Pricing

$300.00 / 1,000 result returneds

Logistics Freight Intelligence

Batch supply chain risk intelligence per entity. One run returns composite score, decision profile, materiality, velocity, escalation policy, mitigation plans, governance, audit replay. 9 official data sources. Bulk-friendly. For ERP, BI, Zapier, Make, n8n. Pay per entity.

Pricing

$300.00 / 1,000 result returneds

Rating

0.0

(0)

Developer

Ryan Clinton

Actor stats

Bookmarked

Total users

Monthly active users

17 days ago

Last modified

Categories

Developer tools

You can access the Logistics Freight Intelligence programmatically from your own applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in Integrations settings in Apify Console.

Python

JavaScript

CLI

OpenAPI

HTTP

MCP

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      "inputSchema": {
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          "entity": {
            "title": "Entity (single)",
            "type": "string",
            "description": "A single supply chain entity, corridor, or trade route to assess (e.g. \"Gulf Coast\", \"US-Shanghai\", \"Taiwan Strait electronics corridor\"). Use this for one-off assessments; for bulk runs use `entities[]` instead."
          },
          "entities": {
            "title": "Entities (bulk)",
            "maxItems": 100,
            "type": "array",
            "description": "Array of entities to assess in a single run. Max 100. Each produces one dataset row. Use for daily portfolio sweeps, ERP imports, or batch scenario planning.",
            "items": {
              "type": "string"
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          "mode": {
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            "enum": [
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              "accurate",
              "audit"
            ],
            "type": "string",
            "description": "`fast` skips the slow sources (UN COMTRADE, OECD, World Bank) for cheaper runs. `accurate` (default) calls all 9. `audit` adds per-source timing and item counts to the runSummary.",
            "default": "accurate"
          },
          "workflowProfile": {
            "title": "Workflow profile",
            "enum": [
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              "dify"
            ],
            "type": "string",
            "description": "Output shape. `raw` keeps the nested envelope. `zapier` flattens to underscore keys. `make` uses dotted paths with array-as-JSON-string. `dify` projects only decision-relevant fields for if/else branching.",
            "default": "raw"
          },
          "policies": {
            "title": "Risk policies (optional)",
            "type": "array",
            "description": "Array of named risk policies to evaluate against each result. Each policy has `name`, `conditions` (maxComposite / forbidVerdict / maxRiskTier / forbidComplianceLevel / requireConfidenceLevel / maxTradePartnerHHI), and `onViolation` (BLOCK / WARN / REVIEW). The response carries `policyEvaluation[]` when supplied."
          },
          "constraints": {
            "title": "Operational constraints (optional)",
            "type": "object",
            "description": "Caller-supplied execution constraints: `alternateCapacityAvailable`, `supplierSwitchLeadTimeDays`, `customsRestriction`, `bookingFreezeActive`. The response evaluates whether the recommended action is feasible under these constraints."
          },
          "revenueContext": {
            "title": "Revenue context (optional)",
            "type": "object",
            "description": "Caller-supplied business context: `dailyRevenueUsd`, `perShipmentValueUsd`, `affectedShipmentsPerDay`, `perDayDelayCostUsd`, `activeInventoryValueUsd`, `downstreamCustomerCount`, `topCustomerDependency`. When provided, the response includes a `financialExposure` block with revenue-at-risk and delay-cost estimates. Omitted entirely if no context supplied — no fabricated numbers."
          },
          "executionContext": {
            "title": "Execution context (optional)",
            "type": "object",
            "description": "Caller-supplied operational reality: `alternateSupplierOnboardingDaysAvg`, `customsApprovalBacklogStatus`, `procurementTeamCapacity`, `executiveAvailabilityHours`, `legalReviewBacklogDays`. When provided, `executionConfidence.score` becomes a real number; otherwise it's null with band `insufficient_context`."
          },
          "budgetContext": {
            "title": "Budget context (optional)",
            "type": "object",
            "description": "Caller-supplied per-action cost estimates: `rerouteCostUsd`, `hedgeCostUsd`, `supplierSwitchCostUsd`, `complianceHoldDailyCostUsd`. When provided, `budgetImpact.estimatedMitigationCostUsd` is populated; otherwise null."
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Supply Chain Risk Intelligence for ERP and BI OpenAPI definition

OpenAPI is a standard for designing and describing RESTful APIs, allowing developers to define API structure, endpoints, and data formats in a machine-readable way. It simplifies API development, integration, and documentation.

OpenAPI is effective when used with AI agents and GPTs by standardizing how these systems interact with various APIs, for reliable integrations and efficient communication.

By defining machine-readable API specifications, OpenAPI allows AI models like GPTs to understand and use varied data sources, improving accuracy. This accelerates development, reduces errors, and provides context-aware responses, making OpenAPI a core component for AI applications.

You can download the OpenAPI definitions for Logistics Freight Intelligence from the options below:

OpenAPI.json

If you’d like to learn more about how OpenAPI powers GPTs, read our blog post.

You can also check out our other API clients:

Logistics Freight Intelligence API in Python

Logistics Freight Intelligence API in JavaScript

Logistics Freight Intelligence API through CLI

Logistics Freight Intelligence API

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