Building Domain-Specific AI for Automotive

GPT-4 doesn't know the difference between a PLC fault code and a robot teach pendant error. It doesn't understand that "tolerance" means something different in machining versus paint versus body shop. This is why generic AI fails in automotive manufacturing.

The Domain Knowledge Problem

Automotive manufacturing is not one domain - it's dozens of specialized disciplines, each with their own:

• Terminology and jargon
• Document types and formats
• Equipment and systems
• Failure modes and troubleshooting patterns
• Tribal knowledge

A controls engineer asking about "interlocks" needs completely different context than a paint engineer asking about "interlocks." Same word, different domains, different answers.

Our Domain Model Architecture

flowchart TB
    Query[Engineer Query] --> Router[Domain Router]
    
    Router --> Controls[Controls Expert]
    Router --> Robotics[Robotics Expert]
    Router --> Vision[Vision Expert]
    Router --> Paint[Paint Expert]
    Router --> Body[Body Expert]
    Router --> Assembly[Assembly Expert]
    Router --> Powertrain[Powertrain Expert]
    
    Controls --> ControlsKB[(PLC Docs, Fault Codes)]
    Robotics --> RobotKB[(Robot Manuals, Programs)]
    Vision --> VisionKB[(Camera Docs, Inspection)]
    Paint --> PaintKB[(Process Params, Defects)]
    Body --> BodyKB[(Weld Schedules, Dimensional)]
    Assembly --> AssemblyKB[(Torque Specs, Procedures)]
    Powertrain --> PowertrainKB[(Machining, Test Data)]
                

Each domain expert is a combination of:

1. Domain-specific vector index - Documents relevant to that discipline
2. Fine-tuned LoRA adapter - Trained on domain Q&A pairs
3. Custom prompting - Response formats appropriate to the domain
4. Confidence thresholds - Tuned to domain risk levels

Domain Deep Dives

Controls & PLC

The controls domain requires understanding of:

• Allen-Bradley, Siemens, Mitsubishi PLC architectures
• Ladder logic, function blocks, structured text
• Fault code interpretation and troubleshooting
• I/O addressing and tag structures
• HMI alarm rationalization

Our controls adapter was trained on:

• 1,200+ fault code explanations
• 500+ troubleshooting conversations
• PLC programming examples and explanations
• Equipment manuals for common controllers

Robotics

Industrial robotics involves:

• FANUC, ABB, KUKA, Yaskawa programming languages
• Motion control and path optimization
• Safety system integration
• End-of-arm tooling configuration
• Vision-guided robotics

A robotics question like "How do I implement a pallet pattern pick?" requires understanding of specific robot syntax, motion types, and register usage that generic models simply don't have.

Paint Shop

Paint is one of the most complex domains:

• E-coat, primer, basecoat, clearcoat processes
• Bell applicator parameters (speed, shaping air, voltage)
• Booth environmental controls
• Defect classification and root cause analysis
• Color matching and blend-out procedures

When a paint engineer asks "Why are we seeing orange peel on horizontal surfaces?", the model needs to understand the relationship between bell parameters, film build, flash times, and booth conditions. Generic models give generic answers. Our paint expert asks the right follow-up questions.

Body Shop

Welding and body assembly requires:

• Resistance welding schedules and parameters
• Electrode management and tip dress frequency
• Dimensional control and fixture adjustment
• Mixed-material joining techniques
• Weld quality analysis

Final Assembly

Assembly operations involve:

• Torque specifications and DC tool programming
• Fluid fill procedures and leak detection
• Electrical system verification
• Error-proofing and vision confirmation
• Kitting and sequence management

Training Domain Adapters

Each domain adapter is trained using LoRA on a base model. The training data includes:

Real-World Impact

At one automotive OEM, we deployed domain-specific models across six manufacturing areas:

• 73% reduction in time engineers spend searching documentation
• 4x faster first-touch resolution for maintenance issues
• 89% accuracy on domain-specific technical questions
• Captured tribal knowledge from retiring experts

The key insight: automotive AI isn't about having a better model. It's about having models that understand your specific operations.