The Auto Repair sector in the DecisioQ Decision Catalog is identified by AUTO-REPR. It currently covers 73 decision types across service operations, diagnosis and estimation, technician and bay allocation, parts procurement, warranty, compliance, quality assurance, pricing, customer communication, digital operations, and business performance.

DecisioQ integrations should load sector, category, decision, Decision Preparation Model, scenario, criterion, and decision overview metadata from the Decision Catalog. Do not hard-code old static decision lists or legacy request models.

DecisioQ should not replace shop management systems, estimating systems, warranty adjudication rules, payment systems, or legally required compliance checks. It should support explainable business decisions around ranking, routing, prioritization, exception handling, and audit-ready recommendations.

Auto repair client applications should use the same discovery flow as the Business Decision Studio:

1. Load GET /decisioncatalog through the approved server-side Decision Catalog gateway.
2. Populate Sector from catalog.sectors[] and select AUTO-REPR for Auto Repair.
3. Populate Category from selectedSector.categories[]. Example categories include Service Operations, Diagnosis & Estimation, Resource Allocation, Parts & Procurement, Warranty & Compliance, Quality Assurance, Customer Communication, Shop Operations, and Business Performance.
4. Populate Decision from selectedCategory.decisions[] when available, or call /decisioncatalog/sectors/{sectorCode}/categories/{categoryCode}/decisions.
5. Display Decision Name only in the dropdown while keeping decisionId as the option value.
6. Load full decision detail from /decisioncatalog/decisions/{decisionId} before rendering Guided Data Collection.

For regional deployments, keep DecisioQ technical identifiers stable and localize only user-facing labels and guidance. Unless the selected endpoint and decision schema explicitly support unit-qualified input, convert mileage, distance, pressure, temperature, mass, volume, speed, fuel consumption, and other measurements to the required canonical units before submission. Do not infer units or currency from locale.

Auto repair integrations should map repair order, estimate, vehicle, technician, bay, parts, warranty, customer, quality, and scheduling data into decision-specific Business Data. The selected decision detail defines which criteria are required and how each value should be measured.

Do not invent criterion IDs. Use the canonical criteria returned by /decisioncatalog/decisions/{decisionId}, including criterion overview metadata, unit, direction, scale, and data guidance.

1. Authenticate through the approved server-side identity flow. Browser pages should not directly handle credentials.
2. Load the Decision Catalog and let the user choose Sector, Category, Decision, Decision Preparation Model, and Scenario.
3. Load decision detail and render Guided Data Collection from returned criteria.
4. User or system prepares Business Data for candidate repair orders, technicians, suppliers, estimate actions, loaner options, quality actions, or shop improvement initiatives.
5. Call POST /api/v1/validate to validate and prepare the decision input before execution.
6. Call POST /api/v1/decide to return the authoritative Decision Result.
7. Display recommendation, ranked alternatives, warnings, assumptions, and Explanation of Decision Result.
8. Keep execution trace and diagnostics collapsed by default for developer and integrator review.

The exact fields depend on the selected Auto Repair decision. The example below shows the integration style rather than a guaranteed schema for every decision.

{
  "decisionId": "AUTO-REPR-005",
  "decisionPreparationModelId": "AUTO-REPR-PROFILE-STANDARD",
  "scenarioId": "AUTO-REPR-SCENARIO-TECHNICIAN-ASSIGNMENT",
  "businessData": {
    "correlationId": "repair-dispatch-20260717-0915",
    "repairOrderId": "RO-184210",
    "vehicleVin": "1HGCM82633A004352",
    "options": [
      {
        "optionId": "TECH-ALEX-M",
        "label": "Assign Alex M.",
        "values": {
          "technicianReadiness": 92,
          "skillMatchScore": 96,
          "currentWorkloadScore": 74,
          "qualityHistoryScore": 88,
          "customerImpact": 79
        }
      },
      {
        "optionId": "TECH-JORDAN-K",
        "label": "Assign Jordan K.",
        "values": {
          "technicianReadiness": 86,
          "skillMatchScore": 89,
          "currentWorkloadScore": 91,
          "qualityHistoryScore": 82,
          "customerImpact": 75
        }
      }
    ]
  }
}

Call DecisioQ from a trusted backend service. Keep identity credentials, account verification, and service configuration outside public browser code.

async function executeAutoRepairDecision(jwtToken, preparedDecisionInput) {
  const response = await fetch(`${process.env.DECISIOQ_BASE_URL}/api/v1/decide`, {
    method: "POST",
    headers: {
      "Authorization": `Bearer ${jwtToken}`,
      "Content-Type": "application/json"
    },
    body: JSON.stringify(preparedDecisionInput)
  });

  const payload = await response.json();

  if (!response.ok) {
    throw new Error(payload.message || "Decision execution failed.");
  }

  return payload;
}

• Use a guided decision selector: Sector, Category, Decision, Decision Preparation Model, and Scenario.
• Show Selection Overview for sector, category, and decision metadata so business users understand the decision before entering data.
• Render Guided Data Collection from criterion definitions and show criterion overview summaries where available.
• Display Decision Name in selectors and show the selected decisionId separately for developers and integrators.
• For service advisor, dispatch, parts, quality, and manager workflows, keep screens focused on recommendation, reason, required action, warnings, and next step.
• Keep execution trace and diagnostics available to integrators but collapsed by default.
• Never expose internal proxy URLs, service credentials, signing keys, or account verification details in browser code.

• Confirm repair decisions support the right shop type, service line, labor model, warranty policy, parts strategy, and customer segment.
• Validate Business Data before execution and show user-friendly validation messages.
• Persist correlation ID, repair order ID, VIN, customer ID, technician ID, bay ID, supplier ID, decision ID, profile ID, scenario ID, selected option, score, and explanation for audit.
• Retain the successful decision response requestId with the host business transaction, then call POST /api/v1/decision-outcomes when the action and realized result are known.
• Require an overrideReason for overridden recommendations; never infer acceptance merely because a result was displayed.
• Monitor GET /api/v1/decision-outcomes/summary for acceptance rate, override rate, and realized-value aggregates. Treat outcome feedback as reporting evidence, not automatic model training.
• Keep safety, regulatory, warranty, payment, and authorization rules deterministic where required; use DecisioQ for multi-factor ranking, prioritization, exception, and recommendation decisions.
• Monitor preview failures, execution failures, account verification errors, timeout rates, and unexpected warning counts.
• Review Decision Preparation Models and scenario assumptions with service operations, dispatch, warranty, parts, quality, finance, and customer experience owners when policies change.