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About
Secondmind AI Optimize is an advanced machine learning platform purpose-built for the automotive engineering industry. As vehicle complexity grows due to tighter emissions regulations, evolving customer expectations, and sustainability goals, the platform helps engineering teams navigate massive datasets and accelerate development cycles from years to months. At the core of the platform is Secondmind Active Learning, a proprietary AI system that intelligently selects only the most critical data needed to meet an engineer's optimization objective. By combining a robust toolbox of purpose-built machine learning algorithms with automated experiment design, it dramatically reduces the need for expensive simulations and physical prototypes. Key product areas include Design Space Exploration, which enables engineers to discover sets of pre-validated system, subsystem, and component designs using up to 80% fewer simulations, and Calibration, which calibrates complex physical and virtual systems in half the typical time with fewer costly prototypes. The platform is cloud-native and designed with a user experience that respects engineering expertise, offering intuitive workflows for tackling the most complex vehicle design challenges. Trusted by leading OEMs such as General Motors, Secondmind helps engineering teams enhance exploration, reduce overhead, and achieve better outcomes across e-powertrain performance, system design, and beyond. It is ideal for automotive OEMs, Tier 1 suppliers, and engineering consultancies seeking to modernize their development processes with AI.
Key Features
- Secondmind Active Learning: Proprietary ML technology that intelligently selects only the most important data needed for optimization, combining purpose-built algorithms with automated experiment design.
- Design Space Exploration: Discover sets of pre-validated system, subsystem, and component designs using up to 80% fewer simulations compared to traditional approaches.
- Accelerated Calibration: Calibrate the performance of complex physical and virtual systems in half the time, with significantly fewer costly prototypes required.
- Cloud-Native Architecture: Fully cloud-native software that scales with engineering workloads and integrates into existing automotive development workflows.
- Data-Efficient Optimization: Reduces reliance on expensive simulations and physical prototypes, cutting development costs while maintaining quality and innovation standards.
Use Cases
- Automotive OEM engineering teams exploring design spaces for new vehicle systems with significantly fewer simulation runs
- E-powertrain engineers calibrating motor and battery systems to optimize performance and range while reducing prototype testing costs
- Tier 1 automotive suppliers accelerating component design validation to meet tightening OEM development timelines
- R&D teams working on emissions compliance needing to rapidly evaluate and validate system configurations against regulatory targets
- Vehicle dynamics engineers using ML-driven calibration to optimize handling and performance characteristics across complex multi-parameter systems
Pros
- Dramatic Simulation Reduction: Up to 80% fewer simulations required for design space exploration, saving significant time and computational costs.
- Proven Industry Adoption: Trusted by major OEMs including General Motors, demonstrating real-world effectiveness in complex automotive engineering contexts.
- Accelerated Development Cycles: Helps compress vehicle development timelines from years to months, enabling teams to respond faster to market and regulatory demands.
Cons
- Highly Specialized Domain: The platform is purpose-built for automotive engineering, making it unsuitable for teams outside the automotive or adjacent engineering industries.
- Enterprise Pricing: Targeted at OEMs and large engineering organizations, likely with enterprise-level pricing that may be inaccessible for smaller teams or startups.
- Limited Publicly Available Details: Specific pricing, integration documentation, and technical specs are not publicly disclosed, requiring direct contact with the sales team.
Frequently Asked Questions
What is Secondmind Active Learning?
Secondmind Active Learning is the proprietary machine learning technology at the heart of the platform. It intelligently selects only the most important data needed to meet an engineer's optimization objective by combining purpose-built ML algorithms with automated, data-efficient experiment design.
How much can Secondmind reduce simulation requirements?
Secondmind's Design Space Exploration product can help engineers discover pre-validated designs using up to 80% fewer simulations compared to conventional methods.
What types of automotive engineering tasks does Secondmind support?
Secondmind supports two primary workflows: Design Space Exploration for discovering optimal system and component designs, and Calibration for optimizing the performance of complex physical and virtual systems such as e-powertrains.
Is Secondmind suitable for electric vehicle development?
Yes. Secondmind has been highlighted by engineers at General Motors as particularly valuable for e-powertrain calibration, helping optimize performance and extend range for electric vehicles.
How is Secondmind deployed?
Secondmind is a cloud-native platform, meaning it is accessed via the web without requiring on-premises infrastructure installation, making it scalable and easy to integrate into existing engineering workflows.
