Five paths to AI in engineering
Start with a workshop if you are not sure where to begin, and training so your engineers can run and check what you adopt. Then automate the workflow, connect the data, and predict with surrogate models.
01AI Adoption Framework
Where teams start, and what we build
- 1Get started
Workshop
- Outcome
- A concrete deployment plan for one specific engineering problem.
- When
- You are not sure where to begin with AI.
- 2Get started
Training
- Outcome
- Engineers who can operate AI tools and catch an incorrect result.
- When
- You are adopting AI, whether we build it or you buy it from a vendor.
- 3Solution
Workflow automation
AgentCrew · Spectra · Iso
- Outcome
- Routine simulation and CAD steps run as one repeatable process.
- When
- Your engineers spend their days on setup, drafting and data transfer.
- 4Solution
Data management
Prism
- Outcome
- Legacy files and new runs become a searchable catalog you can question.
- When
- Your data is scattered across drives, workstations and business systems.
- 5Solution
Surrogate models
Sift
- Outcome
- Fast design iterations, or dedicated models for complex physics.
- When
- You have recurring product families or very large problems.
02Solutions
The three solutions
- Solution03
Agentic AI
Highly trained engineers spend their days on setup, drafting and moving data between CAD, meshing and solver tools. We turn those steps into a workflow that runs the same way every time, then put agents on the parts that need judgement.
- Solution04
Digital Thread
Engineering knowledge sits in drives, workstations, spreadsheets and PLM, ERP and test exports. We gather it into one catalog with the context of each run attached, and Prism lets every team ask it questions and check the answer.
- Solution05
Surrogate Modeling
Surrogate models compress the design cycle by orders of magnitude — simulation results in minutes instead of hours or weeks — and finally put your legacy simulation archives to work as training data.
03FAQ
Clearly answered
01What is the Curlscape AI Adoption Framework?
The Curlscape AI Adoption Framework is five ways to start with AI in engineering. Two get a team started: a workshop that produces a deployment plan for one specific problem, and training so engineers can run AI tools and catch an incorrect result. Three are solutions: agentic AI for workflow automation, a digital thread for data management, and surrogate models for fast design iteration.
See the framework02Do we have to start with a workshop?
No. Each path in the framework can be the first one, so a team that already knows its problem can go straight to agentic AI, data management or surrogate models. The workshop is for teams that are not yet sure where AI fits.
Schedule a call03How do agentic AI, the digital thread and surrogate models fit together?
They build on each other. Automating a workflow with agentic AI captures structured data from every run; the digital thread gathers that data, along with older files, into one searchable catalog; and surrogate models are trained on that catalog to predict new results in minutes. A team can adopt any one of them on its own.
Start with agentic AIHow we run this ourselves
- Lab01
Multi-fidelity optimization of a propeller design space
2,560 propeller designs screened with fast blade-element theory, then corrected with 12 blade-resolved CFD runs, instead of running CFD on every candidate.
- Lab02
Electronics-thermal surrogates: where to trust a fast model
A surrogate trained on high-fidelity thermal runs predicts component temperatures in milliseconds, with the trained envelope treated as part of the deliverable.
- Tool03
Simulation Cost & Surrogate ROI Calculator
Put a number on what your CAE/CFD pipeline costs today, and on the payback of a surrogate model, using your own figures.