FinCore

Proposal: FinCore - Combination of Equitus SmartFabric (KGNN) with the foundational infrastructure of Core Scientific and the hyperscale cloud services of CoreWeave creates a powerful and optimized solution that addresses key data center challenges to reduce costs, improve security, and enhance scale.

FinCore - Intelligently connecting the data layer (Equitus) with the physical compute layer (Core Scientific/CoreWeave).

---

Reduced Costs and Improved Scale

The synergy drastically improves the efficiency and utilization of extremely expensive, high-density compute infrastructure (like NVIDIA GPU clusters).

1. Eliminating Data Prep Overhead (Cost/Scale)

• KGNN's Role: The Equitus KGNN automatically unifies, cleans, and contextualizes all enterprise data (structured and unstructured) into a semantically rich Knowledge Graph [1.1]. It extracts facts and relationships, skipping traditional, labor-intensive ETL (Extract, Transform, Load) pipelines.

• Data Center Impact: This offloads massive data preparation work from the GPU clusters housed in the Core Scientific/CoreWeave data centers. GPU cycles are saved for their highest-value task: AI model training and inference. This means businesses get more high-value compute per dollar spent on power and hardware.

• Hardware Efficiency: Equitus KGNN is optimized for IBM Power10 servers with MMA (Matrix-Math Assist), allowing high-performance deep learning to run without expensive, power-hungry GPUs for the data layer, which lowers hardware and energy costs [1.1].

2. Optimized Infrastructure Utilization (Cost/Scale)

• Context-Aware Scheduling: By providing a contextual view of data and workload requirements, the KGNN can be integrated with the CoreWeave platform's orchestration tools (like Kubernetes). This can lead to smarter workload scheduling, ensuring high-density racks are used optimally and preventing underutilization of servers [2.2].

• Faster Deployment: By having AI-ready data out of the box, the time and manual effort required to deploy new AI and analytics applications are drastically reduced, enabling faster scaling of business solutions [1.1].

---

Improved Security

The KGNN structure and its deployment model directly enhance data security and control.

3. Enhanced Data Control (Security)

• On-Premises Option: Equitus KGNN allows enterprises to run the core data unification and graph processing on-premises [1.1]. This is crucial for financial precision (Fincore) workloads, as it ensures sensitive, regulated data remains within the company's full control, mitigating the security and compliance risks associated with moving large volumes of data to the public cloud.

4. Anomaly and Fraud Detection (Security)

• Hidden Pattern Detection: The graph structure excels at uncovering hidden patterns and anomalies within interconnected data [1.1]. In a financial context, this allows for highly sophisticated, real-time fraud detection by identifying complex, non-obvious relationships between transactions, users, and accounts that traditional security systems might miss.

This fusion essentially provides the Intelligent Foundation for Finance by coupling cutting-edge, secure, and context-aware data processing (Equitus) with scalable, high-density compute infrastructure (Core Scientific/CoreWeave).