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AUTO-AUCT-044 End-to-End Example

Approve High-Risk Purchase using Identity, the secured Decision Catalog, Decision Catalog token propagation, catalog constraints, and Prepared Criteria Mode execution.

Automotive Auto Auctions Compliance & Risk TOPSIS
IdentityIssue bearer token
CatalogDiscover decision metadata
ProfileSelect weights and scenario
ExecuteApply constraints and rank
ExplainReview result context

Overview

AUTO-AUCT-044, Approve High-Risk Purchase, determines whether a high-risk auction vehicle purchase should be approved, escalated, deferred, or rejected.

Decision ID
AUTO-AUCT-044
Decision Name
Approve High-Risk Purchase
Decision Preparation Model
auto-auctions-approve-high-risk-purchase-auto-auct-044 version 1.0.0
Default Profile
balanced
Default Scenario
standard
Catalog
DKR-AUTO-RUNTIME-001, version 13.9.3
The deterministic Decision Service ranks eligible purchase cases. The explanation object is explanatory only and must not choose, rerank, or override the deterministic result.

Understanding This Decision

Approve High-Risk Purchase helps Compliance & Risk teams compare candidate purchase cases when profit opportunity must be weighed against risk exposure, title confidence, repair uncertainty, and management priority.

Business question

Which purchase case should be recommended for approve high-risk purchase in the selected Auto Auctions context?

Expected outcome

A recommended purchase case or ranked set of purchase cases with the criteria and constraints that most influenced the result.

Typical users

Operations managers, compliance teams, auction buyers, risk reviewers, and integration teams building controlled purchase workflows.

Decision boundary

Use this decision to compare submitted purchase cases. It supports business judgment and does not replace required legal, title, compliance, or management approvals.

Criteria

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

Criterion IDNameDirectionWeightValidation
expected_profit_marginExpected Profit Marginmaximize25non_negative_currency
risk_exposureRisk Exposureminimize30score_0_to_100
title_confidenceTitle Confidencemaximize15score_0_to_100
repair_uncertaintyRepair Uncertaintyminimize15score_0_to_100
management_priorityManagement Prioritymaximize15score_0_to_100

Data Preparation Guide

Loading criterion-specific integration guidance...

Constraint Processing

This decision includes one catalog-defined hard constraint. Decision Catalog returns the constraint with the decision detail, and Decision Service evaluates it before criteria-based ranking.

Verified Catalog ConstraintStatusEffect
AUTO-AUCT-044-TITLE-CONFIDENCE-MIN-80Hard, enabled, mandatoryRequires title_confidence >= 80 before a candidate can participate in ranking.
Eligible and Excluded Candidates
{
  "constraintSummary": {
    "definedConstraintCount": 1,
    "activeConstraintCount": 1,
    "eligibleOptionCount": 2,
    "excludedOptionCount": 1,
    "eligibleOptions": [
      "option-1",
      "option-2"
    ],
    "excludedOptions": [
      {
        "optionId": "option-3",
        "reasons": [
          "Title confidence is below the minimum threshold of 80."
        ]
      }
    ]
  }
}
Constraint Handling Pattern
const constraints = decisionDetail.constraints || [];
if (constraints.length > 0) {
  // Decision Service 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-044, option-3 is excluded from the showcase sample because its title confidence is below the catalog threshold.

Decision Preparation Model

The published Decision Preparation Model validates and transforms illustrative Business Data into the canonical criteria required by AUTO-AUCT-044 before Decision Service applies ranking.

Profile ID
auto-auctions-approve-high-risk-purchase-auto-auct-044
Version
1.0.0
Options Path
$.auctionLots
Option ID Path
$.optionId
Display Name Path
$.name
Client Business Data PathTransformationCanonical Criterion IDUnitValidation
option.financial.expectedProfitMargindirect valueexpected_profit_marginCurrencyInput Contract
option.risk.riskExposuredirect valuerisk_exposureScoreInput Contract
option.compliance.and.risk.titleConfidencedirect valuetitle_confidenceScoreInput Contract
option.compliance.and.risk.repairUncertaintydirect valuerepair_uncertaintyScoreInput Contract
option.compliance.and.risk.managementPrioritydirect valuemanagement_priorityScoreInput Contract
Illustrative Business Data
{
  "requestContext": {
    "sourceSystem": "automotive-profile-factory",
    "correlationId": "factory-auto-auct-044"
  },
  "auctionLots": [
    {
      "optionId": "LOT-044-01",
      "name": "Approve High-Risk Purchase Option 1",
      "financial": {
        "expectedProfitMargin": 55000.0
      },
      "risk": {
        "riskExposure": 35.0
      },
      "compliance": {
        "and": {
          "risk": {
            "titleConfidence": 88.0,
            "repairUncertainty": 83.0,
            "managementPriority": 29.0
          }
        }
      }
    },
    {
      "optionId": "LOT-044-02",
      "name": "Approve High-Risk Purchase Option 2",
      "financial": {
        "expectedProfitMargin": 90000.0
      },
      "risk": {
        "riskExposure": 42.0
      },
      "compliance": {
        "and": {
          "risk": {
            "titleConfidence": 92.0,
            "repairUncertainty": 45.0,
            "managementPriority": 50.0
          }
        }
      }
    }
  ]
}
Prepared Option Values
{
  "optionId": "LOT-044-01",
  "name": "Approve High-Risk Purchase Option 1",
  "values": {
    "expected_profit_margin": 55000.0,
    "risk_exposure": 35.0,
    "title_confidence": 88.0,
    "repair_uncertainty": 83.0,
    "management_priority": 29.0
  }
}
This example illustrates the published request structure. Adapt it to your organization and validate it against the Input Contract.

