YUANSUAN | AI-NATIVE ENGINEERING PLATFORM

Engineering AI makes complexity computable.

Model real-world systems, compute every critical step, and make smarter engineering decisions.

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WHY YUANSUAN

One AI-Native Platform

Unifies simulation, compute, data, and traceability

Three Products, One Workflow

Matches the interface to the engineering task

Built for Measurable Results

Explore more, validate faster, and reuse what works

141Granted invention patents

10+High-value engineering use cases validated

26Provinces and municipalities served in China

2016Founded by engineers for engineers

01|WHAT IS ENGINEERING AI

Engineering AI connects the full problem-solving workflow

Make problems executable, models integrated, solutions optimized, conclusions trustworthy, and learning continuous.

HOW TO READEach column tracks one step; select any column to lock itPILLS = STEP OUTPUTSALLComparing STEP 01Define the ProblemComparing STEP 02Build the ModelComparing STEP 03Explore SolutionsComparing STEP 04Validate the ResultComparing STEP 05Accumulate Experience

ENHANCEMENT VS. HUMAN BASELINE0No added support1Task-level support2Strong support3Workflow-wide support

ENGINEERING PROBLEM-SOLVING STEPS

BASELINEEngineer

Five-step workflow · owns engineering judgment

STEP 01

Define the Problem

System, conditions, goals, and constraints

Problem DefinitionSTEP 02

Build the Model

Turn the real system into a model

Engineering ModelSTEP 03

Explore Solutions

Compare and optimize candidates

Candidate SolutionsSTEP 04

Validate the Result

Verify results and form evidence

Engineering EvidenceSTEP 05

Accumulate Experience

Retain patterns, methods, and applicability limits

Engineering Experience

01Traditional CAE

ComputePoint Solution

Physics-based modeling and numerical solving

Simulation Definition

Engineers set objectives, load cases, and constraintsSimulation Brief

Physics Modeling

Geometry, materials, mesh, and boundaries are defined explicitlySimulation Model

Case Comparison

A finite set of predefined cases is solved and comparedComparison Results

Result Check

Convergence and physical plausibility still need expert reviewCheck Results

Project Archive

Models, results, and reports are stored by projectProject Files

02General-Purpose AI

Data IntelligenceLocal Intelligence

Data modeling and content generation

Problem Structuring

Structures the problem and relevant contextProblem Summary

Data Modeling

Learns patterns from data without physics constraintsData Model

Concept Generation

Generates concept options without engineering convergenceConcept Options

Result Interpretation

Explains existing results without producing validation evidenceInterpretation Report

Knowledge Synthesis

Synthesizes existing knowledge without applicability limitsKnowledge Entries

03Engineering AI

Full-loopEnd-to-end Loop

Engineering intelligence connects all five steps

Intelligent Framing

Executable, computable, testable tasksExecutable Task

Integrated Modeling

Broader scope with accuracy, speed, and cost balancedIntegrated Model

Design Space Exploration

Broader search, faster convergence, better solutionsOptimized Solution

Trusted Validation

Reviewable, traceable, trusted conclusionsTrusted Conclusion

System Enhancement

Assets become retainable, reusable, and continuously improvableEngineering Assets

ModelsRulesWorkflowsLearningConsolidate into engineering assetsFeed the next intelligent framing cycle

ENGINEERING PROBLEM-SOLVING STEPS

BASELINEThe engineer

Defines the goal and owns engineering judgment

0—3 ENHANCEMENTPILLS = STEP OUTPUTS

TECHNOLOGY ENHANCEMENT

STEP 01STEP 01 / 05

Define the Problem

System, conditions, goals, and constraints

Problem Definition

Traditional CAEComputePoint Solution

Simulation DefinitionEngineers set objectives, load cases, and constraintsSimulation Brief

General-Purpose AIData IntelligenceLocal Intelligence

Problem StructuringStructures the problem and relevant contextProblem Summary

Engineering AIFull-loopEnd-to-end Loop

Intelligent FramingExecutable, computable, testable tasksExecutable Task

STEP 02STEP 02 / 05

Build the Model

Turn the real system into a model

Engineering Model

Traditional CAEComputePoint Solution

Physics ModelingGeometry, materials, mesh, and boundaries are defined explicitlySimulation Model

General-Purpose AIData IntelligenceLocal Intelligence

Data ModelingLearns patterns from data without physics constraintsData Model

Engineering AIFull-loopEnd-to-end Loop

Integrated ModelingBroader scope with accuracy, speed, and cost balancedIntegrated Model

STEP 03STEP 03 / 05

Explore Solutions

Compare and optimize candidates

Candidate Solutions

Traditional CAEComputePoint Solution

Case ComparisonA finite set of predefined cases is solved and comparedComparison Results

General-Purpose AIData IntelligenceLocal Intelligence

Concept GenerationGenerates concept options without engineering convergenceConcept Options

Engineering AIFull-loopEnd-to-end Loop

Design Space ExplorationBroader search, faster convergence, better solutionsOptimized Solution

STEP 04STEP 04 / 05

Validate the Result

Verify results and form evidence

Engineering Evidence

Traditional CAEComputePoint Solution

Result CheckConvergence and physical plausibility still need expert reviewCheck Results

General-Purpose AIData IntelligenceLocal Intelligence

Result InterpretationExplains existing results without producing validation evidenceInterpretation Report

