YUANSUAN | ENGINEERING AI

Engineering AIBuilt for Real-World Engineering

One platform brings engineering computation, scaled execution, and engineering intelligence together—turning complex work into runnable, verifiable, reusable capability

Explore the System Start with a Real Problem

THE YUANSUAN SYSTEM

01One Engineering AI Platform

Engineering computation, scaled execution, and engineering intelligence

02Three Ways In

Expert control, governed apps, and intelligent execution

03Four Value Paths

Verify, design, predict, and decide

129Granted invention patents

10+High-value engineering scenario types validated

26Provincial-level regions

NationalSpecialized “Little Giant”

01｜DEFINING ENGINEERING AI

Engineering AI is the system layer for real-world engineering

Physical AI meets the real world. Engineering AI makes that intelligence runnable inside the constraints of engineering work

THE REALITY OF ENGINEERING

Complex systems

Multiscale, multi-component, interconnected

Complex physics

Multiple regimes, operating conditions, uncertainty

Complex workflows

Tools, roles, handoffs, and lifecycle stages

Complex validation

Review, traceability, and engineering evidence

SYSTEM LAYER

ENGINEERING AI

Builds and runs work within engineering constraints

01Computable

02Runnable

03Verifiable

04Reusable

COMPOUNDING CAPABILITY

From one answer to capability that keeps running

Tasks keep runningInvoke the right capability when work calls for it

Evidence keeps accumulatingRetain context, results, and decision rationale

Capability keeps compoundingGovern access, versions, and reuse

Real-world complexity × system-level execution = durable engineering capabilitySee How Yuansuan Builds Engineering AI

02｜THE YUANSUAN ENGINEERING AI SYSTEM

One system that brings Engineering AI into real work

The platform creates capability. Products put it in the hands of users. Solutions turn it into deliverable engineering work

Platform creates capability → Products put it to work → Solutions compose it → Engineering proves it

Real-World Engineering

Run in workflows and acceptance gates

Industry Workflows

Real Tasks

Acceptance Evidence

Four Value Paths

Compose capability for outcomes

Verify with Confidence

Design Before Freeze

Predict Before Failure

Decide with Simulation

Three Product Interfaces

Enter at the right control level

GEWU | Expert Control

LUBAN | Governed Apps

MOZI | Intelligent Execution

Six Core Capabilities

Connects platform and tasks

Workflow Coordination

Trusted Solving & Validation

Reusable Method Packaging

Intelligent Solution Generation

Engineering State Prediction

Autonomous Engineering Decisions

Engineering AI Platform

Creates the capability foundation

Engineering Computation

Scaled Execution

Engineering Intelligence

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View the Product System Browse Solutions

03｜PRODUCT ENTRY BY TASK

Choose the right interface for the task at hand

Use GEWU for expert exploration, LUBAN for governed Engineering Apps, and MOZI for intelligent task execution

GEWUENGINEERING WORKSPACE

Engineering Workspace

Expert Control

Explore and validate engineering problems

For CAE engineers who need full engineering context to explore designs, converge on answers, and validate boundaries

Control models, parameters, and the solve

Open GEWU

LUBANENGINEERING APP PLATFORM

Engineering App Platform

Governed Invocation

Build and run Engineering Apps

For method owners and R&D teams turning workflows, rules, and templates into governed Engineering Apps

Turn a proven method into reusable work

Open LUBAN

MOZIENGINEERING TASK AGENT

Engineering Task Agent

Intelligent Execution

Delegate and execute engineering work

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

Start with the goal; plan, invoke, and execute

Explore MOZI

LEVEL OF CONTROL

01GEWU controls the process

02LUBAN governs parameters and workflows

03MOZI works to the goal

View the Product System

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

04｜ENGINEERING AI VALUE SYSTEM

Four ways Engineering AI creates value across the lifecycle

From R&D to operations, Engineering AI drives four outcomes: verify with confidence, design before freeze, predict before failure, and decide with simulation

Explore Engineering AI Solutions

R&D01 — 02

OPERATIONS03 — 04

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

DESIGN BEFORE FREEZE

Design Before Freeze

Explore earlier. Learn faster at lower cost.

Bring computation into concept development and compare more options before design freeze

TYPICAL OUTCOME

More options compared and validated before design freeze

PREDICT BEFORE FAILURE

Predict Before Failure

Detect earlier. Act with more lead time.

Combine operating data and engineering models to forecast trends, identify risk, and intervene 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｜REAL-WORLD PROOF AND CAPABILITY CAPTURE

Prove it in real engineering. Keep what works.

Engineering AI earns trust through runnable tasks, review-ready evidence, and capability that can be reused—not through demos alone

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