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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's unified API acts as a common denominator across all connected systems. We map each system's data structure to a standardized data model, exposing consistent endpoints regardless of the underlying platform. This means you write the same code to retrieve purchase orders from SAP, NetSuite, or Dynamics—the API calls and response formats are identical. You always get data in the same structure, making it easy to build consistent business logic. The unified approach eliminates the need to learn each system's unique API, manage multiple authentication methods, or handle varying data formats.
Makini's API supports date filtering on most endpoints using query parameters. You can filter by creation date, modification date, or entity-specific date fields like order date or delivery date. Common patterns include `modified_after=2024-01-01` to retrieve records updated since a specific date, or relative timestamps like `modified_after=2024-01-01T00:00:00Z`. For optimal performance, use incremental data retrieval patterns rather than repeatedly fetching all records. The sync status endpoint provides the last sync timestamp, which you can use as the `modified_after` value for your next query. This approach minimizes data transfer and API load while ensuring you capture all changes.
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.
