




Shipedge is a cloud-based order-management and warehouse-fulfilment platform built for ecommerce and 3PLs, combining inventory control, shipping automation and multi-channel integration.
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Makini maintains a comprehensive data model built from analyzing thousands of industrial systems. When data flows through Makini, we automatically transform it from the source system's format into our standardized structure. For example, purchase orders from SAP, NetSuite, and Dynamics all return with consistent field names, data types, and structures. This normalization happens in real-time as data passes through the API. You also have access to raw data if needed for specific use cases. The unified model covers common entities like purchase orders, work orders, inventory items, vendors, and assets, with extensive field coverage across systems.
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 provides several performance monitoring capabilities. API responses include timing information showing request processing time. The dashboard includes performance metrics showing average response times, throughput, and error rates over time. You can set up alerts for performance degradation or error rate increases. Each request generates a unique request ID that enables detailed performance analysis. For workflow-based integrations, execution logs show per-step timing, helping identify bottlenecks. We recommend implementing client-side monitoring to track end-to-end latency including network time. Monitor trends over time rather than individual requests—occasional slow requests are normal, but sustained increases may indicate issues requiring investigation.
Makini maintains API stability and provides advance notice of breaking changes. The current API version is v1. When we introduce breaking changes, we release a new API version while maintaining the previous version for a transition period (typically 12 months minimum). Non-breaking changes (like adding new fields or endpoints) are introduced into the current version without requiring updates. We announce upcoming version changes through multiple channels: email notifications, dashboard announcements, and release notes. API responses include version information in headers. We recommend specifying the API version explicitly in your requests to ensure consistent behavior. During version transitions, we provide migration guides and support for updating integrations.
