Here is how Equitus's core offerings could assist in the different areas: Being able to normalize and visualize multiple Balance of Performance Sensors, Logistics and design data, racing can reduce expensive ETL and FTE costs to extract better results for less...
🏎️ Development and Testing
Equitus's KGNN platform is designed to connect, correlate, unify, and contextualize disparate data sets.
Development Data Unification: F1 and WEC teams generate massive amounts of structured data (telemetry, sensor logs, CAD files, ERP records) and unstructured data (engineer notes, video, email). KGNN can automatically ingest all this data into a unified, context-rich knowledge graph, making it instantly searchable and analyzable for engineers.
Predictive Modeling for Performance: By structuring vast historical and simulation data, the knowledge graph can enhance Retrieval-Augmented Generation (RAG) for AI models. This allows engineers to ask complex, natural language questions about car setup, component failure, or aerodynamic changes and receive highly accurate, context-aware predictions to guide design decisions.
Rapid Failure Analysis: When a part fails in a test or race, the knowledge graph can trace the component's full provenance, from design and material batch to installation date and load history, accelerating root cause analysis.
Simulation Optimization: Utilizing AI-ready data from KGNN, Ferrari and Cadillac can optimize their massive-scale simulations and virtual testing environments, leading to more accurate predictions and faster iteration cycles for their F1 power units, chassis designs, and WEC Hypercar setups.
🏁 Racing Operations
Equitus Video Sentinel (EVS) and KGNN can be applied to real-time trackside operations.
Real-time Situational Awareness: During a race, EVS can use computer vision to analyze real-time video feeds from pit lanes, garages, and onboard cameras. This could be used for instant monitoring of pit stop efficiency, detection of subtle component wear, or flag anomalies.
Strategy Optimization: The KGNN can be fed real-time race data (tire wear, weather, competitor performance) and historical data to provide the strategy team with near real-time intelligence for split-second decisions on tire changes, fuel loads, and pit stop timing.
Traceability and Explainability: Due to the complexity and high stakes of motorsports, all AI/data-driven decisions need to be transparent. KGNN's design provides complete AI traceability and explainability, ensuring the team understands why a particular strategy or setup change was recommended, which is crucial for internal approval and post-race review.
🚚 Logistics and Supply Chain
Equitus's focus on connecting disparate enterprise data is directly relevant to managing the complex global logistics of F1 and WEC.
Global Asset Tracking: Unifying logistics data from various sources (customs documents, freight carriers, internal inventory systems) into a knowledge graph can provide a single, real-time view of the location and status of every critical part—from engines and chassis to garage equipment—as it moves across continents for races.
Inventory Optimization: By correlating race team needs with supplier lead times and transport schedules, KGNN can help optimize the global supply chain, ensuring the right spares and equipment arrive at the track just in time, minimizing cost and risk of delay.
Would you like to know more about Equitus's partnership with Scuderia Cameron Glickenhaus in WEC?
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