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AspenTech Asset Performance Management is a suite of applications including Mtell, Fidelis, and ProMV providing predictive and prescriptive maintenance for asset-intensive industries.
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Makini is a unified API platform for industrial systems integration. We provide connectivity to over 2,000 ERP, CMMS, and WMS systems through a single, standardized API. Instead of building separate integrations for each system, you connect once to Makini and gain access to all supported platforms. This approach transforms integration projects that typically cost tens of thousands of dollars and take months into a manageable operational expense with deployment times of 1-2 weeks.
Makini sends webhooks for several event types: sync completion (successful or failed), connection authentication required (when credentials need renewal), connection status changes (online/offline), and system errors requiring attention. Each webhook payload includes the event type, timestamp, connection ID, and event-specific details like error messages or affected entities. You can configure which events trigger webhooks on a per-connection basis. For workflow-based integrations using Makini Flows, you can also set up custom webhooks triggered by specific conditions in your business logic, providing granular control over real-time notifications.
Testing should cover authentication, data retrieval, data writing, error handling, and workflow logic. Start by connecting a test system through Makini's authentication flow. Use sandbox or non-production instances of your target systems when available. Test API calls for each entity type you'll use (purchase orders, work orders, etc.) to verify data mapping and field coverage. Test error scenarios by providing invalid inputs or attempting operations without proper permissions. For workflow-based integrations, test each workflow step independently before testing end-to-end. Verify webhook delivery and signature verification. Test with realistic data volumes to identify performance issues. Include tests for connection failure scenarios and verify your error handling and retry logic work correctly.
Makini uses cursor-based pagination for retrieving large datasets. API responses include a next_cursor field when additional results are available. To retrieve the next page, include the cursor value in your next request: GET /api/v1/purchase-orders?cursor=CURSOR_VALUE. Cursor-based pagination is more reliable than offset-based pagination because it handles data changes between requests—if records are added or deleted while you're paginating, you won't miss records or see duplicates. Page size is configurable up to a maximum limit (typically 100-500 records per page depending on entity type). For optimal performance, use the largest page size your application can handle efficiently. The API response also includes total count when available from the source system.