Engineering AIFull-loopEnd-to-end Loop

Trusted ValidationReviewable, traceable, trusted conclusionsTrusted Conclusion

STEP 05STEP 05 / 05

Accumulate Experience

Retain patterns, methods, and applicability limits

Engineering Experience

Traditional CAEComputePoint Solution

Project ArchiveModels, results, and reports are stored by projectProject Files

General-Purpose AIData IntelligenceLocal Intelligence

Knowledge SynthesisSynthesizes existing knowledge without applicability limitsKnowledge Entries

Engineering AIFull-loopEnd-to-end Loop

System EnhancementAssets become retainable, reusable, and continuously improvableEngineering AssetsModelsRulesWorkflowsLearningConsolidate into engineering assetsFeed the next intelligent framing cycle

02|ENGINEERING AI PLATFORM

One platform for the full engineering workflow

Modeling creates computable modelsExploration finds better solutionsEngineering agents plan and coordinate workGovernance keeps every result reviewable

AI-NATIVE ENGINEERING PLATFORM

AI-Native Engineering Platform

Connect tasks, data, models, compute, and validation while engineers stay in control.

SYS 01/Integrated Modeling

Integrated Modeling

INTEGRATED MODELING

Fuse objects, physics, data, and constraints into computable models

STEP 02Build the ModelSTEP 04Validate the Result

Core technologyHybrid Engineering Solving EngineHYBRID ENGINEERING SOLVING ENGINE

ModelSolveCheckValidate

SYS 02/Design Space Exploration

Design Space Exploration

DESIGN SPACE EXPLORATION

Explore the engineering space and compare candidates to find better solutions

STEP 03Explore OptionsSTEP 04Validate the Result

Core technologyScalable Design Exploration EngineSCALABLE DESIGN EXPLORATION ENGINE

OrchestrateScheduleExecuteGovern

SYS 03/Engineering Agents

Engineering Agents

ENGINEERING AGENTS

Understand engineering problems, organize context, and plan executable tasks

STEP 01Define the ProblemSTEP 05Build Reusable Capability

Core technologyEngineering Models & AgentsENGINEERING MODELS & AGENTS

UnderstandReasonPlanLearn

TRUST & GOVERNANCE

Governance, Validation & Traceability

Controlled workflows. Review-ready results.

CONTROL PLANEThe three systems work together while the control plane sets boundaries, governs execution, and validates results.Spans all five steps

BOUNDARY CONTROLModel Limits

PROCESS OVERSIGHTControlled Runs

VALIDATIONV&VCredibility

EVIDENCE TRACEEvidence

Go Deeper into the Platform

See how modeling, design space exploration, engineering agents, and governance work together.

Explore the Platform

03|CHOOSE YOUR WORKFLOW

Choose the right interface for the work

Use GEWU to explore, LUBAN to deploy proven methods, and MOZI to coordinate complex work.

GEWUENGINEERING WORKSPACE

Engineering Workspace

Expert Control

Take direct control to explore and validate engineering problems

For CAE engineers who need full context and control to explore designs and validate conclusions.

Control models, parameters, and solver execution

Open GEWU

LUBANENGINEERING APP PLATFORM

Engineering App Platform

Standardized Execution

Package proven methods and run engineering tasks consistently

For method owners and R&D teams deploying validated workflows as governed Engineering Apps.

Turn proven methods into reusable tasks

Open LUBAN

MOZIENGINEERING TASK AGENT

Engineering Task Agent

Intelligent Execution

Delegate engineering goals and move tasks forward intelligently

For engineering leaders who need goals turned into coordinated work with a complete execution record.

Start with the goal, then plan, coordinate, and execute

Explore MOZI

LEVEL OF CONTROL

01GEWU controls the process

02LUBAN governs parameters and workflows

03MOZI works to the goal

Explore All Products

Products are the point of entry. Value is created when capability enters the workflow

04|BUSINESS OUTCOMES

Improve decisions across design, validation, and operations

Validate faster, explore before design freeze, predict risk earlier, and compare decisions before acting.

Explore Solutions

R&D01 — 02

OPERATIONS03 — 04

DESIGN BEFORE FREEZE

Design Before Freeze

Explore earlier. Learn faster at lower cost.

Explore, compare, and validate more options before design freeze to reduce late changes and rework

TYPICAL OUTCOME

More options compared and validated before design freeze

VERIFY WITH CONFIDENCE

Verify with Confidence

Validate faster. Decide with confidence.

Unify models, analysis, tests, and runtime evidence in one traceable validation loop

TYPICAL OUTCOME

Faster validation with reproducible, reviewable conclusions

PREDICT BEFORE FAILURE

Predict Before Failure

Detect earlier. Act with more lead time.

Combine operating data and engineering models to identify trends, risks, and likely causes earlier

TYPICAL OUTCOME

Risk identified and localized before failure

DECIDE WITH SIMULATION

Decide with Simulation

Simulate more options. Choose the better action.

Simulate and compare candidate actions across complex constraints, multiple objectives, and uncertainty

TYPICAL OUTCOME

Critical actions simulated, checked, and compared before execution

05|CUSTOMER RESULTS & REUSABLE WORKFLOWS

Validated in real work. Reused on the next project.

Prove each workflow on a real task, retain the evidence, and reuse the validated method.

Automotive & TransportationFeatured PracticeAnonymized Practice

FEATURED PROOF

01 / FEATURED PROOF

Trusted 13° Wheel-Impact Validation

ENGINEERING BOTTLENECK

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