AI & Data-Driven Engineering Services
Your Data. Our Physics. Real Results.
Engineering organizations often have years of valuable design knowledge stored across CFD analyses, FEA studies, test campaigns, and operational data, yet much of that engineering investment remains underutilized. Turning that data into reliable AI takes more than data science. It takes an understanding of the physics behind it, and the discipline to apply AI in a way that reflects real design constraints, operating conditions, and performance objectives.
SoftInWay combines physics-informed AI with more than 25 years of validated turbomachinery expertise to help organizations transform existing engineering knowledge into reliable, machine-specific AI models, developed for your machines, deployed within your environment, and owned by your organization.
There are two ways to get there. If you have a library of your own data, we can build AI models trained directly on it. If you don’t, we have pre-trained foundation models ready to use — and we can fine-tune it to your machines. The right starting point depends on what you already have
Build AI Models from Your Own Engineering Data
SoftInWay develops surrogate models and reduced-order models (ROMs) directly from your CFD, FEA, and test data to deliver rapid, reliable predictions.
These models can help your organization:
- Accelerate design space exploration
- Replace computationally intensive simulations with rapid predictions
- Support optimization workflows and digital twin development
- Interpret test data and evaluate design concepts earlier in the process
- Improve engineering productivity
- Extend the value of existing engineering knowledge
Because these models are physics-informed, they typically require less training data than purely statistical machine learning approaches while staying relevant to your application.
You can deploy models entirely on-premises or within your own secure cloud environment, including for ITAR and other export-controlled programs. The resulting models, workflows, and intellectual property remain fully yours.
Evaluate & Fine-Tune Foundation Models
Foundation models continue to evolve across engineering applications, but their suitability depends on your machines, objectives, and available data. SoftInWay helps organizations evaluate where foundation models can provide value and how they can best be applied within an engineering workflow.
Our team can help you:
- Fine-tune models using your own engineering data
- Deploy models entirely within your environment
- Develop machine-specific AI seeded with AxSTREAM’s validated turbomachinery physics
Our goal is to help your organization select the AI approach that best aligns with your engineering objectives, not simply adopt the latest technology.
Start with an AI Readiness Assessment
Not sure which approach fits your data and objectives? Every successful AI initiative starts with understanding where it can create the greatest value. Our 2-Week AI Readiness Assessment helps your organization identify practical AI opportunities before significant time or budget is invested.
- Define the engineering objectives, workflows, and challenges that AI could help address
- Review available CFD, FEA, test, operational, and other relevant engineering data
- Evaluate data quality, coverage, and readiness for AI development
- Identify and prioritize AI opportunities based on technical feasibility and potential engineering value
- Recommend the most appropriate approach based on your objectives and available data
- Outline the data, validation, deployment, and implementation requirements for the recommended path
- Deliver a practical roadmap that remains yours whether or not you continue with SoftInWay
The result is a clear, actionable plan that helps your organization move forward with confidence.
Why Organizations Partner with SoftInWay
For more than 25 years, SoftInWay has helped organizations solve complex engineering challenges in turbomachinery and thermal-fluid systems, combining deep domain knowledge with advanced modeling technologies.
At SoftInWay, we provide:
- Engineering context, not just AI expertise: Our team understands the physics, constraints, and design decisions behind the data used to build AI solutions.
- A practical path from engineering data to deployment: We help identify where AI can create measurable value, develop the right approach, and integrate solutions into existing workflows.
- Solutions tailored to your machines and objectives: Rather than relying on generic models, we develop approaches aligned with your specific equipment, operating conditions, and engineering goals.
- Secure development aligned with engineering requirements: Solutions can be deployed within your infrastructure while maintaining control of sensitive data, models, and workflows.
- Knowledge transfer to your engineering team: We work alongside your organization to ensure your team understands, adopts, and can continue using the resulting AI capabilities.
Whether you’re building surrogate models, evaluating foundation models, or defining a long-term AI strategy, our engineers work alongside your organization to develop solutions tailored to your machines, data, and engineering goals.
Frequently Asked Questions
How much data do I actually need?
Physics-informed AI often requires less training data than conventional machine learning because engineering knowledge helps guide the model. During the AI Readiness Assessment, our engineers evaluate your available data and recommend the most appropriate path forward.
Should I use a surrogate model, reduced-order model, or foundation model?
That depends on your objectives and the engineering data available. Surrogate models and reduced-order models are developed from your own engineering archives for your specific machines. Foundation models can provide a useful starting point for certain component-level applications but may not yet be appropriate for multistage machines, off-design operating conditions, or specialized working fluids. We’ll help determine the approach, or combination of approaches, that best supports your engineering objectives
Can AI solutions be deployed within our existing IT environment?
Yes. Projects can be completed entirely within your own infrastructure or controlled cloud environment, making them suitable for organizations with strict security, export-control, or IT requirements.
Who owns the models and intellectual property?
Your organization does. The trained models, workflows, training pipelines, and intellectual property developed during the engagement remain with your organization.
Put AI to Work for Your Engineering Organization
Whether you’re evaluating your first AI initiative or expanding existing capabilities, SoftInWay works alongside your organization to develop AI solutions grounded in validated engineering methods and tailored to your machines, your data, and your engineering objectives.
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