chatbot or a dynamic product page.

• Monitoring and Optimization:

  • Wallaroo.ai will monitor the performance of the Gen AI model, tracking metrics such as response time and accuracy.

  • The team will gather user feedback and A/B test the pilot solution against the existing recommendation system.

  • Regular meetings will be held with all stakeholders to discuss progress and identify areas for improvement.

Phase 3: Evaluation and Future Planning (Days 76-90)

• Pilot Metrics and Reporting:

  • Success Metrics: Evaluate the pilot's success based on pre-defined KPIs, including:

    • Customer Engagement: Increase in click-through rates on recommended products.

    • Conversion Rate: Percentage of customers who purchase a product after interacting with the AI.

    • Operational Efficiency: Time saved in content generation or manual recommendation tasks.

    • System Performance: Latency of the integrated system and resource utilization of the Power11.

• Roadmap for Expansion:

  • Based on the results, a detailed plan will be created for expanding the solution to other use cases, such as:

    • Automated Marketing Copy: Using the agentic AI to analyze current trends and customer segments and instructing the Gen AI to generate targeted email or social media campaigns.

    • Supply Chain Automation: The agentic AI analyzes real-time sales data and predicts future demand, and then uses the Gen AI to generate actionable reports or alerts for supply chain managers.

• Final Report and Recommendations: Present a comprehensive report to Dillard's leadership, detailing the pilot's findings, ROI, and recommendations for a full-scale rollout. This report will also highlight the strategic advantage of the on-premise, secure, and efficient AI infrastructure provided by the IBM Power11.