Profiles and Scenarios

This example selects a Profile for evaluation emphasis and a Scenario for operating context. Discover both from the selected Decision Catalog definition.

Profile IDNamePurpose
balancedBalancedPreserves the default Decision Catalog criterion weights.
cost_focusedCost FocusedPlaces stronger emphasis on minimized cost, fee, and expense criteria.
quality_focusedQuality FocusedPlaces stronger emphasis on maximized quality, confidence, value, and performance criteria.
risk_averseRisk AversePlaces stronger emphasis on risk reduction, compliance confidence, and operational reliability.
Scenario IDNameUse When
standardStandard Operating ScenarioRoutine auction decision execution.
limited_budgetLimited BudgetPurchasing or operating capital is constrained, requiring greater cost discipline.
high_demandHigh DemandMarket demand is strong and timely inventory acquisition is more valuable.
risk_controlRisk ControlOperational or compliance risk is elevated and conservative decisioning is preferred.

Prepared Input

For Prepared Criteria Mode, send decisionId, selected profile/scenario IDs, and option values keyed by canonical criterion ID. This request intentionally does not duplicate values under display labels.

Prepared Criteria Mode JSON
{
  "decisionId": "AUTO-AUCT-044",
  "profileId": "balanced",
  "scenarioId": "standard",
  "algorithm": "TOPSIS",
  "weightStrategy": "Expert",
  "runSensitivity": false,
  "requestContext": {
    "correlationId": "auto-auct-044-demo-001"
  },
  "options": [
    {
      "optionId": "option-1",
      "name": "Controlled High-Margin Purchase",
      "values": {
        "expected_profit_margin": 5200,
        "risk_exposure": 12,
        "title_confidence": 90,
        "repair_uncertainty": 12,
        "management_priority": 90
      }
    },
    {
      "optionId": "option-2",
      "name": "Borderline Risk Purchase",
      "values": {
        "expected_profit_margin": 4700,
        "risk_exposure": 22,
        "title_confidence": 82,
        "repair_uncertainty": 22,
        "management_priority": 82
      }
    },
    {
      "optionId": "option-3",
      "name": "Defer Pending Documentation",
      "values": {
        "expected_profit_margin": 4300,
        "risk_exposure": 34,
        "title_confidence": 74,
        "repair_uncertainty": 34,
        "management_priority": 74
      }
    }
  ]
}

Execute

cURL
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-044-demo-001" \
  --data @auto-auct-044-execute.json

Decision Service retrieves authoritative criteria, validation metadata, and constraints from Decision Catalog, excludes candidates that fail mandatory hard constraints, executes the deterministic ranking, and returns the decision result plus execution metadata.

Interpret the Result

The successful response includes both the deterministic decisionResult and a top-level explanation object. The explanation adds context but does not select, rerank, recalculate, or override the ranking.

Successful Response Shape
{
  "service": "decisioq",
  "version": "7.6.3",
  "requestId": "0HNE...",
  "operation": "Decide",
  "success": true,
  "decisionType": "AUTO-AUCT-044",
  "decisionVersion": "13.9.3",
  "timestampUtc": "2026-07-19T00:00:00Z",
  "decisionResult": {
    "winner": "option-1",
    "confidence": 91,
    "ranking": [
      {
        "optionId": "option-1",
        "score": 0.91,
        "breakdown": {
          "expected_profit_margin": 0.25,
          "risk_exposure": 0.30,
          "title_confidence": 0.15,
          "repair_uncertainty": 0.15,
          "management_priority": 0.15
        },
        "normalizationBreakdown": {}
      },
      {
        "optionId": "option-2",
        "score": 0.84,
        "breakdown": {},
        "normalizationBreakdown": {}
      }
    ],
    "excludedOptions": [
      {
        "optionId": "option-3",
        "reasons": [
          "Title confidence is below the minimum threshold of 80."
        ]
      }
    ]
  },
  "explanation": {
      "summary": "The selected purchase case provided the strongest overall balance of margin, risk, confidence, and management priority.",
      "whyRecommended": "It met the hard title-confidence threshold and ranked highest among eligible cases.",
      "keyDrivers": [],
      "tradeoffs": [],
      "competitors": [],
      "sensitivitySummary": "Sensitivity analysis was not included in this response.",
      "scenarioSummary": "The standard scenario was selected.",
      "risks": [],
      "nextSteps": [],
      "assumptions": []
  },
  "constraintSummary": {
    "definedConstraintCount": 1,
    "activeConstraintCount": 1,
    "eligibleOptionCount": 2,
    "excludedOptionCount": 1,
    "eligibleOptions": [
      "option-1",
      "option-2"
    ],
    "excludedOptions": [
      {
        "optionId": "option-3",
        "reasons": [
          "Title confidence is below the minimum threshold of 80."
        ]
      }
    ]
  },
  "warnings": [],
  "requestContext": {
    "correlationId": "auto-auct-044-demo-001"
  }
}
WinnerThe selected option ID in decisionResult.winner.
RankingOnly eligible options ordered by deterministic score.
ExclusionsHard-constraint failures listed under constraintSummary and decisionResult.excludedOptions.

