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Connecting to any system through Makini is straightforward. You select the product you want to connect to, log in with your credentials, and receive an API token. The process takes just a few clicks and works consistently across all 2,000+ supported systems. For most systems (85-90% of cases), you only need the instance URL, username, and password. Some systems may require additional steps like API token generation, and we provide detailed authentication guides for these cases. The connection experience is designed to be simple enough that non-technical users can complete it without IT support.
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.
Yes, Makini provides extensive customization options for field mappings. Through the connection settings interface, you can view how each system's fields map to Makini's unified model. You can remap fields, add custom field mappings, or create entirely new custom fields that will appear in API responses. These customizations are connection-specific, allowing different mapping configurations for different customers. Mapping changes take effect immediately without requiring code changes. For standardized workflows, default mappings typically provide sufficient coverage. Custom mappings are most useful when integrating with heavily customized systems or when you need fields beyond the standard unified model.
For bulk operations, we recommend batch processing with appropriate rate limiting and error handling. Makini Flows provides built-in batch processing capabilities with configurable batch sizes, delays between batches, and error handling. For API-based bulk operations, implement pagination when retrieving large datasets—our API returns results in pages with continuation tokens for fetching subsequent pages. When writing large volumes of data, break operations into smaller batches (typically 50-100 records per batch) with delays between batches to avoid overwhelming the target system. Implement comprehensive error logging to identify which specific records fail in a batch. For very large operations (thousands of records), consider asynchronous processing patterns where you queue operations and process them in the background.
