AIMLUX : Proposed Pilot

 

Equitus KGNN, Wallaroo.ai, and IBM Power 11's MMA architecture, your proposed pilot project can create significant value for Dillard's department store, particularly for FCU (fraud control unit) and address correlation.

Value Proposition

The synergy between these three technologies can deliver value in the following ways:

• Accelerated Fraud Detection: Equitus KGNN excels at transforming fragmented and disconnected data—such as customer transactions, purchase history, and address information—into a structured knowledge graph. This graph makes it easier to uncover hidden connections and patterns that are indicative of fraudulent activity.

• High-Performance AI Inference: Wallaroo.ai's platform can then be used to rapidly deploy and manage AI models on the knowledge graph data for fraud detection and address correlation. Its optimization for IBM Power servers means these models can perform with high efficiency and low latency, essential for real-time applications.

• On-Chip AI Acceleration: The IBM Power 11's Matrix Math Assist (MMA) architecture is designed to run AI inference workloads directly on the CPU, alongside Dillard's mission-critical transactional systems. This eliminates the need for separate, expensive GPU servers, reducing infrastructure costs and complexity. It also allows the AI models to process data where it resides, leading to a faster, more integrated solution for identifying fraudulent transactions and correlating addresses in real-time.

This combined approach provides a powerful, cost-effective, and highly performant solution for a major retail enterprise like Dillard's, enabling them to make smarter, faster decisions regarding fraud and customer data.

Equitus/Wallaroo Pilot Program

 

Equitus.AI and Wallaroo.AI, here's how they could create and execute pilot programs to demonstrate their combined value to IBM Power11 users:

1. Define the Pilot Program's Core Value Proposition

Equitus/Wallaroo Pilot program must clearly demonstrate how the combined solution addresses a major pain point for enterprises: the gap between preparing data for AI and getting AI models into production. The key message would be: "From Disparate Data to Deployed AI—Faster, More Efficiently, and Without GPUs on IBM Power11."

• Equitus.AI's Role: Show the ability to ingest and unify messy, siloed data from various sources (e.g., enterprise databases, unstructured documents, video feeds) and transform it into a single, contextualized knowledge graph. The pilot would highlight the speed and efficiency of this "AI-ready" data preparation.

• Wallaroo.AI's Role: Showcase the seamless transition from the Equitus.AI-prepared data to a production-ready model. The pilot would emphasize Wallaroo's ability to deploy, manage, and monitor the AI model for high-performance inference on the IBM Power11's CPU-based architecture, demonstrating GPU-like performance without the associated costs and complexities.

• IBM Power11's Role: Serve as the optimized, underlying infrastructure. The pilot would leverage the in-core AI acceleration of the Power11 to prove that this solution is not only possible but also a superior, more cost-effective alternative to traditional GPU-based AI pipelines.

2. Identify Target Industries and Use Cases

To make the pilot programs impactful, they should focus on industries where data is fragmented and AI is mission-critical. Potential target industries and use cases include:

• Financial Services:

  • Pilot Use Case: Fraud detection.

  • Equitus.AI: Fusing transaction data, customer data, and external risk data (e.g., sanction lists) into a knowledge graph to identify complex, non-obvious fraudulent networks.

  • Wallaroo.AI: Deploying and monitoring a model that uses the knowledge graph to score transactions for fraud in real-time. The pilot would demonstrate high-volume, low-latency inference on the Power11 system.

• Manufacturing/Industrial IoT:

  • Pilot Use Case: Predictive maintenance.

  • Equitus.AI: Unifying sensor data from machinery with maintenance logs, historical failure data, and environmental factors into a single, holistic view.

  • Wallaroo.AI: Operationalizing an AI model that predicts machine failures based on the unified data, enabling real-time alerts and proactive maintenance on the edge or in a central location.

• Government/Defense & Intelligence:

  • Pilot Use Case: Situational awareness and intelligence fusion.

  • Equitus.AI: Combining data from disparate sources like open-source intelligence (OSINT), geospatial data, and sensor feeds into a unified knowledge graph.

  • Wallaroo.AI: Running a computer vision or natural language processing model on this data to provide real-time insights and alerts to operators on the Power11, demonstrating secure, on-premises AI processing.

3. Establish a Clear Pilot Program Framework

To ensure success, a structured approach is essential. The pilot program should include:

• Phase 1: Discovery & Scoping (1-2 Weeks): Work with a potential customer to identify a specific business challenge that the combined solution can solve. Define the scope, key performance indicators (KPIs), and success criteria for the pilot.

• Phase 2: Deployment & Data Fusion (2-4 Weeks): Deploy the Equitus.AI KGNN platform on the customer's IBM Power11 system. Equitus.AI experts work with the customer's data to unify and contextualize it, demonstrating the platform's speed and efficiency.

• Phase 3: Model Operationalization & Inference (2-3 Weeks): A pre-trained or new model is provided to Wallaroo.AI. Wallaroo.AI's platform is used to deploy, optimize, and run the model on the prepared data. This phase would include a live demonstration of the model's performance (e.g., latency, throughput) and a comparison to the customer's existing methods.

• Phase 4: Results & Business Impact Analysis (1 Week): Present a detailed report to the customer outlining the pilot's success. This report would include:

  • A before-and-after analysis of data preparation time.

  • Quantifiable metrics on AI model performance (e.g., inference speed, cost per inference).

  • A business impact statement on how the solution addresses the initial challenge.

4. Create Joint Marketing and Sales Materials

Beyond the pilot itself, it's crucial to promote the partnership. This can be done through:

• Webinars and Joint Presentations: Co-host webinars with IBM to showcase the integrated solution and share pilot program results.

• Case Studies: Upon successful completion, create a detailed, public-facing case study with the pilot customer's permission.

• Joint Demo Environments: Create a persistent, on-demand demo environment on IBM Power11 where potential customers can see the platforms working together with sample data.

• Sales Enablement: Train both Equitus.AI and Wallaroo.AI sales teams on the joint value proposition, common use cases, and how to identify opportunities for the pilot program.