Overview

AUTO-AUCT-006, Select Auction House, ranks candidate auction houses by acquisition fit, cost control, vehicle quality, logistics, reputation, and title handling.

Decision ID

AUTO-AUCT-006

Decision Name

Select Auction House

Industry Profile

auto-auctions-select-auction-house-auto-auct-006 version 1.0.0

Default Profile

balanced

Default Scenario

standard

Catalog

DKR-AUTO-RUNTIME-001, version 13.9.3

Understanding This Decision

Select Auction House helps a buyer choose the auction source that best balances inventory access, fee discipline, expected vehicle quality, logistics, auction reputation, and title-processing reliability.

Architecture

1. Request a token from the Identity service.
2. Use the returned jwtToken as the bearer token.
3. Load the catalog and decision detail from DKS.
4. Build a Prepared Decision Input using canonical criterion IDs.
5. Execute the decision through DDE.
6. Use X-Request-Id and X-Correlation-Id for tracing across DDE and DKS.

Authentication

Request a secure session token from Identity. The current token response field is jwtToken.

POST https://identity.vinquery.com/connect/token
Content-Type: application/json

{
  "clientId": "{clientId}",
  "clientSecret": "{clientSecret}",
  "audience": "vinquery:api:decisioq"
}

Catalog Discovery

Use DKS to discover the catalog and then load complete metadata for AUTO-AUCT-006.

GET https://dks.vinquery.com/decisioncatalog
Authorization: Bearer {jwtToken}

GET https://dks.vinquery.com/decisioncatalog/decisions/AUTO-AUCT-006
Authorization: Bearer {jwtToken}

Criteria

Criterion IDs are intentionally stable machine identifiers. Display labels are for users; request values should be keyed by canonical criterionId.

Constraint Processing

This decision includes a catalog-defined hard constraint. DKS returns the constraint with the decision detail, and DDE evaluates it before criteria-based ranking.

{
  "constraintSummary": {
    "definedConstraintCount": 1,
    "activeConstraintCount": 1,
    "eligibleOptionCount": 2,
    "excludedOptionCount": 1,
    "eligibleOptions": [
      "option-1",
      "option-2"
    ],
    "excludedOptions": [
      {
        "optionId": "option-3",
        "reasons": [
          "Auction reputation score is below the minimum threshold of 80."
        ]
      }
    ]
  }
}

const constraints = decisionDetail.constraints || [];
if (constraints.length > 0) {
  // DDE applies catalog constraints before ranking.
  // Excluded candidates appear in response.constraintSummary.excludedOptions.
}

const summary = response.constraintSummary;
const excluded = response.decisionResult?.excludedOptions || [];

Excluded candidates do not participate in ranking because hard constraints are evaluated before scoring. For AUTO-AUCT-006, option-3 is excluded from the showcase sample because its auction reputation score is below the catalog threshold.

Industry Profile

The active industry profile uses $.auctionLots as the options path, $.optionId as the option ID, and $.name as the option display name.

{
  "auctionLots": [
    {
      "optionId": "MANHEIM-TORONTO",
      "name": "Manheim Toronto",
      "assessment": {
        "vehicleAvailabilityScore": 88,
        "averageVehicleQuality": 88,
        "auctionReputationScore": 88
      },
      "financial": {
        "buyerFeeLevel": 1250
      },
      "vehicle": {
        "acquisition": {
          "distanceToFacility": 54,
          "titleProcessingSpeed": 88
        }
      }
    }
  ]
}

{
  "optionId": "MANHEIM-TORONTO",
  "name": "Manheim Toronto",
  "values": {
    "vehicle_availability_score": 88,
    "buyer_fee_level": 1250,
    "average_vehicle_quality": 88,
    "distance_to_facility": 54,
    "auction_reputation_score": 88,
    "title_processing_speed": 88
  }
}

Profiles and Scenarios

A profile changes criterion weights. A scenario describes the operating context for the execution. The active DKS catalog exposes these choices with the decision detail response.

Prepared Input

For Prepared Decision Input, send decisionId, selected profile/scenario IDs, and option values keyed by canonical criterion ID.

{
  "decisionId": "AUTO-AUCT-006",
  "profileId": "balanced",
  "scenarioId": "standard",
  "algorithm": "TOPSIS",
  "weightStrategy": "Manual",
  "runSensitivity": false,
  "requestContext": {
    "correlationId": "auto-auct-006-demo-001"
  },
  "options": [
    {
      "optionId": "MANHEIM-TORONTO",
      "name": "Manheim Toronto",
      "values": {
        "vehicle_availability_score": 88,
        "buyer_fee_level": 1250,
        "average_vehicle_quality": 88,
        "distance_to_facility": 54,
        "auction_reputation_score": 88,
        "title_processing_speed": 88
      }
    },
    {
      "optionId": "ADESA-TORONTO",
      "name": "ADESA Toronto",
      "values": {
        "vehicle_availability_score": 81,
        "buyer_fee_level": 1750,
        "average_vehicle_quality": 81,
        "distance_to_facility": 71,
        "auction_reputation_score": 81,
        "title_processing_speed": 81
      }
    }
  ]
}

Execute

curl -X POST "https://dde.vinquery.com/api/v1/decide" \
  -H "Authorization: Bearer ${DECISIOQ_TOKEN}" \
  -H "Content-Type: application/json" \
  -H "X-Correlation-Id: auto-auct-006-demo-001" \
  --data @auto-auct-006-execute.json

The Decision Engine validates the request, retrieves authoritative criteria, constraints, profiles, scenarios, and validation metadata from DKS, applies hard constraints, ranks eligible options, and returns the decision result plus execution metadata.

