YUANSUAN | ENGINEERING AI
One platform brings integrated modeling, full-domain exploration, and engineering intelligence together—turning complex work into runnable, verifiable, reusable capability
Creates the compute and intelligence foundation
Enters work at the right control level
Creates value in verification, design, prediction, and decisions
It connects understanding, modeling, exploration, validation, and learning into a continuously improving engineering loop
System, conditions, goals, and constraints
Turn the real system into a model
Compare and optimize candidates
Verify results and form evidence
Retain patterns, methods, and boundaries
Physics modeling and single-task compute
Language, knowledge, and content generation
Connects the full engineering loop
FIVE STEPS IN ENGINEERING PROBLEM-SOLVING
Defines the goal and owns engineering judgment
The engineer stays in control. Engineering AI connects all five steps.
Modeling creates computable modelsExploration finds better solutionsIntelligence frames problems and captures learningEngineering Trust spans the full process
UNIFIED ENGINEERING AI PLATFORM
An engineer-led platform connecting tasks, data, models, knowledge, workflows, runtime environments, and evidence
INTEGRATED MODELING SYSTEM
Fuse objects, physics, data, and constraints into computable models
FULL-DOMAIN EXPLORATION SYSTEM
Explore the engineering space and compare candidates to find better solutions
ENGINEERING INTELLIGENCE SYSTEM
Understand engineering problems, organize context, and plan executable tasks
ENGINEERING TRUST
Bounded process. Evidenced results.
Explore the Technology Platform
See how the three systems create models, find solutions, and execute work.
03｜PRODUCT ENTRY BY TASK
Use GEWU for expert exploration, LUBAN for governed Engineering Apps, and MOZI for intelligent task execution
For CAE engineers who need full engineering context to explore designs, converge on answers, and validate boundaries
Control models, parameters, and the solve
For method owners and R&D teams turning workflows, rules, and templates into governed Engineering Apps
Turn a proven method into reusable work
For engineering leaders who need goals translated into coordinated work with a complete execution record
Start with the goal; plan, invoke, and execute
04｜ENGINEERING AI VALUE SYSTEM
From R&D to operations, Engineering AI drives four outcomes: verify with confidence, design before freeze, predict before failure, and decide with simulation
Explore, compare, and validate more options before design freeze to reduce late changes and rework
More options compared and validated before design freeze
Unify models, analysis, tests, and runtime evidence in one traceable validation loop
Faster validation with reproducible, reviewable conclusions
Combine operating data and engineering models to identify trends, risks, and likely causes earlier
Risk identified and localized before failure
Simulate and compare candidate actions across complex constraints, multiple objectives, and uncertainty
Critical actions simulated, checked, and compared before execution
05｜REAL-WORLD PROOF AND CAPABILITY CAPTURE
Engineering AI earns trust through runnable tasks, review-ready evidence, and capability that can be reused—not through demos alone
Wheel impact, fatigue, and lightweight validation relies heavily on expert experience and physical testing, while test-to-digital-validation correlation and reporting criteria remain difficult to standardize
13° workflow → standard task
Boundaries and criteria → test correlation
Runs and reports → review-ready evidence
Proven method → task family
06｜ENGINEERING AI PILOT
Choose one bounded, valuable task with clear acceptance criteria. Use the first pilot to prove Engineering AI in your environment
