Cognitive Mobility Platform: From Data to Decision

 

The Cognitive Mobility Platform: From Data to Decision

Our solution, powered by Equitus.us PowerGraph (KGNN) on IBM Power 10/11, MMA, Spyre, and Kubernetes, is a Cognitive Mobility Platform. It transforms the chaotic volume of unstructured automotive data into intelligent, actionable insights. It's not just a system upgrade; it's an organizational shift.

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I. Organizational Transition: Embracing the AI-Powered Enterprise

This approach focuses on moving your organization from reactive data processing to proactive, cognitive decision-making—a seamless evolution to AI-AIX (AI-Powered Infrastructure).

• 📈 Future-Proofing with Hybrid Cloud: Leverage the IBM Power 10/11 architecture, optimized for massive data and AI workloads, integrated with Kubernetes for scalable, portable, and future-proof deployment across on-premise, edge, and hybrid cloud environments.

• 🌉 Bridging the Data Gap: The PowerGraph's KGNN (Knowledge Graph Neural Network) model instantly connects fragmented information—from raw Data (unstructured) to organized Nodes (structured)—to create a unified, intelligent view of your entire operation.

• 🔄 Phased Integration: We provide a clear transition roadmap, utilizing the normalize, visualize, and iterate functions to prove immediate, localized value before scaling organization-wide. This minimizes disruption and maximizes adoption.

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II. Empowering Teams: The Agent-Driven Workflow

The platform provides your teams with intelligent tools that automate complexity, allowing them to focus on innovation and strategic action. This elevates the role of every team member.

• 🧠 Intelligent Automation with Vectors (Agents): The platform doesn't just store data; it activates it. Vectors (Agents) are autonomous, AI-driven components that execute complex tasks, such as preemptive maintenance scheduling or personalized driver experiences, freeing up engineering and operations teams.

• 👁️ Data Visualization for All: The visualize function delivers complex graph-based insights into intuitive dashboards, democratizing AI. Maintenance, product development, and fleet management teams can instantly see correlations and predicted outcomes that were previously hidden.

• 🛠️ Agile Development & Iteration: The iterate function fosters a culture of continuous improvement. Development teams can rapidly prototype new services and features using real-time insights from the Deltas (uses), drastically cutting time-to-market for new automotive apps and systems.

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III. Quantifiable Value: Delivering ROI at Every Turn

The ultimate marketing objective is to demonstrate clear, measurable business value across key operational domains.

• 💰 Optimized Resource Allocation: By using the Deltas (uses) stream to analyze actual system utilization, enterprises can right-size their infrastructure investments, reduce wasted computational power, and significantly lower total cost of ownership (TCO) on the IBM Power architecture.

• 🛡️ Enhanced System Reliability: The KGNN's predictive capability identifies subtle, non-obvious relationships between component failures, allowing for predictive maintenance that increases vehicle uptime and reduces warranty claims.

• ⭐ Accelerated Innovation: By providing a structured, agent-ready data environment, the platform accelerates the deployment of new AI applications (e.g., driver behavior analysis, infotainment personalization) through Spyre and MMA integration, leading to new revenue streams and competitive advantage.

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Would you like to focus on a specific industry vertical, like automotive manufacturing or fleet management, to refine the value propositions further?

CFR

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Proposal: An enhanced partnership creating a Cadillac Ferrari Racing, with IBM Technology Partner Equitus.us producing focus on AI-ready data, Knowledge Graph Neural Network (KGNN) platform, and intelligent video analytics (EVS), could offer significant support to Ferrari and Cadillac across their Formula 1 (F1) and World Endurance Championship (WEC) efforts. Equitus has a history of working with racing teams, notably Scuderia Cameron Glickenhaus (SCG), demonstrating their applicability in motorsports.

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...

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🏎️ 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.

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🏁 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.

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🚚 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?

cadillac ferrari

 

RaceCar/Track/Driver (RTD) program

 

Advanced Racing.AI: combines several cutting-edge technologies to enhance auto racing performance across Formula 1 (F1), World Endurance Championship (WEC), and IndyCar series.

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The RTD platform integrates Equitus.ai's Knowledge Graph Neural Network (KGNN) with IBM Power10 servers and their Matrix Math Accelerator (MMA) capabilities, along with cloud services and racing-specific technologies like Motec, Bosch, and Computational Fluid Dynamics (CFD)[1][3].

The RaceCar/Track/Driver (RTD) program within Advanced Racing.AI leverages these technologies to improve racing performance in the following ways:

Advanced Racing.AI is a platform that integrates several cutting-edge technologies to enhance performance in F1, WEC, and IndyCar racing through its RaceCar/Track/Driver (RTD) program. Here's how it combines various components to improve auto racing:

1. Equitus.ai KGNN and IBM Power10 Servers:

Equitus Corporation is leveraging IBM's latest Power10 servers to revolutionize AI inferencing at the edge[3]. This partnership allows for powerful data processing and analysis capabilities, crucial for real-time decision-making in racing scenarios.

2. Matrix Math Accelerator (MMA):

The MMA technology in IBM Power10 servers accelerates complex mathematical computations, which is essential for processing large amounts of racing data quickly and efficiently.

3. Cloud Services:

Cloud integration enables remote access and analysis of data, facilitating collaboration between trackside personnel and off-site engineers.

4. Motec and Bosch Systems:

These systems provide real-time telemetry and sensor data from the race car, offering crucial information about vehicle performance and driver inputs.

5. Atlas.ti:

While not specifically mentioned in the search results, Atlas.ti could be used for qualitative data analysis, potentially helping teams analyze non-numerical data such as driver feedback or race strategies.

6. Computational Fluid Dynamics (CFD):

CFD simulations run on the powerful IBM Power10 servers to optimize aerodynamic performance and vehicle dynamics.

The RTD program likely integrates these technologies to:

1. Analyze real-time and historical race data to optimize car setup and race strategy.

2. Predict and prevent potential mechanical issues through predictive maintenance.

3. Enhance driver performance through AI-driven analysis of driving techniques and racing lines.

4. Improve aerodynamics and overall vehicle performance through advanced simulations.

5. Optimize pit stop timing and race tactics based on real-time race conditions and competitor data.

While the specific details of Advanced Racing.AI are not provided in the search results, the combination of these technologies creates a comprehensive platform for improving various aspects of auto racing performance. The integration of high-performance computing, AI, and specialized racing technologies allows teams to make data-driven decisions and gain a competitive edge in F1, WEC, and IndyCar racing.

Citations:

[1] https://www.airacingtech.com

[2] https://www.code19.ai

[3] https://www.linkedin.com/posts/equitus_equitus-corporation-and-glickenhaus-racing-activity-7032057358306439168-Qsfz

[4] https://www.formulaedge.org

[5] https://forum.unity.com/register/genesis?error=login_required&state=JunIsPgHYQ27xLVQLdidPIFGMFh9SN5c46L37IMj%3B%2Fthreads%2Fwhich-ai-method-is-used-in-advanced-racing-games.541281%2F