Interpret the Result

The successful response is centered on decisionResult. The winner and ranking are deterministic outputs; optional explanation fields add context but do not change the ranking.

{
  "service": "decisioq",
  "version": "7.6.3",
  "requestId": "0HNE...",
  "operation": "Decide",
  "success": true,
  "decisionType": "AUTO-AUCT-006",
  "decisionVersion": "13.9.3",
  "timestampUtc": "2026-07-18T00:00:00Z",
  "decisionResult": {
    "winner": "MANHEIM-TORONTO",
    "confidence": 72.4,
    "ranking": [
      {
        "optionId": "MANHEIM-TORONTO",
        "score": 0.8421,
        "breakdown": {
          "vehicle_availability_score": 0.25
        },
        "normalizationBreakdown": {}
      }
    ],
    "excludedOptions": [
      {
        "optionId": "IMPACT-AUTO-AUCTIONS",
        "reasons": [
          "Auction reputation score is below the minimum threshold of 80."
        ]
      }
    ],
    "explanation": {
      "summary": "Manheim Toronto ranked highest based on the submitted criteria.",
      "strengths": [],
      "weaknesses": [],
      "exclusions": [
        "IMPACT-AUTO-AUCTIONS: Auction reputation score is below the minimum threshold of 80."
      ]
    }
  },
  "constraintSummary": {
    "definedConstraintCount": 1,
    "activeConstraintCount": 1,
    "eligibleOptionCount": 2,
    "excludedOptionCount": 1,
    "eligibleOptions": [
    "MANHEIM-TORONTO",
    "ADESA-TORONTO"
    ],
    "excludedOptions": [
      {
        "optionId": "IMPACT-AUTO-AUCTIONS",
        "reasons": [
          "Auction reputation score is below the minimum threshold of 80."
        ]
      }
    ]
  },
  "warnings": [],
  "requestContext": {
    "correlationId": "auto-auct-006-demo-001"
  }
}

Sensitivity Analysis

Set runSensitivity to true on catalog execution when the client wants recommendation-stability information in the same response. The sensitivity engine perturbs criterion weights by controlled factors and reports whether the winner remains stable.

{
  "sensitivityResult": {
    "stableWinner": true,
    "winner": "MANHEIM-TORONTO",
    "mostSensitiveCriterion": "buyer_fee_level",
    "confidence": 95,
    "criterionImpacts": {
      "buyer_fee_level": 0.1842
    },
    "winnerChangeCounts": {
      "buyer_fee_level": 0
    }
  }
}

Use sensitivity output to decide whether a recommendation is robust enough for automation or should be reviewed by a person.

Explanation of Decision Result

Every successful response can include deterministic explanation text under decisionResult.explanation and explanation. If the optional AI layer is enabled, the response may also include an ai block with structured business-language explanation fields.

{
  "ai": {
    "enabled": true,
    "generated": true,
    "schemaValidated": true,
    "explanation": {
      "summary": "The selected auction house provided the strongest overall fit.",
      "whyRecommended": "It combined strong availability and quality with acceptable cost.",
      "keyDrivers": [],
      "tradeoffs": [],
      "competitors": [],
      "sensitivitySummary": "Sensitivity analysis was not included in this response.",
      "scenarioSummary": "The standard scenario was selected.",
      "risks": [],
      "nextSteps": [],
      "assumptions": []
    }
  }
}

Tracing and Logs

Use request identifiers to connect client, catalog, and execution activity during support or integration testing.

X-Request-Id

Optional client-supplied request ID. If omitted, the server generates one.

X-Correlation-Id

Optional client correlation value propagated from DDE to DKS.

requestContext.correlationId

Optional payload value echoed in the response and used for tracing.

executionTrace

Advanced execution metadata including selected profile, selected scenario, effective weights, and applied constraints.

Code Examples

These examples demonstrate the current DecisioQ flow:

1. Request jwtToken from https://identity.vinquery.com/connect/token.
2. Load AUTO-AUCT-006 metadata from https://dks.vinquery.com/decisioncatalog/decisions/AUTO-AUCT-006.
3. Execute a Prepared Decision Input at https://dde.vinquery.com/api/v1/decide.

Set these environment variables before running any companion example:

DECISIOQ_CLIENT_ID
DECISIOQ_CLIENT_SECRET

Optional:
DECISIOQ_IDENTITY_URL=https://identity.vinquery.com/connect/token
DECISIOQ_DKS_URL=https://dks.vinquery.com
DECISIOQ_DDE_URL=https://dde.vinquery.com

Download the source files directly:

Troubleshooting

Production Checklist