OrbisFrameworkOrbisFramework

Case Study / Automotive

Diagnostic and repair decision support, in production in two weeks.

Time to production

2 weeks

Workflow stages

6

Decision gates

4

The Challenge

Automotive field technicians face complex multi-step diagnostics under time pressure. Vehicle data is scattered across multiple systems. Repair decisions carry safety and warranty consequences. A wrong diagnosis can mean safety risk for the driver or thousands of dollars in unnecessary repairs.

The client needed a decision support system that could synthesize data from multiple sources, apply diagnostic logic, and present technicians with actionable recommendations while keeping humans in control of final decisions.

Building this from scratch would require months of infrastructure work before any diagnostic value could be delivered. The business could not wait.

The Architecture

The diagnostic workflow runs on OrbisFramework infrastructure. Six workflow stages handle data collection, symptom analysis, diagnostic generation, repair recommendation, validation, and output delivery.

Workflow Stages

  1. 1
    Data Collection: Vehicle telemetry, service history, and symptom input aggregated
  2. 2
    Symptom Analysis: AI analysis of symptoms against known patterns
  3. 3
    Diagnostic Generation: Candidate diagnoses generated with confidence scores
  4. 4
    Repair Recommendation: Repair procedures matched to diagnoses with parts and labor estimates
  5. 5
    Technician Validation: Human review at decision gate for high-stakes recommendations
  6. 6
    Output Delivery: Approved recommendations delivered to service management system

OrbisFramework Pillars Used

  • AI Orchestration: Multi-step workflow with conditional logic
  • Live Data Integration: Vehicle systems, service databases, parts catalogs
  • Decision Gates: Human validation for safety-critical recommendations
  • Audit Trail: Complete record of every diagnosis for warranty and liability

The Result

Two weeks to production

From kickoff to live deployment with real technicians using the system

One developer

Single developer focused on workflow logic while infrastructure was inherited

Complete audit trail

Every diagnostic decision logged for warranty claims and quality review

Human-in-the-loop

Technicians remain in control of final decisions with AI support

Architecture in This Deployment

Components That Carried the Weight

  • 2
    Generator: Multiple diagnostic hypotheses produced for each symptom set, including rare fault candidates
  • 3
    Evaluation: Independent scoring against symptom patterns, vehicle history, and known failure modes
  • 4
    Gate: Three-route gate with accept (high confidence), return (insufficient data), and escalate (ambiguous or safety-critical)

Governing Edge

Edge of the standard: When symptoms do not cleanly match any common fault pattern, the system recognizes it may be facing an uncommon fault. The case escalates to senior technicians or engineering rather than projecting confidence on an uncertain diagnosis.

What Was Built on Top

With infrastructure handled by OrbisFramework, development focused on the diagnostic workflow configuration, technician interface screens, and the scoring criteria that determine when recommendations are ready for delivery versus when they need human review.

The workflow can be modified without code changes. New diagnostic patterns can be added. Scoring thresholds can be adjusted. The business iterates on the workflow while the infrastructure remains stable.

For the industry-specific context:

See Automotive Solutions

For the complete decision architecture:

See Decision Architecture

Your diagnostic workflow

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