A potential technology partnership between Equitus.us and IBM Power 11 systems to assist a Cadillac and Ferrari F1 effort could work in the following way, integrating the new information you provided.
Ferrari will be supplying power units and gearboxes to the Cadillac F1 team from the 2026 season. Additionally, Equitus Corporation is a verified IBM Independent Software Vendor (ISV) and their KGNN (Knowledge Graph Neural Network) solution is indeed available on IBM Power systems, which now include the new Power 11 series.
Proposed partnership function:
1. Real-Time Data Ingestion and AI-Ready Infrastructure
The core of the partnership is the ability to handle the massive volume of data from the F1 car's 300+ sensors in real-time.
IBM Power 11's Role: The IBM Power 11 systems would serve as the on-premise, high-performance computing backbone. As an IBM ISV, Equitus.us would have optimized its KGNN software to take full advantage of the Power 11's hardware, including its on-chip AI acceleration. This would allow the F1 team to ingest the millions of telemetry data points generated per second with minimal latency. This is critical for Formula 1, where every millisecond counts.
Equitus.us Kgnn's Role: The Equitus.us KGNN platform, running on the Power 11 systems, would ingest and unify the disparate data streams. It would create a "knowledge graph" that connects data from various sensors—engine temperature, tire pressure, suspension dynamics, etc.—to external data like track conditions, weather, and historical race data. This unified data model would be the foundation for all subsequent analysis.
2. Development and Predictive Analysis
The unified data would be used to drive performance improvements both during and after a race.
In-Race Analysis: During a race, the Equitus.us platform would use its machine learning models to analyze the real-time data stream. The models, running on the Power 11's accelerated architecture, could provide immediate predictive insights. For example, by analyzing patterns in engine stress and vibration data, the system could alert the team to a potential component failure before it happens, allowing them to adjust strategy or bring the car in for a pit stop to avoid a race-ending failure.
Development and Simulation: After a race, the Equitus.us knowledge graph would become a repository of historical data. The team could leverage this to conduct deep-dive analysis and simulations. For instance, engineers could query the graph to compare different car setups at a specific corner from previous races, or simulate the impact of a new aerodynamic component on tire wear. This is a direct parallel to how Equitus.us helped analyze performance data for the Scuderia Cameron Glickenhaus WEC team, which was also focused on optimizing a small number of cars.
3. Logistics and Team Integration
The partnership's capabilities would extend beyond track performance to optimize overall team operations.
Supply Chain Optimization: The Equitus.us KGNN could connect sensor data on component wear to the team's inventory and logistics systems. The platform could predict when a specific part—say, a brake disc or a suspension component—will reach the end of its life, allowing the logistics team to ensure a replacement is available at the track and ready for use.
Unified Access: The IBM Power 11's robust, secure, and highly available architecture would ensure that all members of the team, from trackside engineers to factory-based designers, have secure access to the same up-to-date data. This would break down information silos and allow for seamless collaboration between different departments, providing a single source of truth for all car-related data. The partnership would essentially create an end-to-end data pipeline from the sensor on the car to the decision-making dashboard in the garage or at the factory.
Balance and equilibrium
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