Sensitivity Analysis

runSensitivity is an optional execution flag supported by both Business Data Mode and Prepared Criteria Mode. Set it to true when the client wants recommendation-stability information in the same response. The sensitivity engine analyzes the prepared criteria produced by either input path, perturbs criterion weights by controlled factors, and reports whether the winner remains stable.

Sensitivity Result Shape
{
  "sensitivityResult": {
    "stableWinner": true,
    "winner": "option-1",
    "mostSensitiveCriterion": "risk_exposure",
    "confidence": 95,
    "criterionImpacts": {
      "risk_exposure": 0.1842
    },
    "winnerChangeCounts": {
      "risk_exposure": 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 includes a provider-neutral top-level explanation object. It explains the already-finalized deterministic result and does not change the ranking.

Explanation Shape
{
  "explanation": {
      "summary": "The selected purchase case provided the strongest overall balance of margin, risk, confidence, and management priority.",
      "whyRecommended": "It met the hard title-confidence threshold and ranked highest among eligible cases.",
      "keyDrivers": [],
      "tradeoffs": [],
      "competitors": [],
      "sensitivitySummary": "Sensitivity analysis was not included in this response.",
      "scenarioSummary": "The standard scenario was selected.",
      "risks": [],
      "nextSteps": [],
      "assumptions": []
  }
}
Explanation output is supporting context. Business-facing pages should render the explanation, warnings, assumptions, and limitations without provider branding.

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 Decision Service to Decision Catalog.
requestContext.correlationId
Optional payload value echoed in the response and used for tracing.
configurationUsed
The authoritative effective weight strategy, ranking algorithm, profile, scenario, sensitivity setting, and source for each value.
diagnostics
Safe execution counts returned only when responseOptions.includeDiagnostics is true.

Code Examples

These examples demonstrate the current DecisioQ flow:

  1. Request jwtToken from https://identity.vinquery.com/connect/token.
  2. Load AUTO-AUCT-044 metadata from https://dks.vinquery.com/decisioncatalog/decisions/AUTO-AUCT-044.
  3. Execute a Prepared Criteria Mode at https://dde.vinquery.com/api/v1/decide.

Set these environment variables before running any companion example:

Environment variables
DECISIOQ_CLIENT_ID
DECISIOQ_CLIENT_SECRET

Optional:
DECISIOQ_AUDIENCE=vinquery:api:decisioq
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

SymptomLikely CauseWhat to Check
401 UnauthorizedMissing, expired, or invalid bearer token.Request a fresh jwtToken from Identity and send it as Authorization: Bearer ....
Decision not foundThe decision ID is not in the active catalog.Load /decisioncatalog/decisions/AUTO-AUCT-044 and confirm the ID is published.
Validation failedA required criterion value is missing or outside its rule.Use canonical criterion IDs and keep score values in the expected range.
Candidate excludedA mandatory hard constraint failed.Review constraintSummary.excludedOptions for the exact candidate and reason.
HTML error responseAn upstream hosted service failed before returning JSON.Check service health and server logs for Identity, Decision Catalog, or Decision Service.

Production Checklist

Security

Keep API Consumer credentials and jwtTokens on the server side. Use HTTPS, short-lived bearer tokens, and an API Consumer linked to a DecisioQ account for usage accounting.

Catalog

Load decision metadata from Decision Catalog and cache cautiously. Refresh when catalog versions change.

Request Quality

Use canonical criterion IDs, validate value ranges, and send at least two candidate options.

Constraints

Show excluded candidates separately. Do not include them in user-facing ranking tables.

Explanation

Display explanation text as supporting context only. Never let generated explanation text override deterministic results.

User Experience

Show business labels to users and keep raw execution trace collapsed for advanced diagnostics.