




RefMan is a ship repair management system designed to help maritime operators control the full docking and maintenance process. It supports defect recording and monitoring, specification creation, contractor and shipyard bid comparison, cost control and reporting, progress and time tracking, yard performance evaluation, and documentation of drawings and job references.
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Integration timelines vary by complexity. For standard implementations with no customizations, connections can be live within 1-2 weeks. This includes authentication setup and basic workflow configuration. For implementations requiring custom workflows or specific business logic, timelines typically range from 2-6 weeks depending on the scope. Complex enterprise deployments with multiple systems and custom requirements may take 6-10 weeks. These timelines are significantly shorter than traditional integration projects, which often take 2-24 months.
When customers change their system credentials, the existing Makini connection will lose access and workflows will begin failing with authentication errors. Makini provides webhook notifications when connections require reauthorization, allowing you to proactively notify customers. Customers can reconnect by logging into the system through Makini's authentication flow again, which issues a new API token. The reconnection process takes only a few minutes. Best practice is to implement connection health monitoring and automated alerts when connections require attention, so you can address issues before they impact operations.
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.
